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17 Data Visualization Techniques All Professionals Should Know

Data Visualizations on a Page

  • 17 Sep 2019

There’s a growing demand for business analytics and data expertise in the workforce. But you don’t need to be a professional analyst to benefit from data-related skills.

Becoming skilled at common data visualization techniques can help you reap the rewards of data-driven decision-making , including increased confidence and potential cost savings. Learning how to effectively visualize data could be the first step toward using data analytics and data science to your advantage to add value to your organization.

Several data visualization techniques can help you become more effective in your role. Here are 17 essential data visualization techniques all professionals should know, as well as tips to help you effectively present your data.

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What Is Data Visualization?

Data visualization is the process of creating graphical representations of information. This process helps the presenter communicate data in a way that’s easy for the viewer to interpret and draw conclusions.

There are many different techniques and tools you can leverage to visualize data, so you want to know which ones to use and when. Here are some of the most important data visualization techniques all professionals should know.

Data Visualization Techniques

The type of data visualization technique you leverage will vary based on the type of data you’re working with, in addition to the story you’re telling with your data .

Here are some important data visualization techniques to know:

  • Gantt Chart
  • Box and Whisker Plot
  • Waterfall Chart
  • Scatter Plot
  • Pictogram Chart
  • Highlight Table
  • Bullet Graph
  • Choropleth Map
  • Network Diagram
  • Correlation Matrices

1. Pie Chart

Pie Chart Example

Pie charts are one of the most common and basic data visualization techniques, used across a wide range of applications. Pie charts are ideal for illustrating proportions, or part-to-whole comparisons.

Because pie charts are relatively simple and easy to read, they’re best suited for audiences who might be unfamiliar with the information or are only interested in the key takeaways. For viewers who require a more thorough explanation of the data, pie charts fall short in their ability to display complex information.

2. Bar Chart

Bar Chart Example

The classic bar chart , or bar graph, is another common and easy-to-use method of data visualization. In this type of visualization, one axis of the chart shows the categories being compared, and the other, a measured value. The length of the bar indicates how each group measures according to the value.

One drawback is that labeling and clarity can become problematic when there are too many categories included. Like pie charts, they can also be too simple for more complex data sets.

3. Histogram

Histogram Example

Unlike bar charts, histograms illustrate the distribution of data over a continuous interval or defined period. These visualizations are helpful in identifying where values are concentrated, as well as where there are gaps or unusual values.

Histograms are especially useful for showing the frequency of a particular occurrence. For instance, if you’d like to show how many clicks your website received each day over the last week, you can use a histogram. From this visualization, you can quickly determine which days your website saw the greatest and fewest number of clicks.

4. Gantt Chart

Gantt Chart Example

Gantt charts are particularly common in project management, as they’re useful in illustrating a project timeline or progression of tasks. In this type of chart, tasks to be performed are listed on the vertical axis and time intervals on the horizontal axis. Horizontal bars in the body of the chart represent the duration of each activity.

Utilizing Gantt charts to display timelines can be incredibly helpful, and enable team members to keep track of every aspect of a project. Even if you’re not a project management professional, familiarizing yourself with Gantt charts can help you stay organized.

5. Heat Map

Heat Map Example

A heat map is a type of visualization used to show differences in data through variations in color. These charts use color to communicate values in a way that makes it easy for the viewer to quickly identify trends. Having a clear legend is necessary in order for a user to successfully read and interpret a heatmap.

There are many possible applications of heat maps. For example, if you want to analyze which time of day a retail store makes the most sales, you can use a heat map that shows the day of the week on the vertical axis and time of day on the horizontal axis. Then, by shading in the matrix with colors that correspond to the number of sales at each time of day, you can identify trends in the data that allow you to determine the exact times your store experiences the most sales.

6. A Box and Whisker Plot

Box and Whisker Plot Example

A box and whisker plot , or box plot, provides a visual summary of data through its quartiles. First, a box is drawn from the first quartile to the third of the data set. A line within the box represents the median. “Whiskers,” or lines, are then drawn extending from the box to the minimum (lower extreme) and maximum (upper extreme). Outliers are represented by individual points that are in-line with the whiskers.

This type of chart is helpful in quickly identifying whether or not the data is symmetrical or skewed, as well as providing a visual summary of the data set that can be easily interpreted.

7. Waterfall Chart

Waterfall Chart Example

A waterfall chart is a visual representation that illustrates how a value changes as it’s influenced by different factors, such as time. The main goal of this chart is to show the viewer how a value has grown or declined over a defined period. For example, waterfall charts are popular for showing spending or earnings over time.

8. Area Chart

Area Chart Example

An area chart , or area graph, is a variation on a basic line graph in which the area underneath the line is shaded to represent the total value of each data point. When several data series must be compared on the same graph, stacked area charts are used.

This method of data visualization is useful for showing changes in one or more quantities over time, as well as showing how each quantity combines to make up the whole. Stacked area charts are effective in showing part-to-whole comparisons.

9. Scatter Plot

Scatter Plot Example

Another technique commonly used to display data is a scatter plot . A scatter plot displays data for two variables as represented by points plotted against the horizontal and vertical axis. This type of data visualization is useful in illustrating the relationships that exist between variables and can be used to identify trends or correlations in data.

Scatter plots are most effective for fairly large data sets, since it’s often easier to identify trends when there are more data points present. Additionally, the closer the data points are grouped together, the stronger the correlation or trend tends to be.

10. Pictogram Chart

Pictogram Example

Pictogram charts , or pictograph charts, are particularly useful for presenting simple data in a more visual and engaging way. These charts use icons to visualize data, with each icon representing a different value or category. For example, data about time might be represented by icons of clocks or watches. Each icon can correspond to either a single unit or a set number of units (for example, each icon represents 100 units).

In addition to making the data more engaging, pictogram charts are helpful in situations where language or cultural differences might be a barrier to the audience’s understanding of the data.

11. Timeline

Timeline Example

Timelines are the most effective way to visualize a sequence of events in chronological order. They’re typically linear, with key events outlined along the axis. Timelines are used to communicate time-related information and display historical data.

Timelines allow you to highlight the most important events that occurred, or need to occur in the future, and make it easy for the viewer to identify any patterns appearing within the selected time period. While timelines are often relatively simple linear visualizations, they can be made more visually appealing by adding images, colors, fonts, and decorative shapes.

12. Highlight Table

Highlight Table Example

A highlight table is a more engaging alternative to traditional tables. By highlighting cells in the table with color, you can make it easier for viewers to quickly spot trends and patterns in the data. These visualizations are useful for comparing categorical data.

Depending on the data visualization tool you’re using, you may be able to add conditional formatting rules to the table that automatically color cells that meet specified conditions. For instance, when using a highlight table to visualize a company’s sales data, you may color cells red if the sales data is below the goal, or green if sales were above the goal. Unlike a heat map, the colors in a highlight table are discrete and represent a single meaning or value.

13. Bullet Graph

Bullet Graph Example

A bullet graph is a variation of a bar graph that can act as an alternative to dashboard gauges to represent performance data. The main use for a bullet graph is to inform the viewer of how a business is performing in comparison to benchmarks that are in place for key business metrics.

In a bullet graph, the darker horizontal bar in the middle of the chart represents the actual value, while the vertical line represents a comparative value, or target. If the horizontal bar passes the vertical line, the target for that metric has been surpassed. Additionally, the segmented colored sections behind the horizontal bar represent range scores, such as “poor,” “fair,” or “good.”

14. Choropleth Maps

Choropleth Map Example

A choropleth map uses color, shading, and other patterns to visualize numerical values across geographic regions. These visualizations use a progression of color (or shading) on a spectrum to distinguish high values from low.

Choropleth maps allow viewers to see how a variable changes from one region to the next. A potential downside to this type of visualization is that the exact numerical values aren’t easily accessible because the colors represent a range of values. Some data visualization tools, however, allow you to add interactivity to your map so the exact values are accessible.

15. Word Cloud

Word Cloud Example

A word cloud , or tag cloud, is a visual representation of text data in which the size of the word is proportional to its frequency. The more often a specific word appears in a dataset, the larger it appears in the visualization. In addition to size, words often appear bolder or follow a specific color scheme depending on their frequency.

Word clouds are often used on websites and blogs to identify significant keywords and compare differences in textual data between two sources. They are also useful when analyzing qualitative datasets, such as the specific words consumers used to describe a product.

16. Network Diagram

Network Diagram Example

Network diagrams are a type of data visualization that represent relationships between qualitative data points. These visualizations are composed of nodes and links, also called edges. Nodes are singular data points that are connected to other nodes through edges, which show the relationship between multiple nodes.

There are many use cases for network diagrams, including depicting social networks, highlighting the relationships between employees at an organization, or visualizing product sales across geographic regions.

17. Correlation Matrix

Correlation Matrix Example

A correlation matrix is a table that shows correlation coefficients between variables. Each cell represents the relationship between two variables, and a color scale is used to communicate whether the variables are correlated and to what extent.

Correlation matrices are useful to summarize and find patterns in large data sets. In business, a correlation matrix might be used to analyze how different data points about a specific product might be related, such as price, advertising spend, launch date, etc.

Other Data Visualization Options

While the examples listed above are some of the most commonly used techniques, there are many other ways you can visualize data to become a more effective communicator. Some other data visualization options include:

  • Bubble clouds
  • Circle views
  • Dendrograms
  • Dot distribution maps
  • Open-high-low-close charts
  • Polar areas
  • Radial trees
  • Ring Charts
  • Sankey diagram
  • Span charts
  • Streamgraphs
  • Wedge stack graphs
  • Violin plots

Business Analytics | Become a data-driven leader | Learn More

Tips For Creating Effective Visualizations

Creating effective data visualizations requires more than just knowing how to choose the best technique for your needs. There are several considerations you should take into account to maximize your effectiveness when it comes to presenting data.

Related : What to Keep in Mind When Creating Data Visualizations in Excel

One of the most important steps is to evaluate your audience. For example, if you’re presenting financial data to a team that works in an unrelated department, you’ll want to choose a fairly simple illustration. On the other hand, if you’re presenting financial data to a team of finance experts, it’s likely you can safely include more complex information.

Another helpful tip is to avoid unnecessary distractions. Although visual elements like animation can be a great way to add interest, they can also distract from the key points the illustration is trying to convey and hinder the viewer’s ability to quickly understand the information.

Finally, be mindful of the colors you utilize, as well as your overall design. While it’s important that your graphs or charts are visually appealing, there are more practical reasons you might choose one color palette over another. For instance, using low contrast colors can make it difficult for your audience to discern differences between data points. Using colors that are too bold, however, can make the illustration overwhelming or distracting for the viewer.

Related : Bad Data Visualization: 5 Examples of Misleading Data

Visuals to Interpret and Share Information

No matter your role or title within an organization, data visualization is a skill that’s important for all professionals. Being able to effectively present complex data through easy-to-understand visual representations is invaluable when it comes to communicating information with members both inside and outside your business.

There’s no shortage in how data visualization can be applied in the real world. Data is playing an increasingly important role in the marketplace today, and data literacy is the first step in understanding how analytics can be used in business.

Are you interested in improving your analytical skills? Learn more about Business Analytics , our eight-week online course that can help you use data to generate insights and tackle business decisions.

This post was updated on January 20, 2022. It was originally published on September 17, 2019.

different data representation techniques

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21 Best Data Visualization Types: Examples of Graphs and Charts Uses

Those who master different data visualization types and techniques (such as graphs, charts, diagrams, and maps) are gaining the most value from data.

Why? Because they can analyze data and make the best-informed decisions.

Whether you work in business, marketing, sales, statistics, or anything else, you need data visualization techniques and skills.

Graphs and charts make data much more understandable for the human brain.

On this page:

  • What are data visualization techniques? Definition, benefits, and importance.
  • 21 top data visualization types. Examples of graphs and charts with an explanation.
  • When to use different data visualization graphs, charts, diagrams, and maps?
  • How to create effective data visualization?
  • 10 best data visualization tools for creating compelling graphs and charts.

What Are Data V isualization T echniques? Definition And Benefits.

Data visualization techniques are visual elements (like a line graph, bar chart, pie chart, etc.) that are used to represent information and data.

Big data hides a story (like a trend and pattern).

By using different types of graphs and charts, you can easily see and understand trends, outliers, and patterns in data.

They allow you to get the meaning behind figures and numbers and make important decisions or conclusions.

Data visualization techniques can benefit you in several ways to improve decision making.

Key benefits:

  • Data is processed faster Visualized data is processed faster than text and table reports. Our brains can easily recognize images and make sense of them.
  • Better analysis Help you analyze better reports in sales, marketing, product management, etc. Thus, you can focus on the areas that require attention such as areas for improvement, errors or high-performing spots.
  • Faster decision making Businesses who can understand and quickly act on their data will gain more competitive advantages because they can make informed decisions sooner than the competitors.
  • You can easily identify relationships, trends, patterns Visuals are especially helpful when you’re trying to find trends, patterns or relationships among hundreds or thousands of variables. Data is presented in ways that are easy to consume while allowing exploration. Therefore, people across all levels in your company can dive deeper into data and use the insights for faster and smarter decisions.
  • No need for coding or data science skills There are many advanced tools that allow you to create beautiful charts and graphs without the need for data scientist skills . Thereby, a broad range of business users can create, visually explore, and discover important insights into data.

How Do Data Visualization Techniques work?

Data visualization techniques convert tons of data into meaningful visuals using software tools.

The tools can operate various types of data and present them in visual elements like charts, diagrams, and maps.

They allow you to easily analyze massive amounts of information, discover trends and patterns in data and then make data-driven decisions .

Why data visualization is very important for any job?

Each professional industry benefits from making data easier to understand. Government, marketing, finance, sales, science, consumer goods, education, sports, and so on.

As all types of organizations become more and more data-driven, the ability to work with data isn’t a good plus, it’s essential.

Whether you’re in sales and need to present your products to prospects or a manager trying to optimize employee performance – everything is measurable and needs to be scored against different KPI s.

We need to constantly analyze and share data with our team or customers.

Having data visualization skills will allow you to understand what is happening in your company and to make the right decisions for the good of the organization.

Before start using visuals, you must know…

Data visualization is one of the most important skills for the modern-day worker.

However, it’s not enough to see your data in easily digestible visuals to get real insights and make the right decisions.

  • First : to define the information you need to present
  • Second: to find the best possible visual to show that information

Don’t start with “I need a bar chart/pie chart/map here. Let’s make one that looks cool” . This is how you can end up with misleading visualizations that, while beautiful, don’t help for smart decision making.

Regardless of the type of data visualization, its purpose is to help you see a pattern or trend in the data being analyzed.

The goal is not to come up with complex descriptions such as: “ A’s sales were more than B by 5.8% in 2018, and despite a sales growth of 30% in 2019, A’s sales became less than B by 6.2% in 2019. ”

A good data visualization summarizes and presents information in a way that enables you to focus on the most important points.

Let’s go through 21 data visualization types with examples, outline their features, and explain how and when to use them for the best results.

21 Best Types Of Data Visualization With Examples And Uses

1. Line Graph

The line graph is the most popular type of graph with many business applications because they show an overall trend clearly and concisely.

What is a line graph?

A line graph (also known as a line chart) is a graph used to visualize the values of something over a specified period of time.

For example, your sales department may plot the change in the number of sales your company has on hand over time.

Data points that display the values are connected by straight lines.

When to use line graphs?

  • When you want to display trends.
  • When you want to represent trends for different categories over the same period of time and thus to show comparison.

For example, the above line graph shows the total units of a company sales of Product A, Product B, and Product C from 2012 to 2019.

Here, you can see at a glance that the top-performing product over the years is product C, followed by Product B.

2. Bar Chart

At some point or another, you’ve interacted with a bar chart before. Bar charts are very popular data visualization types as they allow you to easily scan them for valuable insights.

And they are great for comparing several different categories of data.

What is a bar chart?

A bar chart (also called bar graph) is a chart that represents data using bars of different heights.

The bars can be two types – vertical or horizontal. It doesn’t matter which type you use.

The bar chart can easily compare the data for each variable at each moment in time.

For example, a bar chart could compare your company’s sales from this year to last year.

When to use a bar chart?

  • When you need to compare several different categories.
  • When you need to show how large data changes over time.

The above bar graph visualizes revenue by age group for three different product lines – A, B, and C.

You can see more granular differences between revenue for each product within each age group.

As different product lines are groups by age group, you can easily see that the group of 34-45-year-old buyers are the most valuable to your business as they are your biggest customers.

3. Column Chart

If you want to make side-by-side comparisons of different values, the column chart is your answer.

What is a column chart?

A column chart is a type of bar chart that uses vertical bars to show a comparison between categories.

If something can be counted, it can be displayed in a column chart.

Column charts work best for showing the situation at a point in time (for example, the number of products sold on a website).

Their main purpose is to draw attention to total numbers rather than the trend (trends are more suitable for a line chart).

When to use a column chart?

  • When you need to show a side-by-side comparison of different values.
  • When you want to emphasize the difference between values.
  • When you want to highlight the total figures rather than the trends.

For example, the column chart above shows the traffic sources of a website. It illustrates direct traffic vs search traffic vs social media traffic on a series of dates.

The numbers don’t change much from day to day, so a line graph isn’t appropriate as it wouldn’t reveal anything important in terms of trends.

The important information here is the concrete number of visitors coming from different sources to the website each day.

4. Pie Chart

Pie charts are attractive data visualization types. At a high-level, they’re easy to read and used for representing relative sizes.

What is a pie chart?

A Pie Chart is a circular graph that uses “pie slices” to display relative sizes of data.

A pie chart is a perfect choice for visualizing percentages because it shows each element as part of a whole.

The entire pie represents 100 percent of a whole. The pie slices represent portions of the whole.

When to use a pie chart?

  • When you want to represent the share each value has of the whole.
  • When you want to show how a group is broken down into smaller pieces.

The above pie chart shows which traffic sources bring in the biggest share of total visitors.

You see that Searches is the most effective source, followed by Social Media, and then Links.

At a glance, your marketing team can spot what’s working best, helping them to concentrate their efforts to maximize the number of visitors.

5. Area Chart 

If you need to present data that depicts a time-series relationship, an area chart is a great option.

What is an area chart?

An area chart is a type of chart that represents the change in one or more quantities over time. It is similar to a line graph.

In both area charts and line graphs, data points are connected by a line to show the value of a quantity at different times. They are both good for showing trends.

However, the area chart is different from the line graph, because the area between the x-axis and the line is filled in with color. Thus, area charts give a sense of the overall volume.

Area charts emphasize a trend over time. They aren’t so focused on showing exact values.

Also, area charts are perfect for indicating the change among different data groups.

When to use an area chart?

  • When you want to use multiple lines to make a comparison between groups (aka series).
  • When you want to track not only the whole value but also want to understand the breakdown of that total by groups.

In the area chart above, you can see how much revenue is overlapped by cost.

Moreover, you see at once where the pink sliver of profit is at its thinnest.

Thus, you can spot where cash flow really is tightest, rather than where in the year your company simply has the most cash.

Area charts can help you with things like resource planning, financial management, defining appropriate storage space, and more.

6. Scatter Plot

The scatter plot is also among the popular data visualization types and has other names such as a scatter diagram, scatter graph, and correlation chart.

Scatter plot helps in many areas of today’s world – business, biology, social statistics, data science and etc.

What is a Scatter plot?

Scatter plot is a graph that represents a relationship between two variables . The purpose is to show how much one variable affects another.

Usually, when there is a relationship between 2 variables, the first one is called independent. The second variable is called dependent because its values depend on the first variable.

But it is also possible to have no relationship between 2 variables at all.

When to use a Scatter plot?

  • When you need to observe and show relationships between two numeric variables.
  • When just want to visualize the correlation between 2 large datasets without regard to time.

The above scatter plot illustrates the relationship between monthly e-commerce sales and online advertising costs of a company.

At a glance, you can see that online advertising costs affect monthly e-commerce sales.

When online advertising costs increase, e-commerce sales also increase.

Scatter plots also show if there are unexpected gaps in the data or if there are any outlier points.

7. Bubble chart

If you want to display 3 related dimensions of data in one elegant visualization, a bubble chart will help you.

What is a bubble chart?

A bubble chart is like an extension of the scatter plot used to display relationships between three variables.

The variables’ values for each point are shown by horizontal position, vertical position, and dot size.

In a bubble chart, we can make three different pairwise comparisons (X vs. Y, Y vs. Z, X vs. Z).

When to use a bubble chart?

  • When you want to depict and show relationships between three variables.

The bubble chart above illustrates the relationship between 3 dimensions of data:

  • Cost (X-Axis)
  • Profit (Y-Axis)
  • Probability of Success (%) (Bubble Size).

Bubbles are proportional to the third dimension – the probability of success. The larger the bubble, the greater the probability of success.

It is obvious that Product A has the highest probability of success.

8. Pyramid Graph

Pyramid graphs are very interesting and visually appealing graphs. Moreover, they are one of the most easy-to-read data visualization types and techniques.

What is a pyramid graph?

It is a graph in the shape of a triangle or pyramid. It is best used when you want to show some kind of hierarchy. The pyramid levels display some kind of progressive order, such as:

  • More important to least important. For example, CEOs at the top and temporary employees on the bottom level.
  • Specific to least specific. For example, expert fields at the top, general fields at the bottom.
  • Older to newer.

When to use a pyramid graph?

  • When you need to illustrate some kind of hierarchy or progressive order

Image Source: Conceptdraw

The above is a 5 Level Pyramid of information system types that is based on the hierarchy in an organization.

It shows progressive order from tacit knowledge to more basic knowledge. Executive information system at the top and transaction processing system on the bottom level.

The levels are displayed in different colors. It’s very easy to read and understand.

9. Treemaps

Treemaps also show a hierarchical structure like the pyramid graph, however in a completely different way.

What is a treemap?

Treemap is a type of data visualization technique that is used to display a hierarchical structure using nested rectangles.

Data is organized as branches and sub-branches. Treemaps display quantities for each category and sub-category via a rectangle area size.

Treemaps are a compact and space-efficient option for showing hierarchies.

They are also great at comparing the proportions between categories via their area size. Thus, they provide an instant sense of which data categories are the most important overall.

When to use a treemap?

  • When you want to illustrate hierarchies and comparative value between categories and subcategories.

Image source: Power BI

For example, let’s say you work in a company that sells clothing categories: Urban, Rural, Youth, and Mix.

The above treemap depicts the sales of different clothing categories, which are then broken down by clothing manufacturers.

You see at a glance that Urban is your most successful clothing category, but that the Quibus is your most valuable clothing manufacturer, across all categories.

10. Funnel chart

Funnel charts are used to illustrate optimizations, specifically to see which stages most impact drop-off.

Illustrating the drop-offs helps to show the importance of each stage.

What is a funnel chart?

A funnel chart is a popular data visualization type that shows the flow of users through a sales or other business process.

It looks like a funnel that starts from a large head and ends in a smaller neck. The number of users at each step of the process is visualized from the funnel width as it narrows.

A funnel chart is very useful for identifying potential problem areas in the sales process.

When to use a funnel chart?

  • When you need to represent stages in a sales or other business process and show the amount of revenue for each stage.

Image Source: DevExpress

This funnel chart shows the conversion rate of a website.

The conversion rate shows what percentage of all visitors completed a specific desired action (such as subscription or purchase).

The chart starts with the people that visited the website and goes through every touchpoint until the final desired action – renewal of the subscription.

You can see easily where visitors are dropping out of the process.

11. Venn Diagram 

Venn diagrams are great data visualization types for representing relationships between items and highlighting how the items are similar and different.

What is a Venn diagram?

A Venn Diagram is an illustration that shows logical relationships between two or more data groups. Typically, the Venn diagram uses circles (both overlapping and nonoverlapping).

Venn diagrams can clearly show how given items are similar and different.

Venn diagram with 2 and 3 circles are the most common types. Diagrams with a larger number of circles (5,6,7,8,10…) become extremely complicated.

When to use a Venn diagram?

  • When you want to compare two or more options and see what they have in common.
  • When you need to show how given items are similar or different.
  • To display logical relationships from various datasets.

The above Venn chart clearly shows the core customers of a product – the people who like eating fast foods but don’t want to gain weight.

The Venn chart gives you an instant understanding of who you will need to sell.

Then, you can plan how to attract the target segment with advertising and promotions.

12. Decision Tree

As graphical representations of complex or simple problems and questions, decision trees have an important role in business, finance, marketing, and in any other areas.

What is a decision tree?

A decision tree is a diagram that shows possible solutions to a decision.

It displays different outcomes from a set of decisions. The diagram is a widely used decision-making tool for analysis and planning.

The diagram starts with a box (or root), which branches off into several solutions. That’s why it is called a decision tree.

Decision trees are helpful for a variety of reasons. Not only they are easy-to-understand diagrams that support you ‘see’ your thoughts, but also because they provide a framework for estimating all possible alternatives.

When to use a decision tree?

  • When you need help in making decisions and want to display several possible solutions.

Imagine you are an IT project manager and you need to decide whether to start a particular project or not.

You need to take into account important possible outcomes and consequences.

The decision tree, in this case, might look like the diagram above.

13. Fishbone Diagram

Fishbone diagram is a key tool for root cause analysis that has important uses in almost any business area.

It is recognized as one of the best graphical methods to understand and solve problems because it takes into consideration all the possible causes.

What is a fishbone diagram?

A fishbone diagram (also known as a cause and effect diagram, Ishikawa diagram or herringbone diagram) is a data visualization technique for categorizing the potential causes of a problem.

The main purpose is to find the root cause.

It combines brainstorming with a kind of mind mapping and makes you think about all potential causes of a given problem, rather than just the one or two.

It also helps you see the relationships between the causes in an easy to understand way.

When to use a fishbone diagram?

  • When you want to display all the possible causes of a problem in a simple, easy to read graphical way.

Let’s say you are an online marketing specialist working for a company witch experience low website traffic.

You have the task to find the main reasons. Above is a fishbone diagram example that displays the possible reasons and can help you resolve the situation.

14. Process Flow Diagram

If you need to visualize a specific process, the process flow diagram will help you a lot.

What is the process flow diagram?

As the name suggests, it is a graphical way of describing a process, its elements (steps), and their sequence.

Process flow diagrams show how a large complex process is broken down into smaller steps or tasks and how these go together.

As a data visualization technique, it can help your team see the bigger picture while illustrating the stages of a process.

When to use a process flow diagram?

  • When you need to display steps in a process and want to show their sequences clearly.

The above process flow diagram shows clearly the relationship between tasks in a customer ordering process.

The large ordering process is broken down into smaller functions and steps.

15. Spider/Radar Chart

Imagine, you need to rank your favorite beer on 8 aspects (Bitterness, Sweetness, Sourness, Saltiness, Hop, Malt, Yeast, and Special Grain) and then show them graphically. You can use a radar chart.

What is a radar chart?

Radar chart (also called spider, web, and polar bar) is a popular data visualization technique that displays multivariate data.

In can compare several items with many metrics of characteristics.

To be effective and clear, the radar chart should have more than 2 but no more than 6 items that are judged.

When to use a radar chart?

  • When you need to compare several items with more than 5 metrics of characteristics.

The above radar chart compares employee’s performance with a scale of 1-5 on skills such as Communications, Problem-solving, Meeting deadlines, Technical knowledge, Teamwork.

A point that is closer to the center on an axis shows a lower value and a worse performance.

It is obvious that Mary has a better performance than Linda.

16. Mind Map

Mind maps are beautiful data visuals that represent complex relationships in a very digestible way.

What is a mind map?

A mind map is a popular diagram that represents ideas and concepts.

It can help you structure your information and analyze, recall, and generate new ideas.

It is called a mind map because it is structured in a way that resembles how the human brain works.

And, best of all, it is a fun and artistic data visualization technique that engages your brain in a much richer way.

When to use a mind map?

  • When you want to visualize and connect ideas in an easy to digest way.
  • When you want to capture your thoughts/ideas and bring them to life in visual form.

Image source: Lucidchart

The above example of a mind map illustrates the key elements for running a successful digital marketing campaign.

It can help you prepare and organize your marketing efforts more effectively.

17. Gantt Chart

A well-structured Gantt chart aids you to manage your project successfully against time.

What is a Gantt chart?

Gantt charts are data visualization types used to schedule projects by splitting them into tasks and subtasks and putting them on a timeline.

Each task is listed on one side of the chart. This task also has a horizontal line opposite it representing the length of the task.

By displaying tasks with the Gantt chart, you can see how long each task will take and which tasks will overlap.

Gantt charts are super useful for scheduling and planning projects.

They help you estimate how long a project should take and determine the resources needed.

They also help you plan the order in which you’ll complete tasks and manage the dependencies between tasks.

When to use a Gantt chart?

  • When you need to plan and track the tasks in project schedules.

Image Source: Aha.io

The above example is a portfolio planning Gantt Chart Template that illustrates very well how Gantt Charts work.

It visualizes the release timeline for multiple products for an entire year.

It shows also dependencies between releases.

You can use it to help team members understand the release schedule for the upcoming year, the duration of each release, and the time for delivering.

This helps you in resource planning and allows teams to coordinate implementation plans.

18. Organizational Charts

Organizational charts are data visualization types widely used for management and planning.

What is an organizational chart?

An organizational chart (also called an org chart) is a diagram that illustrates a relationship hierarchy.

The most common application of an org chart is to display the structure of a business or other organization.

Org charts are very useful for showing work responsibilities and reporting relationships.

They help leaders effectively manage growth or change.

Moreover, they show employees how their work fits into the company’s overall structure.

When to use the org chart?

  • When you want to display a hierarchical structure of a department, company or other types of organization.

Image Source: Organimi

The above hierarchical org chart illustrates the chain of command that goes from the top (e.g., the CEOs) down (e.g., entry-level and low-level employees) and each person has a supervisor.

It clearly shows levels of authority and responsibility and who each person reports to.

It also shows employees the career paths and chances for promotion.

19. Area Map

Most business data has a location. Revenue, sales, customers, or population are often displayed with a dimensional variable on a map.

What is an area map?

It is a map that visualizes location data.

They allow you to see immediately which geographical locations are most important to your brand and business.

Image Source: Infogram

The map above depicts sales by location and the color indicates the level of sales (the darker the blue, the higher the sales).

These data visualization types are very useful as they show where in the world most of your sales are from and where your most valuable sales are from.

Insights like these illustrate weaknesses in a sales and marketing strategy in seconds.

20. Infographics

In recent years, the use of infographics has exploded in almost every industry.

From sales and marketing to science and healthcare, infographics are applied everywhere to present information in a visually appealing way.

What is an infographic?

Infographics are specific data visualization types that combine images, charts, graphs, and text. The purpose is to represent an easy-to-understand overview of a topic.

However, the main goal of an infographic is not only to provide information but also to make the viewing experience fun and engaging for readers.

It makes data beautiful—and easy to digest.

When you want to represent and share information, there are many data visualization types to do that – spreadsheets, graphs, charts, emails, etc.

But when you need to show data in a visually impactful way, the infographic is the most effective choice.

When to use infographics?

  • When you need to present complex data in a concise, highly visually-pleasing way.

Image Source: Venngage

The above statistical infographic represents an overview of Social Buzz’s biggest social platforms by age and geography.

For example, we see that 75% of active Facebook users are 18-29 years old and 48% of active users live in North America.

21. T-Chart

If you want to compare and contrast items in a table form, T-Chart can be your solution.

What is a T-Chart?

A T-Chart is a type of graphic organizer in the shape of the English letter “T”. It is used for comparison by separating information into two or more columns.

You can use T-Chart to compare ideas, concepts or solutions clearly and effectively.

T-Charts are often used for comparison of pros and cons, facts and opinions.

By using T-Chart, you can list points side by side, achieve a quick, at-a-glance overview of the facts, and arrive at conclusions quickly and easily.

When to use a T-Chart?

  • When you need to compare and contrast two or more items.
  • When you want to evaluate the pros and cons of a decision.

The above T-Chart example clearly outlines the cons and pros of hiring a social media manager in a company.

10 Best Data Visualization Tools

There is a broad range of data visualization tools that allow you to make fascinating graphs, charts, diagrams, maps, and dashboards in no time.

They vary from BI (Business Intelligence) tools with robust features and comprehensive dashboards to more simple software for just creating graphs and charts.

Here we’ve collected some of the most popular solutions. They can help you present your data in a way that facilitates understanding and decision making.

1. Visme is a data presentation and visualization tool that allows you to create stunning data reports. It provides a great variety of presentation tools and templates for a unique design.

2. Infogram is a chart software tool that provides robust diagram-making capabilities. It comes with an intuitive drag-and-drop editor and ready-made templates for reports. You can also add images for your reports, icons, GIFs, photos, etc.

3. Venngage is an infographic maker. But it also is a great chart software for small businesses because of its ease of use, intuitive design, and great templates.

4. SmartDraw is best for those that have someone graphic design skills. It has a slightly more advanced design and complexity than Venngage, Visme, and Infogram, … so having some design skills is an advantage. It’s a drawing tool with a wide range of charts, diagrams, maps, and well-designed templates.

5. Creately is a dynamic diagramming tool that offers the best free version. It can be deployed from the cloud or on the desktop and allows you to create your graphs, charts, diagrams, and maps without any tech skills.

6. Edraw Max is an all-in-one diagramming software tool that allows you to create different data visualization types at a high speed. These include process flow charts, line graphs, org charts, mind maps, infographics, floor plans, network diagrams, and many others. Edraw Max has a wide selection of templates and symbols, letting you to rapidly produce the visuals you need for any purpose.

7. Chartio is an efficient business intelligence tool that can help you make sense of your company data. Chartio is simple to use and allows you to explore all sorts of information in real-time.

8. Sisense – a business intelligence platform with a full range of data visualizations. You can create dashboards and graphical representations with a drag and drop user interface.

9. Tableau – a business intelligence system that lets you quickly create, connect, visualize, and share data seamlessly.

10. Domo is a cloud business intelligence platform that helps you examine data using graphs and charts. You can conduct advanced analysis and create great interactive visualization.

Data visualization techniques are vital components of data analysis, as they can summarize large amounts of data effectively in an easy to understand graphical form.

There are countless data visualization types, each with different pros, cons, and use cases.

The trickiest part is to choose the right visual to represent your data.

Your choice depends on several factors – the kind of conclusion you want to draw, your audience, the key metrics, etc.

I hope the above article helps you understand better the basic graphs and their uses.

When you create your graph or diagram, always remember this:

A good graph is the one reduced to its simplest and most elegant form without sacrificing what matters most – the purpose of the visual.

About The Author

different data representation techniques

Silvia Valcheva

Silvia Valcheva is a digital marketer with over a decade of experience creating content for the tech industry. She has a strong passion for writing about emerging software and technologies such as big data, AI (Artificial Intelligence), IoT (Internet of Things), process automation, etc.

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What are the different ways of Data Representation?

  • What are the Different Kinds of Data Scientists?
  • Different types of Coding Schemes to represent data
  • Graphical Representation of Data
  • What are the types of statistics?
  • Textual Presentation of Data: Meaning, Suitability, and Drawbacks
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  • Different Types of Data in Data Mining
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The process of collecting the data and analyzing that data in large quantity is known as statistics. It is a branch of mathematics trading with the collection, analysis, interpretation, and presentation of numeral facts and figures.

It is a numerical statement that helps us to collect and analyze the data in large quantity the statistics are based on two of its concepts:

  • Statistical Data 
  • Statistical Science

Statistics must be expressed numerically and should be collected systematically.

Data Representation

The word data refers to constituting people, things, events, ideas. It can be a title, an integer, or anycast.  After collecting data the investigator has to condense them in tabular form to study their salient features. Such an arrangement is known as the presentation of data.

It refers to the process of condensing the collected data in a tabular form or graphically. This arrangement of data is known as Data Representation.

The row can be placed in different orders like it can be presented in ascending orders, descending order, or can be presented in alphabetical order. 

Example: Let the marks obtained by 10 students of class V in a class test, out of 50 according to their roll numbers, be: 39, 44, 49, 40, 22, 10, 45, 38, 15, 50 The data in the given form is known as raw data. The above given data can be placed in the serial order as shown below: Roll No. Marks 1 39 2 44 3 49 4 40 5 22 6 10 7 45 8 38 9 14 10 50 Now, if you want to analyse the standard of achievement of the students. If you arrange them in ascending or descending order, it will give you a better picture. Ascending order: 10, 15, 22, 38, 39, 40, 44. 45, 49, 50 Descending order: 50, 49, 45, 44, 40, 39, 38, 22, 15, 10 When the row is placed in ascending or descending order is known as arrayed data.

Types of Graphical Data Representation

Bar chart helps us to represent the collected data visually. The collected data can be visualized horizontally or vertically in a bar chart like amounts and frequency. It can be grouped or single. It helps us in comparing different items. By looking at all the bars, it is easy to say which types in a group of data influence the other.

Now let us understand bar chart by taking this example  Let the marks obtained by 5 students of class V in a class test, out of 10 according to their names, be: 7,8,4,9,6 The data in the given form is known as raw data. The above given data can be placed in the bar chart as shown below: Name Marks Akshay 7 Maya 8 Dhanvi 4 Jaslen 9 Muskan 6

A histogram is the graphical representation of data. It is similar to the appearance of a bar graph but there is a lot of difference between histogram and bar graph because a bar graph helps to measure the frequency of categorical data. A categorical data means it is based on two or more categories like gender, months, etc. Whereas histogram is used for quantitative data.

For example:

The graph which uses lines and points to present the change in time is known as a line graph. Line graphs can be based on the number of animals left on earth, the increasing population of the world day by day, or the increasing or decreasing the number of bitcoins day by day, etc. The line graphs tell us about the changes occurring across the world over time. In a  line graph, we can tell about two or more types of changes occurring around the world.

For Example:

Pie chart is a type of graph that involves a structural graphic representation of numerical proportion. It can be replaced in most cases by other plots like a bar chart, box plot, dot plot, etc. As per the research, it is shown that it is difficult to compare the different sections of a given pie chart, or if it is to compare data across different pie charts.

Frequency Distribution Table

A frequency distribution table is a chart that helps us to summarise the value and the frequency of the chart. This frequency distribution table has two columns, The first column consist of the list of the various outcome in the data, While the second column list the frequency of each outcome of the data. By putting this kind of data into a table it helps us to make it easier to understand and analyze the data. 

For Example: To create a frequency distribution table, we would first need to list all the outcomes in the data. In this example, the results are 0 runs, 1 run, 2 runs, and 3 runs. We would list these numerals in numerical ranking in the foremost queue. Subsequently, we ought to calculate how many times per result happened. They scored 0 runs in the 1st, 4th, 7th, and 8th innings, 1 run in the 2nd, 5th, and the 9th innings, 2 runs in the 6th inning, and 3 runs in the 3rd inning. We set the frequency of each result in the double queue. You can notice that the table is a vastly more useful method to show this data.  Baseball Team Runs Per Inning Number of Runs Frequency           0       4           1        3            2        1            3        1

Sample Questions

Question 1: Considering the school fee submission of 10 students of class 10th is given below:

In order to draw the bar graph for the data above, we prepare the frequency table as given below. Fee submission No. of Students Paid   6 Not paid    4 Now we have to represent the data by using the bar graph. It can be drawn by following the steps given below: Step 1: firstly we have to draw the two axis of the graph X-axis and the Y-axis. The varieties of the data must be put on the X-axis (the horizontal line) and the frequencies of the data must be put on the Y-axis (the vertical line) of the graph. Step 2: After drawing both the axis now we have to give the numeric scale to the Y-axis (the vertical line) of the graph It should be started from zero and ends up with the highest value of the data. Step 3: After the decision of the range at the Y-axis now we have to give it a suitable difference of the numeric scale. Like it can be 0,1,2,3…….or 0,10,20,30 either we can give it a numeric scale like 0,20,40,60… Step 4: Now on the X-axis we have to label it appropriately. Step 5: Now we have to draw the bars according to the data but we have to keep in mind that all the bars should be of the same length and there should be the same distance between each graph

Question 2: Watch the subsequent pie chart that denotes the money spent by Megha at the funfair. The suggested colour indicates the quantity paid for each variety. The total value of the data is 15 and the amount paid on each variety is diagnosed as follows:

Chocolates – 3

Wafers – 3

Toys – 2

Rides – 7

To convert this into pie chart percentage, we apply the formula:  (Frequency/Total Frequency) × 100 Let us convert the above data into a percentage: Amount paid on rides: (7/15) × 100 = 47% Amount paid on toys: (2/15) × 100 = 13% Amount paid on wafers: (3/15) × 100 = 20% Amount paid on chocolates: (3/15) × 100 = 20 %

Question 3: The line graph given below shows how Devdas’s height changes as he grows.

Given below is a line graph showing the height changes in Devdas’s as he grows. Observe the graph and answer the questions below.

different data representation techniques

(i) What was the height of  Devdas’s at 8 years? Answer: 65 inches (ii) What was the height of  Devdas’s at 6 years? Answer:  50 inches (iii) What was the height of  Devdas’s at 2 years? Answer: 35 inches (iv) How much has  Devdas’s grown from 2 to 8 years? Answer: 30 inches (v) When was  Devdas’s 35 inches tall? Answer: 2 years.

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Types of Data Visualization and Their Uses

In today’s data-first business environment, the ability to convey complex information in an understandable and visually appealing manner is paramount. Different types of data visualization help transform analyzed data into comprehensible visuals for all types of audiences, from novices to experts. In fact, research has shown that the human brain can process images in as little as […]

different data representation techniques

In today’s data-first business environment, the ability to convey complex information in an understandable and  visually appealing  manner is paramount. Different types of data visualization help transform analyzed data into comprehensible visuals for all types of audiences, from novices to experts. In fact, research has shown that the human brain can process images in as little as 13 milliseconds.

different data representation techniques

In essence, data visualization is indispensable for distilling complex information into digestible formats that support both  quick comprehension  and informed decision-making. Its role in analysis and reporting underscores its value as a critical tool in any data-centric activity. 

Types of Data Visualization: Charts, Graphs, Infographics, and Dashboards

The diverse landscape of data visualization begins with simple charts and graphs but moves beyond infographics and animated dashboards.  Charts , in their various forms – be it bar charts for comparing quantities across categories or line charts depicting trends over time – serve as efficient tools for data representation. Graphs extend this utility further: Scatter plots reveal correlations between variables, while pie graphs offer a visual slice of proportional relationships within a dataset. 

Venturing beyond these traditional forms,  infographics  emerge as powerful storytelling tools, combining graphical elements with narrative to enlighten audiences on complex subjects. Unlike standard charts or graphs that focus on numerical data representation, infographics can incorporate timelines, flowcharts, and comparative images to weave a more comprehensive story around the data. 

A dashboard, when  effectively designed , serves as an instrument for synthesizing complex data into accessible and actionable insights. Dashboards very often encapsulate a wide array of information, from real-time data streams to historical trends, and present it through an amalgamation of charts, graphs, and indicators. 

A dashboard’s efficacy lies in its ability to tailor the visual narrative to the specific needs and objectives of its audience. By  selectively  filtering and highlighting critical data points, dashboards facilitate a focused analysis that aligns with organizational goals or individual projects. 

The best type of data visualization to use depends on the data at hand and the purpose of its presentation. Whether aiming to highlight trends, compare values, or elucidate complex relationships, selecting the appropriate visual form is crucial for effectively communicating insights buried within datasets. Through thoughtful design and strategic selection among these varied types of visualizations, one can illuminate patterns and narratives hidden within numbers – transforming raw data into meaningful knowledge.   

Other Types of Data Visualization: Maps and Geospatial Visualization  

Utilizing maps and geospatial visualization serves as a powerful method for uncovering and displaying insightful patterns hidden within complex datasets. At the intersection of geography and data analysis, this technique transforms numerical and categorical data into visual formats that are easily interpretable, such as heat maps, choropleths, or symbolic representations on geographical layouts. This approach enables viewers  to quickly grasp spatial relationships, distributions, trends, and anomalies that might be overlooked in traditional tabular data presentations. 

For instance, in public health,  geospatial visualizations  can highlight regions with high incidences of certain diseases, guiding targeted interventions. In environmental studies, they can illustrate changes in land use or the impact of climate change across different areas over time. By embedding data within its geographical context, these visualizations foster a deeper understanding of how location influences the phenomena being studied. 

Furthermore, the advent of interactive web-based mapping tools has enhanced the accessibility and utility of geospatial visualizations. Users can now engage with the data more directly – zooming in on areas of interest, filtering layers to refine their focus, or even contributing their own data points – making these visualizations an indispensable tool for researchers and decision-makers alike who are looking to extract meaningful patterns from spatially oriented datasets. 

Additionally,  scatter plots  excel in revealing correlations between two variables. By plotting data points on a two-dimensional graph, they allow analysts to discern potential relationships or trends that might not be evident from raw data alone. This makes scatter plots a staple in statistical analysis and scientific research where establishing cause-and-effect relationships is crucial. 

Bubble charts take the concept of scatter plots further by introducing a third dimension – typically represented by the size of the bubbles – thereby enabling an even more layered understanding of data relationships. Whether it’s comparing economic indicators across countries or visualizing population demographics, bubble charts provide a dynamic means to encapsulate complex interrelations within datasets, making them an indispensable tool for advanced data visualization. 

Innovative Data Visualization Techniques: Word Clouds and Network Diagrams 

Some innovative techniques have emerged in the realm of data visualization that not only simplify complex datasets but also enhance engagement and understanding. Among these, word clouds and network diagrams stand out for their  unique approaches  to presenting information. 

Word clouds represent textual data with size variations to emphasize the frequency or importance of words within a dataset. This technique transforms qualitative data into a visually appealing format, making it easier to identify dominant themes or sentiments in large text segments.

Network diagrams introduce an entirely different dimension by illustrating relationships between entities. Through nodes and connecting lines, they depict how individual components interact within a system – be it social networks, organizational structures, or technological infrastructures. This visualization method excels in uncovering patterns of connectivity and influence that might remain hidden in traditional charts or tables. 

Purpose and Uses of Each Type of Data Visualization 

The various types of data visualization – from bar graphs and line charts to heat maps and scatter plots – cater to different analytical needs and objectives. Each type is meticulously designed to highlight specific aspects of the data, making it imperative to understand their unique applications and strengths. This foundational knowledge empowers users to select the most effective visualization technique for their specific dataset and analysis goals.

Line Charts: Tracking Changes Over Time  Line charts are quintessential in the realm of data visualization for their simplicity and effectiveness in showcasing trends and changes over time. By connecting individual data points with straight lines, they offer a clear depiction of how values rise and fall across a chronological axis. This makes line charts particularly useful for tracking the evolution of quantities – be it the fluctuating stock prices in financial markets, the ebb and flow of temperatures across seasons, or the gradual growth of a company’s revenue over successive quarters. The visual narrative that line charts provide helps analysts, researchers, and casual observers alike to discern patterns within the data, such as cycles or anomalies.    

Bar Charts and Histograms: Comparing Categories and   Distributions  Bar charts  are highly suitable for representing comparative data. By plotting each category of comparison with a bar whose height or length reflects its value, bar charts make it easy to visualize relative values at a glance.

Histograms  show the distribution of groups of data in a dataset. This is particularly useful for understanding the shape of data distributions – whether they are skewed, normal, or have any outliers. Histograms provide insight into the underlying structure of data, revealing patterns that might not be apparent.  

Pie Charts: Visualizing Proportional Data   Pie charts  serve as a compelling visualization tool for representing proportional data, offering a clear snapshot of how different parts contribute to a whole. By dividing a circle into slices whose sizes are proportional to their quantity, pie charts provide an immediate visual comparison among various categories. This makes them especially useful in illustrating market shares, budget allocations, or the distribution of population segments.

The simplicity of pie charts allows for quick interpretation, making it easier for viewers to grasp complex data at a glance. However, when dealing with numerous categories or when precise comparisons are necessary, the effectiveness of pie charts may diminish. Despite this limitation, their ability to succinctly convey the relative significance of parts within a whole ensures their enduring popularity in data visualization across diverse fields. 

Scatter Plots: Identifying Relationship and Correlations Between Variables Scatter plots  are primarily used for spotting relationships and correlations between variables. These plots show data points related to one variable on one axis and a different variable on another axis. This visual arrangement allows viewers to determine patterns or trends that might indicate a correlation or relationship between the variables in question. 

For instance, if an increase in one variable consistently causes an increase (or decrease) in the other, this suggests a potential correlation. Scatter plots are particularly valuable for preliminary analyses where researchers seek to identify variables that warrant further investigation. Their straightforward yet powerful nature makes them indispensable for exploring complex datasets, providing clear insights into the dynamics between different factors at play. 

Heat Maps: Representing Complex Data Matrices through Color Gradients Heat maps  serve as a powerful tool in representing complex data matrices, using color gradients to convey information that might otherwise be challenging to digest. At their core, heat maps transform numerical values into a visual spectrum of colors, enabling viewers to quickly grasp patterns, outliers, and trends within the data. This method becomes more effective when the complex relationships between multiple variables need to be reviewed.  

For instance, in fields like genomics or meteorology, heat maps can illustrate gene expression levels or temperature fluctuations across different regions and times. By assigning warmer colors to higher values and cooler colors to lower ones, heat maps facilitate an intuitive understanding of data distribution and concentration areas, making them indispensable for exploratory data analysis and decision-making processes.

Dashboards and Infographics: Integrating Multiple Data Visualizations  Dashboards and infographics represent a synergistic approach in data visualization, blending various graphical elements to offer a holistic view of complex datasets.  Dashboards,  with their capacity to integrate multiple data visualizations such as charts, graphs, and maps onto a single interface, are instrumental in monitoring real-time data and tracking performance metrics across different parameters. They serve as an essential tool for decision-makers who require a comprehensive overview to identify trends and anomalies swiftly. 

Infographics, on the other hand, transform intricate data sets into engaging, easily digestible visual stories. By illustrating strong narratives with striking visuals and solid statistics, infographics make complex information easily digestible to any type of audience. 

Together, dashboards and infographics convey multifaceted data insights in an integrated manner – facilitating informed decisions through comprehensive yet clear snapshots of data landscapes.     

How to choose the right data visualization

Posted by: mike yi, mel restori.

Data visualizations are a vital component of a data analysis, as they have the capability of summarizing large amounts of data efficiently in a graphical format. There are  many chart types available , each with its own strengths and use cases. One of the trickiest parts of the analysis process is choosing the right way to represent your data using one of these visualizations.

In this article, we will approach the task of choosing a data visualization based on the type of task that you want to perform.

Common roles for data visualization include:

  • showing change over time
  • showing a part-to-whole composition
  • looking at how data is distributed
  • comparing values between groups
  • observing relationships between variables

looking at geographical data

The types of variables you are analyzing and the audience for the visualization can also affect which chart will work best within each role. Certain visualizations can also be used for multiple purposes depending on these factors.

Charts for showing change over time

One of the most common applications for visualizing data is to see the change in value for a variable across time. These charts usually have time on the horizontal axis, moving from left to right, with the variable of interest’s values on the vertical axis. There are multiple ways of encoding these values:


  • Bar charts  encode value by the heights of bars from a baseline.
  • Line charts  encode value by the vertical positions of points connected by line segments. This is useful when a baseline is not meaningful, or if the number of bars would be overwhelming to plot.
  • A  box plot  can be useful when a distribution of values need to be plotted for each time period; each set of box and whiskers can show where the most common data values lie.

There are a number of specialist chart types for the financial domain, like the candlestick chart or Kagi chart.

Charts for showing part-to-whole composition

Sometimes, we need to know not just a total, but the components that comprise that total. While other charts like a standard bar chart can be used to compare the values of the components, the following charts put the part-to-whole decomposition at the forefront:

Pie charts and stacked area charts are among the chart types that can be used to show part-to-whole comparisons.

  • The  pie chart  and cousin donut chart represent the whole with a circle, divided by slices into parts.
  • A  stacked bar chart  modifies a bar chart by dividing each bar into multiple sub-bars, showing a part-to-whole composition within each primary bar.
  • Similarly, a  stacked area chart  modifies the line chart by using shading under the line to divide the total into sub-group values.

A host of other more intricate chart types have also been developed to show hierarchical relationships. These include the Marimekko plot and treemap.

Charts for looking at how data is distributed

One important use for visualizations is to show how data points’ values are distributed. This is particularly useful during the exploration process, when trying to build an understanding of the properties of data features.

Histograms and box plots are among the chart types that can be used to show distributions in data values.

  • Bar charts  are used when a variable is qualitative and takes a number of discrete values.
  • A  histogram  is used when a variable is quantitative, taking numeric values.
  • Alternatively, a  density curve  can be used in place of a histogram, as a smoothed estimate of the underlying distribution.
  • A  violin plot  compares numeric value distributions between groups by plotting a density curve for each group.

The  box plot  is another way of comparing distributions between groups, but with a summary of statistics rather than an estimated distributional shape.

Charts for comparing values between groups

Another very common application for a data visualization is to compare values between distinct groups. This is frequently combined with other roles for data visualization, like showing change over time, or looking at how data is distributed.


  • A  bar chart  compares values between groups by assigning a bar to each group.
  • A  dot plot  can be used similarly, except with value indicated by point positions instead of bar lengths. This is like a line chart with the line segments removed, eliminating the ‘connection’ between sequential points. Also like a line chart, a dot plot is useful when including a vertical baseline would not be meaningful.
  • A  line chart  can be used to compare values between groups across time by plotting one line per group.
  • A  grouped bar chart  allows for comparison of data across two different grouping variables by plotting multiple bars at each location, not just one.
  • Violin plots  and  box plots  are used to compare data distributions between groups.
  • A  funnel chart  is a specialist chart for showing how quantities move through a process, like tracking how many visitors get from being shown an ad to eventually making a purchase.
  • Bullet charts  are another specialist chart for comparing a true value to one or more benchmarks.

One sub-category of charts comes from the comparison of values between groups for multiple attributes. Examples of these charts include the parallel coordinates plot (and its special case the slope plot), and the dumbbell plot.

Charts for observing relationships between variables

Another task that shows up in data exploration is understanding the relationship between data features. The chart types below can be used to plot two or more variables against one another to observe trends and patterns between them.

Scatter plots and heatmaps are among the chart types that can be used to show distributions in data values.

  • The  scatter plot  is the standard way of showing the relationship between two variables.
  • Scatter plots can also be expanded to additional variables by adding color, shape, or size to each point as indicators, as in a  bubble chart .
  • When a third variable represents time, points in a scatter plot can be connected with line segments, generating a  connected scatter plot .
  • Another alternative for a temporal third-variable is a  dual-axis plot , such as plotting a line chart and bar chart with a shared horizontal axis.
  • When one or both variables being compared are not numeric, a  heatmap  can show the relationship between groups. Heatmaps can also be used for purely numeric data, like in a 2-d histogram or 2-d density curve.

Charts for looking at geographical data

Sometimes, data includes geographical data like latitude and longitude or regions like country or state. While plotting this data might just be extending an existing visualization onto a map background (e.g. plotting points like in a scatter plot on top of a map), there are other chart types that take the mapping domain into account. Two of these are highlighted below:

The choropleth and cartogram are examples of charts used to depict geographical data.

Right: Cartogram of US Population from  census.gov

  • A  choropleth  is like a heatmap that colors in geopolitical regions rather than a strict grid.
  • Cartograms  take a different approach by using the size of each region to encode value. This approach necessitates some distortion in shapes and topology.

Closing thoughts

Choosing the right chart for the job depends on the kinds of variables that you are looking at and what you want to get out of them. The above is only a general guideline: it is possible that breaking out of the standard modes will help you gain additional insights. Experiment with not just different chart types, but also how the variables are encoded in each chart. It’s also good to keep in mind that you aren’t limited to showing everything in just one plot. Often it is better to keep each individual plot as simple and clear as possible, and instead use multiple plots to make comparisons, show trends, and demonstrate relationships between multiple variables.

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The Power of a Good Chart: Data Visualization Techniques

Consider you’re a meteorologist, and you’ve gathered data on weather patterns over the past decade. With rows upon rows of temperatures, wind speeds, and precipitation levels, the raw data is overwhelming. But visualize that data on a graph, and suddenly, you can see trends, spot outliers, and communicate your findings effortlessly.

This is the essence of data visualization: turning complex data sets into clear, informative, and engaging visuals that can be understood at a glance.

Understanding Data Visualization

Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization provides an accessible way to see and understand trends, outliers, and patterns in data.

In the same way that you intuitively clustered with like-minded individuals at a party, data visualization helps you identify clusters and relationships within data.

Essential Types of Data Visualizations

Data visualization comes in various formats, each with unique advantages. Here are some of the most effective and commonly used types:

  • Bar Charts : Ideal for comparing quantities among different groups.
  • Line Graphs : Perfect for illustrating trends over time.
  • Pie Charts : Used to show parts of a whole.
  • Scatter Plots : Great for identifying relationships between variables.
  • Heat Maps : A method for showing patterns of activity or the density of events.
  • Histograms : Useful for displaying the distribution of a dataset.

Effective Data Visualization: A step-by-step guide

Here’s a straightforward approach to creating visuals that make an impact:

  • Understand Your Data : Know what you’re working with and what you want to communicate.
  • Select the Right Chart : Match your objectives with the most effective visual representation.
  • Keep it Simple : Exclude any unnecessary information that doesn’t serve the purpose of the visualization.
  • Highlight What’s Important : Use colors, arrows, or labels to draw attention to key data.
  • Ensure Accuracy : Double-check your data and how it’s displayed—accuracy is key to maintaining credibility.

Using the right type of visualization can mean the difference between misinterpretation and clear understanding.

Tools for Creating Data Visualizations

Many tools can transform your data into insightful visuals:

  • Tableau : Known for its ability to handle large datasets and interactive features.
  • Microsoft Power BI : Integrates well with other Microsoft products and services.
  • QlikView : Offers in-memory data processing, which gives it a performance advantage.
  • Google Charts : A good free option that is web-based and easy to integrate with other Google services.

Other Visualization Techniques Tailored to Specific Needs

Beyond these basic tools, there are specialized visualization techniques such as geographic mapping tools, dynamic and interactive visualizations, and complex network graphs that cater to more nuanced and specific visualization needs.

Strengths and Weaknesses of Data Visualization

Considering both the upsides and the limits of data visualization is vital for effective application.

  • It makes complex data more accessible and understandable.
  • It facilitates quicker decision-making by highlighting key takeaways.
  • It can identify patterns, trends, and correlations effortlessly.
  • It’s a powerful storytelling tool to present and share insights.


  • Misleading visuals can result from poor design choices or scale manipulation.
  • Overuse of colors or elements can clutter the message.
  • It requires a thoughtful selection of visualization types to match data characteristics.

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  • Mastering Data Visualization: Essential Interview Questions
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Mastering the Art of Data Visualization: Tips & Techniques

Mastering the Art of Data Visualization: Tips & Techniques

Data Visualization Modeling posted by ODSC Community October 5, 2023 ODSC Community

In the digital era, data visualization stands as an indispensable tool in the realm of business intelligence . It represents the graphical display of data and information, transforming complex datasets into intuitive and understandable visuals.

By implementing data visualization, businesses can reap multifaceted benefits:

  • Simplified Data Interpretation : Complex data are converted into easily comprehensible visuals, enabling quick interpretation and understanding
  • Enhanced Decision-making Process : Visual data representation aids in identifying patterns, trends, and outliers, fostering informed decision-making
  • Improved Information Retention : Visuals increase information retention and recall, promoting long-term strategic planning

Remember that effective data visualization acts not only as a summary of your data but also as a guide towards informed decisions. By harnessing the power of visual grammar rules and frameworks like the McCandless Method or Kaiser Fung’s Junk Charts Trifecta Checkup, you can elevate your business intelligence strategy to new heights.

Key Principles and Best Practices for Data Visualization

Understanding the key principles and best practices for data visualization can significantly improve your ability to present data in a meaningful, digestible way.

Visual Grammar Rules

Visual grammar rules are fundamental to creating effective visual presentations. These include clarity, simplicity, and emphasis on the information rather than the graphic design itself.

  • Clarity: The purpose of your visualization should be immediately clear to your audience. Avoid unnecessary complexity in charts, graphs, and diagrams.
  • Simplicity: Keep designs as simple as possible while still conveying the necessary information. Extraneous elements can distract from the data you’re presenting.
  • Emphasis on Information: The main focus should always be on the data, not on the aesthetics of the presentation. Avoid using overly flashy designs or effects that could detract from your message.

Consider these points when designing your own visualizations.

Consider a bar chart showing annual sales figures for your company’s product range. Making each bar a different color might make the chart look more appealing, but it can confuse the audience if there’s no clear reason for the color variation. Instead, use one color for all bars and differentiate them by labeling each one with the product name and sales figure.

This approach applies clarity , simplicity , and an emphasis on information , demonstrating how these visual grammar rules enhance data visualization best practices.

Incorporate these design principles into your own work to ensure your visualizations communicate effectively, keeping your audience engaged and informed without overwhelming them with unnecessary detail or confusing graphics.

Moving forward, let’s delve deeper into organizing visualization through popular frameworks that help structure data effectively. Using such frameworks will enable you to deliver insights more coherently, making it easier for your audience to understand complex datasets.

Frameworks for Organizing Visualization

Organizing data visualization is an art as much as it is a science. It’s where design principles meet visual grammar rules, creating effective and engaging visuals. Two popular frameworks that encapsulate data visualization best practices are the McCandless Method and Kaiser Fung’s Junk Charts Trifecta Checkup .

The McCandless Method, coined by David McCandless, advocates for a balance between information and design. This involves considering aspects like:

  • Ensuring data accuracy
  • Emphasizing clarity and precision
  • Incorporating a meaningful color palette
  • Utilizing pre-attentive attributes effectively to guide viewer attention

Meanwhile, Kaiser Fung’s Junk Charts Trifecta Checkup takes a more analytical approach to visualization. It encourages us to ask three key questions:

  • What does the chart show?
  • What does the data say?
  • What relevant factor is missing?

By following these frameworks, you can create striking visuals that not only look good but also communicate your message effectively.

Effective Techniques for Data Visualization

Let’s dive into the world of Power BI to explore its features and learn some useful data visualization tips and tricks for data visualization. Power BI, a business analytics tool developed by Microsoft, offers interactive visualizations with self-service business intelligence capabilities.

Using Power BI Tips and Tricks

To begin with, Power BI stands out in the crowd due to its ability to produce beautiful reports with interactive visualizations. It allows sharing these reports directly within the platform or embedding them in an app or website. The tool’s drag-and-drop feature simplifies creating complex dashboards, making it user-friendly even for beginners.

One of the most potent features of Power BI is Quick Insights . This function finds patterns, trends, and correlations in the data automatically. To use it effectively:

  • Select a dataset
  • Click ‘Get Insights’
  • Wait for Power BI to do its magic!

Another valuable feature is Natural Language Querying . With this feature, you can type in questions about your data in natural language and get immediate answers. For example, if you have an e-commerce business and want to know your best-selling product last month, just type “What was my top-selling product last month?” into the query box.

The Q&A Visual takes this one step further by allowing users to ask questions directly on the report page and presenting answers in a visual format. To harness this feature:

  • Drag the Q&A button onto your report page
  • Start asking questions!

Don’t forget about Bookmarking . This tool allows you to save a customized view of a report (filters and slicers) and return to it at any point. This is particularly useful when dealing with large datasets.

Lastly, consider using Data Drill Down for hierarchical data visualization. This technique helps users navigate from general overviews down to specific details in just a few clicks.

In the realm of data visualization, Power BI is a game-changer with its advanced features and user-friendly interface. Harness these data visualization tips and techniques to create compelling visualizations and gain valuable insights from your data. Up next, we’ll delve into more intriguing aspects of data visualization: keyboard shortcuts and custom themes. Stay tuned!

Utilizing Keyboard Shortcuts and Custom Themes

Efficiency in data visualization is crucial. One way to boost your productivity is through keyboard shortcuts . They provide a fast, seamless way to navigate your workspace, execute commands, and manipulate data. For instance, in Power BI, you can use Ctrl + M to start a new measure or Alt + Shift + F10 to access the context menu.

Complementing the use of shortcuts, custom themes are essential for enhancing visual appeal. They not only add color and style but also consistency across your visuals. You can create custom themes within Power BI by going to View > Themes > Customize current theme . From there, you can tweak colors, text properties, and visual elements to match your brand or preference.

Remember: The right combination of keyboard efficiency and design aesthetics elevates the impact of your data visualization techniques.

Data Modeling and Drill-Through Techniques

Data modeling plays a crucial role in data visualization. It’s the process of creating a visual representation of data, which can help to understand complex patterns and relationships. Using data modeling effectively allows you to uncover insights that would be difficult to grasp in raw, unprocessed data.

To illustrate, it’s like taking a jumbled pile of puzzle pieces and organizing them into an understandable image. When you organize your data in this way, it becomes easier for everyone to understand.

One of the most powerful techniques in data modeling is drill-through. This technique allows users to navigate from a summary view into detailed data. For instance, if you’re viewing sales data by region, a drill-through could allow you to click on one region and see the individual sales by city or even by store.

Here are some tips for implementing drill-through techniques:

  • Plan Ahead : Define what detailed information would be useful before setting up your drill-throughs.
  • Limit Your Layers : Too many layers can confuse users. Stick to a few key details.
  • Use Clear Labels : Make sure it’s obvious what each layer represents.

These techniques, when used correctly, can dramatically enhance your ability to communicate complex information through your visualizations.

Real-Time Dashboards and Explaining Data

Access to real-time dashboards in data visualization provides a game-changing advantage. These dynamic tools compile and display data as it enters the system, allowing for immediate analysis and action. Benefits of utilizing real-time dashboards include:

  • Keeping stakeholders informed with up-to-the-minute data
  • Enabling rapid response to emerging trends or issues
  • Facilitating ongoing optimization of strategies based on live data

To extract maximum value from your real-time dashboards, it’s essential to adequately explain the data they present. Densely packed data or complex graphs can be overwhelming without clear, concise explanations. Here are some techniques to effectively communicate complex data:

  • Simplicity : Distil complex ideas into simple, understandable terms. Avoid jargon where possible.
  • Context : Provide relevant background information to help readers understand why the data matters.
  • Visual Aids : Use charts, graphs, and infographics to represent data visually, making it easier to digest.
  • Narrative : Weave a story around the data to make it more engaging and relatable.

By employing these techniques, you can ensure that your audience not only sees the numbers but also comprehends their implications. Remember that balancing real-time insight with effective explanations can greatly improve your decision-making process in any online business venture.

Through this article, we’ve unlocked the potential of data visualization , from understanding its benefits to adopting best practices. We’ve dived into Visual Grammar Rules and explored frameworks like the McCandless Method and Kaiser Fung’s Junk Charts Trifecta Checkup . Power BI has been our companion, guiding us through data visualization tips, tricks, and techniques such as keyboard shortcuts, custom themes, data modeling, and drill-through techniques.

We encourage you to leverage these insights in your quest for effective data visualization. Remember, the key is to keep experimenting and learning. Let’s transform complex data into understandable visuals together. After all, a picture is worth a thousand words.

About the author on data visualization tips:

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ODSC Community

The Open Data Science community is passionate and diverse, and we always welcome contributions from data science professionals! All of the articles under this profile are from our community, with individual authors mentioned in the text itself.

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Mastering Data Visualization Techniques: From Raw Data to Visual Stories

Mastering data visualization is the first step toward using data analytics and data science to your advantage to add value to your organization. Here, you will learn how to use different data visualization techniques and reap the rewards of data-driven decision-making.  

Whatagraph marketing reporting tool

Apr 04 2020 ● 4 min read

data visualization techniques cover

Table of Contents

What is data visualization, factors that influence data visualization choice, data visualization techniques, 1. pie chart, 2. bar chart, 3. histogram, 4. line graph, 5. heat map, 6. area chart, 7. scatter plot, 8. box and whisker plot, 10. word clouds and network diagrams, tools for data visualization, microsoft power bi, wrapping up.

Data visualization is the process of creating visual representations of information such as simple charts, maps, graphs, plots, infographics, and dashboards. These data visualization techniques help present data in a way that is easier for the viewer to understand and make the right decisions.

You should use different visualization methods to present complex data and its interactions. But the visualization technique you choose is determined by factors like audience, context, type of data, etc. Choose the wrong tactics, and you risk leaving half the information on the ground .

So, what determines what data visualization you’ll use?

Your visual data representation needs to be suited to the specific target audience. For example, a personal finance app should use uncomplicated visualization like bar charts or pie charts that are easy to read.

simple data visualizations

On the other hand, data scientists or C-level decision-makers who regularly work with data will benefit from more complex visualizations like box-and-whiskers plots, stacked area charts, etc.

The type of data you need to present will often determine the visualization method. For example, you might use line charts to show the dynamics of time-series metrics. You can use a pie chart to show the percentage or share within a whole. Bar charts work well for comparative analysis; you can use a scatter plot to establish correlations between multiple data points.

How you will use certain types of data visualization often depends on the context. For example, you can use different shades of one color to showcase growth. To differentiate elements, you can use contrast colors, and to show positive and negative variance, you can use a diverging color scale, e.g., going from bright red to deep blue.

Another factor determining the choice of visual elements is the rate of change or data dynamics. Financial results can be measured monthly or yearly, while time series and tracking data change constantly . Depending on the rate of change, you may consider dynamic visualization, like an interactive dashboard or static visualization .

The purpose of data visualization can also determine the way of use. A complex analysis requires grouping visualizations into dynamic dashboards with multiple visual data analytics features, such as filtering, comparison, and data transformation . On the other hand, if you need to show occasional data insights, there’s no need for a dashboard.

40+ data

Pie charts are simple and easy to read, making them ideal for audiences interested only in key takeaways. Understanding of data is not required. A workhorse of your data visualization arsenal, you can use a pie chart to illustrate proportions or part-to-whole comparisons.

This chart type is most effective when used with text and percentages to describe the content. Without the percentage values, pie charts can be challenging to interpret as the human eye has difficulty estimating areas, for example when two or more categories have similar arc lengths.

In such cases, a bar chart would be a better choice.

Pie charts work best when there are just two or three categories, so the viewer needs to make fewer comparisons. For example, here’s the proportion of paid ads traffic in Whatagraph’s PPC dashboard:

pie chart

The sample data shows that Facebook Ads bring in the bulk of the traffic, while LinkedIn Ads barely outrun Google Ads.

While you’re here, maybe you want to check out the rest of this fantastic PPC Overview Dashboard Template we created.

A bar chart is another data visualization staple that is easy to read and interpret. Here, one chart axis shows the categories compared, and the other a measured value.

Each bar's length is proportional to each category's value, making these charts an excellent alternative to tables for showing the values of different categories.

Bar chart

This bar chart from Whatagraph’s Web Traffic Report Template clearly shows where the most traffic comes from.

Data analysts typically use bar charts to make comparisons. However, as with pie charts, having too many categories involved can make it difficult to label and compare them.

In that case, you may want to use:

  • Horizontal bar charts : A variation of an original bar chart that works best if you need to visualize many categories with longer names. The chart flows in the same direction as we read the text. Also, while values are presented on the y-axis in a regular bar chart, in a horizontal variety, you must ensure the x-axis always starts from zero.
  • Stacked bar graph : This type of visualization is used to display multiple variables within each bar. For example, different colors might stand for different revenue channels, such as inbound and outbound.

A histogram is a graphical representation of information that uses bars of various heights to illustrate data distribution over a defined period or continuous interval. This makes histograms ideal for identifying increased activity or concentration of values and substandard values and gaps.

For example, you can use a histogram to show how many clicks your website received each day over the past week, which helps you determine on which days your visitors are the most active.


Histograms allow you to inspect data for its underlying distribution, outliers (data points that differ significantly from other observations), skewness (distortion of a symmetrical distribution), etc.

Keep in mind, however, that in histograms, the height of the bar doesn’t necessarily indicate how many occurrences there were within each bar (bin). It’s actually the product of height multiplied by the width that shows the frequency of occurrences in that bin.

This confusion often comes from the fact that many histograms have equally spaced bars, in which case, the bar's height reflects the frequency.

Histogram vs. bar chart

The most significant difference between these two chart types is that a histogram is used to show the frequency of score occurrences in a continuous data set divided into bins. Bar charts, on the other hand, are used for many different types of variables, including ordinal and nominal data sets.

Lina graphs are perfect for tracking data change over long periods or continuously changing data. As such, line charts are most commonly used to indicate trends.

Sometimes, you can have more series (lines) in a single chart, like in the graph below.

Line graph with two series

As with pie and bar charts, having too many series can make your visualization look messy. Make it easier for the viewer by formatting the chart so that the most critical series has the most visible color, like in the example from Ahrefs below:

multiseries line graph

A heat map is a type of data visualization that uses color coding the way a bar graph uses height to indicate numerical values. The color difference makes it easier for viewers to understand the situation quickly.

Heat maps have a wide use. Mapped against a web page or geospatial image, colors can tell you which areas get the most attention.

Although heat maps are an effective data visualization technique for examining a large number of values, they lack the precision of bar charts and other more accurate presentations — simply because color differences are difficult to measure.

An area chart is a variation of a line graph in which the area below the line is shaded to represent the total value of each data point. You can use stacked area charts if you need to compare several data series on the same graph.

This data visualization technique is useful for displaying changes in one or more quantities over time and showing how each quantity combines to make up the whole.

In finance, for example, one area can represent the value of company shares, while the second variable shows the industry benchmark against which you’re comparing it. By looking at it, investors can see how much they would pay for stock shares for each dollar they earn.

A scatter plot is a type of graph that displays data for two variables represented by points plotted against the horizontal and vertical axis. Scatter plots are useful for presenting relations between variables and identifying trends and correlations in data.

Identifying trends is easier when more data points are present, making scatter plots ideal for visualizing very large data sets. The closer the data points are grouped, the stronger the correlation or trends.

These properties make scatter plots an integral part of machine learning.

scatter plot in Python

Machine learning scatter plot in Python’s Matplotlib feature

Still, when interpreting scatter plots, you need to be careful. Even if two variables might be strongly correlated, it needn’t mean there is a causal relationship behind them.

For example, a scatter plot of draft beer sales along the coastline can strongly correlate with the number of shark attacks. This doesn’t mean that buying draft beer causes shark attacks but rather that whatever is causing one trend is also causing the other — in this case, hot and sunny weather that makes people go for refreshments and a swim.

Also known as a box plot, this chart provides a visual summary of data through its quartiles — values that divide sorted data into four parts, each with an equal number of observations.

box and whisker chart

A box is drawn from the first quartile to the third of the data set, with the vertical line within the box representing the median. Whiskers extend from the box to the minimum and maximum value. Outliers are represented by individual points that are in line with the whiskers.

A box and whiskers plot is ideal for quickly identifying whether or not the data is symmetrical or skewed.

Treemaps are a visually engaging way of showing part-to-whole relationships in data. Here, hierarchical data is represented as a set of squares. Each square (called leaf node) is a category within a given variable, while the area of each square is proportional to the size of that category.

This makes treemaps more intuitive than other part-to-whole visualizations, like pie charts.

A huge advantage of treemaps is that they can display many categories on the screen simultaneously, making efficient use of available space.

A treemap can also include different categories, but in that case, each category needs to have a different color.

You can use word clouds or network diagrams to visually represent semi-structured or intentionally unstructured data.

A word cloud is a visual representation of textual data in which the word size is proportional to its frequency. The more often a specific word appears in a dataset, the larger its visualization. Apart from the size, words can have different boldness or color schemes depending on their frequency.

word cloud chart

Word clouds are a good choice for identifying essential keywords and comparing differences in textual data between two sources.

Network diagrams are often used to graphically represent a network. This type of visualization is helpful for network engineers, designers, and data analysts while compiling extensive network documentation.

With data visualization techniques explained, let’s make a selection of the best visualization tools you can use to speed up the process.

Whatagraph is an all-in-one platform to connect, visualize, and share marketing data. This means you can complete the whole reporting process within one app without needing third-party data connectors or visualization tools.

Pull data directly from over 45 marketing platforms and visualize it on detailed dashboards and reports using a variety of visualization types.

Still, you can connect any data source using a Custom API, Google Sheets, or BigQuery data warehouse.

whatagraph marketing report

This makes Whatagraph an ideal choice for both marketing agencies that handle multiple clients, as well as large enterprises that need fresh and accurate multi-source updates to make data-driven decisions.

Use our intuitive drag-and-drop report builder to create a wide range of marketing data visualization , or pick a ready-made report or dashboard template from our library .

Once you visualize your data in Whatagraph, you can share it in a few clicks.

Create an email template, set the recipients, frequency, and delivery time, and automate the sendouts. Alternatively, you can share a live link to a report or dashboard so the recipients can check data as it updates.

cross-channel reports

Microsoft Power BI is a data visualization and business intelligence tool that allows you to create interactive dashboards and reports. Power BI is ideal for users who need to perform deep analytics, combine data from multiple sources, and predict outcomes by identifying real-time trends.

Apart from having exceptional Excel integration, Power BI enables data mining from various databases such as CSV, XML, JSON, SQL Servers, and cloud-based sources like Microsoft Azure data warehouse and Salesforce CRM.

With Power BI you can easily share your insights with others, making collaborating on data analysis projects easier.

Still, when it comes to visualization customization options, Power BI still needs work. It’s more of an all-around data visualization tool for in-house teams that need to visualize complex data for stakeholders. Marketing agencies, on the other hand, could benefit more from a tool like Whatagraph that allows you to change the color scheme and branding to make each report unique for individual clients.

Tableau is a cloud-based data visualization platform that allows you to connect to any data source and create interactive, shareable dashboards.

It helps simplify big data into easily digestible visualizations so that technical and non-technical users can understand it.

tableau dashboard

Tableau is more of a universal data visualization tool than Whatagraph and has powerful business intelligence capabilities. This is why professionals and researchers in various industries use it to answer important and complex data questions.

Data visualization techniques allow users to make large volumes of data more accessible and understandable to audiences that may not understand how data works. With competitors scrabbling for data insights themselves, a quick and reliable way to analyze collected information can give you a huge competitive advantage.

Whether you stick to simple visualization methods or combine them into infographics or interactive dashboards, you can’t ignore the power of visualization.

But you shouldn’t spend a lot of time visualizing your data.

You should be able to add your sources, choose the visualization widgets, and start reading the trends.

That’s just the workflow that Whatagraph provides.

Why don’t you try it and see what your marketing data looks like in our dashboards?

Request a free trial today and visualize your data more efficiently than ever before.

40 data sources

Published on Apr 04 2020

Gintaras is an experienced marketing professional who is always eager to explore the most up-to-date issues in data marketing. Having worked as an SEO manager at several companies, he's a valuable addition to the Whatagraph writers' pool.

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  • Benefits of good data visualization
  • Data Visualization Techniques
  • List of Methods to Visualize Data
  • Five Number Summary of Box Plot
  • Histograms are based on area, not height of bars
  • Histogram Vs Bar Chart
  • Word Clouds and Network Diagrams for Unstructured Data
  • FAQs Related to Data Visualization

Understanding Data Visualization Techniques

Data visualization  is a graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data. This blog on data visualization techniques will help you understand detailed techniques and benefits.

In the world of Big Data, data visualization in Python tools and technologies are essential to analyze massive amounts of information and make data-driven decisions. 

Contributed by: Dinesh

Our eyes are  drawn to colours and patterns . We can quickly identify red from blue, and square from the circle. Our culture is visual, including everything from art and advertisements to TV and movies.

Data visualization is another form of visual art that grabs our interest and keeps our eyes on the message. When we see a chart, we  quickly see trends and outliers . If we can see something, we internalize it quickly. It’s storytelling with a purpose. If you’ve ever stared at a massive spreadsheet of data and couldn’t see a trend, you know how much more effective a visualization can be. The uses of Data Visualization as follows.

  • Powerful way to explore data with presentable results.
  • Primary use is the pre-processing portion of the data mining process.
  • Supports the data cleaning process by finding incorrect and missing values.
  • For variable derivation and selection means to determine which variable to include and discarded in the analysis.
  • Also play a role in combining categories as part of the data reduction process.
  • Word Cloud/Network diagram

E nrol Now – Data Visualization Using Tableau course for free offered by Great Learning Academy .

The image above is a box plot .  A boxplot is a standardized way of displaying the distribution of data based on a five-number summary (“minimum”, first quartile (Q1), median, third quartile (Q3), and “maximum”). It can tell you about your outliers and what their values are. It can also tell you if your data is symmetrical, how tightly your data is grouped, and if and how your data is skewed.

A box plot is a graph that gives you a good indication of how the values in the data are spread out. Although box plots may seem primitive in comparison to a histogram or density plot, they have the advantage of taking up less space, which is useful when comparing distributions between many groups or datasets. For some distributions/datasets, you will find that you need more information than the measures of central tendency (median, mean, and mode). You need to have information on the variability or dispersion of the data.

  • Column Chart: It is also called a vertical bar chart where each category is represented by a rectangle. The height of the rectangle is proportional to the values that are plotted.
  • Bar Graph: It has rectangular bars in which the lengths are proportional to the values which are represented.
  • Stacked Bar Graph: It is a bar style graph that has various components stacked together so that apart from the bar, the components can also be compared to each other.
  • Stacked Column Chart: It is similar to a stacked bar; however, the data is stacked horizontally.
  • Area Chart: It combines the line chart and bar chart to show how the numeric values of one or more groups change over the progress of a viable area.
  • Dual Axis Chart: It combines a column chart and a line chart and then compares the two variables.
  • Line Graph: The data points are connected through a straight line; therefore, creating a representation of the changing trend.
  • Mekko Chart: It can be called a two-dimensional stacked chart with varying column widths.
  • Pie Chart: It is a chart where various components of a data set are presented in the form of a pie which represents their proportion in the entire data set.
  • Waterfall Chart: With the help of this chart, the increasing effect of sequentially introduced positive or negative values can be understood.
  • Bubble Chart: It is a multi-variable graph that is a hybrid of Scatter Plot and a Proportional Area Chart.
  • Scatter Plot Chart: It is also called a scatter chart or scatter graph. Dots are used to denote values for two different numeric variables.
  • Bullet Graph: It is a variation of a bar graph. A bullet graph is used to swap dashboard gauges and meters.
  • Funnel Chart: The chart determines the flow of users with the help of a business or sales process.
  • Heat Map: It is a technique of data visualization that shows the level of instances as color in two dimensions.

A histogram is a graphical display of data using bars of different heights. In a histogram, each bar groups numbers into ranges. Taller bars show that more data falls in that range. A histogram displays the shape and spread of continuous sample data. 

It is a plot that lets you discover, and show, the underlying frequency distribution (shape) of a set of continuous data. This allows the inspection of the data for its underlying distribution (e.g., normal distribution), outliers, skewness, etc. It is an accurate representation of the distribution of numerical data, it relates only one variable. Includes bin or bucket- the range of values that divide the entire range of values into a series of intervals and then count how many values fall into each interval.

Bins are consecutive, non- overlapping intervals of a variable. As the adjacent bins leave no gaps, the rectangles of histogram touch each other to indicate that the original value is continuous.

In a histogram, the height of the bar does not necessarily indicate how many occurrences of scores there were within each bin. It is the product of height multiplied by the width of the bin that indicates the frequency of occurrences within that bin. One of the reasons that the height of the bars is often incorrectly assessed as indicating the frequency and not the area of the bar is because a lot of histograms often have equally spaced bars (bins), and under these circumstances, the height of the bin does reflect the frequency.

Also Read: Machine Learning Interview Questions

The major difference is that a histogram is only used to plot the frequency of score occurrences in a continuous data set that has been divided into classes, called bins. Bar charts, on the other hand, can be used for a lot of other types of variables including ordinal and nominal data sets.

A heat map is data analysis software that uses colour the way a bar graph uses height and width: as a data visualization tool. If you’re looking at a web page and you want to know which areas get the most attention, a heat map shows you in a visual way that’s easy to assimilate and make decisions from. It is a graphical representation of data where the individual values contained in a matrix are represented as colours. Useful for two purposes: for visualizing correlation tables and for visualizing missing values in the data. In both cases, the information is conveyed in a two-dimensional table. Note that heat maps are useful when examining a large number of values, but they are not a replacement for more precise graphical displays, such as bar charts, because colour differences cannot be perceived accurately.

Also Read: Top Data Mining Tools

The simplest technique, a line plot is used to plot the relationship or dependence of one variable on another. To plot the relationship between the two variables, we can simply call the plot function.

Bar charts are used for comparing the quantities of different categories or groups. Values of a category are represented with the help of bars and they can be configured with vertical or horizontal bars, with the length or height of each bar representing the value.

It is a circular statistical graph which decides slices to illustrate numerical proportion. Here the arc length of each slide is proportional to the quantity it represents. As a rule, they are used to compare the parts of a whole and are most effective when there are limited components and when text and percentages are included to describe the content. However, they can be difficult to interpret because the human eye has a hard time estimating areas and comparing visual angles.

Scatter Charts

Another common visualization technique is a scatter plot that is a two-dimensional plot representing the joint variation of two data items. Each marker (symbols such as dots, squares and plus signs) represents an observation. The marker position indicates the value for each observation. When you assign more than two measures, a scatter plot matrix is produced that is a series scatter plot displaying every possible pairing of the measures that are assigned to the visualization. Scatter plots are used for examining the relationship, or correlations, between X and Y variables.

Bubble Charts

It is a variation of scatter chart in which the data points are replaced with bubbles, and an additional dimension of data is represented in the size of the bubbles.

Timeline Charts

Timeline charts illustrate events, in chronological order — for example the progress of a project, advertising campaign, acquisition process — in whatever unit of time the data was recorded — for example week, month, year, quarter. It shows the chronological sequence of past or future events on a timescale.

A treemap is a visualization that displays hierarchically organized data as a set of nested rectangles, parent elements being tiled with their child elements. The sizes and colours of rectangles are proportional to the values of the data points they represent. A leaf node rectangle has an area proportional to the specified dimension of the data. Depending on the choice, the leaf node is coloured, sized or both according to chosen attributes. They make efficient use of space, thus display thousands of items on the screen simultaneously.

The variety of big data brings challenges because semi-structured, and unstructured data require new visualization techniques. A word cloud visual represents the frequency of a word within a body of text with its relative size in the cloud. This technique is used on unstructured data as a way to display high- or low-frequency words.

Another visualization technique that can be used for semi-structured or unstructured data is the network diagram. Network diagrams represent relationships as nodes (individual actors within the network) and ties (relationships between the individuals). They are used in many applications, for example for analysis of social networks or mapping product sales across geographic areas.

Learn all about Data Visualization with Power BI with this free course.

  • What are the techniques of Visualization?

A : The visualization techniques include Pie and Donut Charts, Histogram Plot, Scatter Plot, Kernel Density Estimation for Non-Parametric Data, Box and Whisker Plot for Large Data, Word Clouds and Network Diagrams for Unstructured Data, and Correlation Matrices.

  • What are the types of visualization?

A : The various types of visualization include Column Chart, Line Graph, Bar Graph, Stacked Bar Graph, Dual-Axis Chart, Pie Chart, Mekko Chart, Bubble Chart, Scatter Chart, and Bullet Graph.

  • What are the various visualization techniques used in data analysis?

A: Various visualization techniques are used in data analysis. A few of them include Box and Whisker Plot for Large Data, Histogram Plot, and Word Clouds and Network Diagrams for Unstructured Data, to name a few.

  • How do I start visualizing?

A: You need to have a basic understanding of data and present it without misleading the data. Once you understand it, you can further take up an online course or tutorials.

  • What are the two basic types of data visualization?

A: The two very basic types of data visualization are exploration and explanation.

  • Which is the best visualization tool?

A: Some of the best visualization tools include Visme, Tableau, Infogram, Whatagraph, Sisense, DataBox, ChartBlocks, DataWrapper, etc.

These are some of the Visualization techniques used to represent data effectively for their better understanding and interpretation. We hope this article was useful. You can also upskill with our free courses on Great Learning Academy .

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2.1: Types of Data Representation

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Two common types of graphic displays are bar charts and histograms. Both bar charts and histograms use vertical or horizontal bars to represent the number of data points in each category or interval. The main difference graphically is that in a  bar chart  there are spaces between the bars and in a  histogram  there are not spaces between the bars. Why does this subtle difference exist and what does it imply about graphic displays in general?

Displaying Data

It is often easier for people to interpret relative sizes of data when that data is displayed graphically. Note that a  categorical variable  is a variable that can take on one of a limited number of values and a  quantitative variable  is a variable that takes on numerical values that represent a measurable quantity. Examples of categorical variables are tv stations, the state someone lives in, and eye color while examples of quantitative variables are the height of students or the population of a city. There are a few common ways of displaying data graphically that you should be familiar with. 

A  pie chart  shows the relative proportions of data in different categories.  Pie charts  are excellent ways of displaying categorical data with easily separable groups. The following pie chart shows six categories labeled A−F.  The size of each pie slice is determined by the central angle. Since there are 360 o  in a circle, the size of the central angle θ A  of category A can be found by:

Screen Shot 2020-04-27 at 4.52.45 PM.png

CK-12 Foundation -  https://www.flickr.com/photos/slgc/16173880801  - CCSA

A  bar chart  displays frequencies of categories of data. The bar chart below has 5 categories, and shows the TV channel preferences for 53 adults. The horizontal axis could have also been labeled News, Sports, Local News, Comedy, Action Movies. The reason why the bars are separated by spaces is to emphasize the fact that they are categories and not continuous numbers. For example, just because you split your time between channel 8 and channel 44 does not mean on average you watch channel 26. Categories can be numbers so you need to be very careful.

Screen Shot 2020-04-27 at 4.54.15 PM.png

CK-12 Foundation -  https://www.flickr.com/photos/slgc/16173880801  - CCSA

A  histogram  displays frequencies of quantitative data that has been sorted into intervals. The following is a histogram that shows the heights of a class of 53 students. Notice the largest category is 56-60 inches with 18 people.

Screen Shot 2020-04-27 at 4.55.38 PM.png

A  boxplot  (also known as a  box and whiskers plot ) is another way to display quantitative data. It displays the five 5 number summary (minimum, Q1,  median , Q3, maximum). The box can either be vertically or horizontally displayed depending on the labeling of the axis. The box does not need to be perfectly symmetrical because it represents data that might not be perfectly symmetrical.

Screen Shot 2020-04-27 at 5.03.32 PM.png

Earlier, you were asked about the difference between histograms and bar charts. The reason for the space in bar charts but no space in histograms is bar charts graph categorical variables while histograms graph quantitative variables. It would be extremely improper to forget the space with bar charts because you would run the risk of implying a spectrum from one side of the chart to the other. Note that in the bar chart where TV stations where shown, the station numbers were not listed horizontally in order by size. This was to emphasize the fact that the stations were categories.

Create a boxplot of the following numbers in your calculator.

8.5, 10.9, 9.1, 7.5, 7.2, 6, 2.3, 5.5

Enter the data into L1 by going into the Stat menu.

Screen Shot 2020-04-27 at 5.04.34 PM.png

CK-12 Foundation - CCSA

Then turn the statplot on and choose boxplot.

Screen Shot 2020-04-27 at 5.05.07 PM.png

Use Zoomstat to automatically center the window on the boxplot.

Screen Shot 2020-04-27 at 5.05.34 PM.png

Create a pie chart to represent the preferences of 43 hungry students.

  • Other – 5
  • Burritos – 7
  • Burgers – 9
  • Pizza – 22

Screen Shot 2020-04-27 at 5.06.00 PM.png

Create a bar chart representing the preference for sports of a group of 23 people.

  • Football – 12
  • Baseball – 10
  • Basketball – 8
  • Hockey – 3

Screen Shot 2020-04-27 at 5.06.29 PM.png

Create a histogram for the income distribution of 200 million people.

  • Below $50,000 is 100 million people
  • Between $50,000 and $100,000 is 50 million people
  • Between $100,000 and $150,000 is 40 million people
  • Above $150,000 is 10 million people

Screen Shot 2020-04-27 at 5.07.15 PM.png

1. What types of graphs show categorical data?

2. What types of graphs show quantitative data?

A math class of 30 students had the following grades:

3. Create a bar chart for this data.

4. Create a pie chart for this data.

5. Which graph do you think makes a better visual representation of the data?

A set of 20 exam scores is 67, 94, 88, 76, 85, 93, 55, 87, 80, 81, 80, 61, 90, 84, 75, 93, 75, 68, 100, 98

6. Create a histogram for this data. Use your best judgment to decide what the intervals should be.

7. Find the  five number summary  for this data.

8. Use the  five number summary  to create a boxplot for this data.

9. Describe the data shown in the boxplot below.

Screen Shot 2020-04-27 at 5.11.42 PM.png

10. Describe the data shown in the histogram below.

Screen Shot 2020-04-27 at 5.12.15 PM.png

A math class of 30 students has the following eye colors:

11. Create a bar chart for this data.

12. Create a pie chart for this data.

13. Which graph do you think makes a better visual representation of the data?

14. Suppose you have data that shows the breakdown of registered republicans by state. What types of graphs could you use to display this data?

15. From which types of graphs could you obtain information about the spread of the data? Note that spread is a measure of how spread out all of the data is.

Review (Answers)

To see the Review answers, open this  PDF file  and look for section 15.4. 

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Practice: Types of Data Representation

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10 Methods of Data Presentation with 5 Great Tips to Practice, Best in 2024

Leah Nguyen • 05 April, 2024 • 17 min read

There are different ways of presenting data, so which one is suited you the most? You can end deathly boring and ineffective data presentation right now with our 10 methods of data presentation . Check out the examples from each technique!

Have you ever presented a data report to your boss/coworkers/teachers thinking it was super dope like you’re some cyber hacker living in the Matrix, but all they saw was a pile of static numbers that seemed pointless and didn’t make sense to them?

Understanding digits is rigid . Making people from non-analytical backgrounds understand those digits is even more challenging.

How can you clear up those confusing numbers in the types of presentation that have the flawless clarity of a diamond? So, let’s check out best way to present data. 💎

Table of Contents

  • What are Methods of Data Presentations?
  • #1 – Tabular

#3 – Pie chart

#4 – bar chart, #5 – histogram, #6 – line graph, #7 – pictogram graph, #8 – radar chart, #9 – heat map, #10 – scatter plot.

  • 5 Mistakes to Avoid
  • Best Method of Data Presentation

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What are Methods of Data Presentation?

The term ’data presentation’ relates to the way you present data in a way that makes even the most clueless person in the room understand. 

Some say it’s witchcraft (you’re manipulating the numbers in some ways), but we’ll just say it’s the power of turning dry, hard numbers or digits into a visual showcase that is easy for people to digest.

Presenting data correctly can help your audience understand complicated processes, identify trends, and instantly pinpoint whatever is going on without exhausting their brains.

Good data presentation helps…

  • Make informed decisions and arrive at positive outcomes . If you see the sales of your product steadily increase throughout the years, it’s best to keep milking it or start turning it into a bunch of spin-offs (shoutout to Star Wars👀).
  • Reduce the time spent processing data . Humans can digest information graphically 60,000 times faster than in the form of text. Grant them the power of skimming through a decade of data in minutes with some extra spicy graphs and charts.
  • Communicate the results clearly . Data does not lie. They’re based on factual evidence and therefore if anyone keeps whining that you might be wrong, slap them with some hard data to keep their mouths shut.
  • Add to or expand the current research . You can see what areas need improvement, as well as what details often go unnoticed while surfing through those little lines, dots or icons that appear on the data board.

Methods of Data Presentation and Examples

Imagine you have a delicious pepperoni, extra-cheese pizza. You can decide to cut it into the classic 8 triangle slices, the party style 12 square slices, or get creative and abstract on those slices. 

There are various ways for cutting a pizza and you get the same variety with how you present your data. In this section, we will bring you the 10 ways to slice a pizza – we mean to present your data – that will make your company’s most important asset as clear as day. Let’s dive into 10 ways to present data efficiently.

#1 – Tabular 

Among various types of data presentation, tabular is the most fundamental method, with data presented in rows and columns. Excel or Google Sheets would qualify for the job. Nothing fancy.

a table displaying the changes in revenue between the year 2017 and 2018 in the East, West, North, and South region

This is an example of a tabular presentation of data on Google Sheets. Each row and column has an attribute (year, region, revenue, etc.), and you can do a custom format to see the change in revenue throughout the year.

When presenting data as text, all you do is write your findings down in paragraphs and bullet points, and that’s it. A piece of cake to you, a tough nut to crack for whoever has to go through all of the reading to get to the point.

  • 65% of email users worldwide access their email via a mobile device.
  • Emails that are optimised for mobile generate 15% higher click-through rates.
  • 56% of brands using emojis in their email subject lines had a higher open rate.

(Source: CustomerThermometer )

All the above quotes present statistical information in textual form. Since not many people like going through a wall of texts, you’ll have to figure out another route when deciding to use this method, such as breaking the data down into short, clear statements, or even as catchy puns if you’ve got the time to think of them.

A pie chart (or a ‘donut chart’ if you stick a hole in the middle of it) is a circle divided into slices that show the relative sizes of data within a whole. If you’re using it to show percentages, make sure all the slices add up to 100%.

Methods of data presentation

The pie chart is a familiar face at every party and is usually recognised by most people. However, one setback of using this method is our eyes sometimes can’t identify the differences in slices of a circle, and it’s nearly impossible to compare similar slices from two different pie charts, making them the villains in the eyes of data analysts.

a half-eaten pie chart

Bonus example: A literal ‘pie’ chart! 🥧

The bar chart is a chart that presents a bunch of items from the same category, usually in the form of rectangular bars that are placed at an equal distance from each other. Their heights or lengths depict the values they represent.

They can be as simple as this:

a simple bar chart example

Or more complex and detailed like this example of presentation of data. Contributing to an effective statistic presentation, this one is a grouped bar chart that not only allows you to compare categories but also the groups within them as well.

an example of a grouped bar chart

Similar in appearance to the bar chart but the rectangular bars in histograms don’t often have the gap like their counterparts.

Instead of measuring categories like weather preferences or favourite films as a bar chart does, a histogram only measures things that can be put into numbers.

an example of a histogram chart showing the distribution of students' score for the IQ test

Teachers can use presentation graphs like a histogram to see which score group most of the students fall into, like in this example above.

Recordings to ways of displaying data, we shouldn’t overlook the effectiveness of line graphs. Line graphs are represented by a group of data points joined together by a straight line. There can be one or more lines to compare how several related things change over time. 

an example of the line graph showing the population of bears from 2017 to 2022

On a line chart’s horizontal axis, you usually have text labels, dates or years, while the vertical axis usually represents the quantity (e.g.: budget, temperature or percentage).

A pictogram graph uses pictures or icons relating to the main topic to visualise a small dataset. The fun combination of colours and illustrations makes it a frequent use at schools.

How to Create Pictographs and Icon Arrays in Visme-6 pictograph maker

Pictograms are a breath of fresh air if you want to stay away from the monotonous line chart or bar chart for a while. However, they can present a very limited amount of data and sometimes they are only there for displays and do not represent real statistics.

If presenting five or more variables in the form of a bar chart is too stuffy then you should try using a radar chart, which is one of the most creative ways to present data.

Radar charts show data in terms of how they compare to each other starting from the same point. Some also call them ‘spider charts’ because each aspect combined looks like a spider web.

a radar chart showing the text scores between two students

Radar charts can be a great use for parents who’d like to compare their child’s grades with their peers to lower their self-esteem. You can see that each angular represents a subject with a score value ranging from 0 to 100. Each student’s score across 5 subjects is highlighted in a different colour.

a radar chart showing the power distribution of a Pokemon

If you think that this method of data presentation somehow feels familiar, then you’ve probably encountered one while playing Pokémon .

A heat map represents data density in colours. The bigger the number, the more colour intense that data will be represented.

a heatmap showing the electoral votes among the states between two candidates

Most U.S citizens would be familiar with this data presentation method in geography. For elections, many news outlets assign a specific colour code to a state, with blue representing one candidate and red representing the other. The shade of either blue or red in each state shows the strength of the overall vote in that state.

a heatmap showing which parts the visitors click on in a website

Another great thing you can use a heat map for is to map what visitors to your site click on. The more a particular section is clicked the ‘hotter’ the colour will turn, from blue to bright yellow to red.

If you present your data in dots instead of chunky bars, you’ll have a scatter plot. 

A scatter plot is a grid with several inputs showing the relationship between two variables. It’s good at collecting seemingly random data and revealing some telling trends.

a scatter plot example showing the relationship between beach visitors each day and the average daily temperature

For example, in this graph, each dot shows the average daily temperature versus the number of beach visitors across several days. You can see that the dots get higher as the temperature increases, so it’s likely that hotter weather leads to more visitors.

5 Data Presentation Mistakes to Avoid

#1 – assume your audience understands what the numbers represent.

You may know all the behind-the-scenes of your data since you’ve worked with them for weeks, but your audience doesn’t.

a sales data board from Looker

Showing without telling only invites more and more questions from your audience, as they have to constantly make sense of your data, wasting the time of both sides as a result.

While showing your data presentations, you should tell them what the data are about before hitting them with waves of numbers first. You can use interactive activities such as polls , word clouds , online quiz and Q&A sections , combined with icebreaker games , to assess their understanding of the data and address any confusion beforehand.

#2 – Use the wrong type of chart

Charts such as pie charts must have a total of 100% so if your numbers accumulate to 193% like this example below, you’re definitely doing it wrong.

a bad example of using a pie chart in the 2012 presidential run

Before making a chart, ask yourself: what do I want to accomplish with my data? Do you want to see the relationship between the data sets, show the up and down trends of your data, or see how segments of one thing make up a whole?

Remember, clarity always comes first. Some data visualisations may look cool, but if they don’t fit your data, steer clear of them. 

#3 – Make it 3D

3D is a fascinating graphical presentation example. The third dimension is cool, but full of risks.

different data representation techniques

Can you see what’s behind those red bars? Because we can’t either. You may think that 3D charts add more depth to the design, but they can create false perceptions as our eyes see 3D objects closer and bigger than they appear, not to mention they cannot be seen from multiple angles.

#4 – Use different types of charts to compare contents in the same category

different data representation techniques

This is like comparing a fish to a monkey. Your audience won’t be able to identify the differences and make an appropriate correlation between the two data sets. 

Next time, stick to one type of data presentation only. Avoid the temptation of trying various data visualisation methods in one go and make your data as accessible as possible.

#5 – Bombard the audience with too much information

The goal of data presentation is to make complex topics much easier to understand, and if you’re bringing too much information to the table, you’re missing the point.

a very complicated data presentation with too much information on the screen

The more information you give, the more time it will take for your audience to process it all. If you want to make your data understandable and give your audience a chance to remember it, keep the information within it to an absolute minimum. You should set your session with open-ended questions , to avoid dead-communication!

What are the Best Methods of Data Presentation?

Finally, which is the best way to present data?

The answer is…

There is none 😄 Each type of presentation has its own strengths and weaknesses and the one you choose greatly depends on what you’re trying to do. 

For example:

  • Go for a scatter plot if you’re exploring the relationship between different data values, like seeing whether the sales of ice cream go up because of the temperature or because people are just getting more hungry and greedy each day?
  • Go for a line graph if you want to mark a trend over time. 
  • Go for a heat map if you like some fancy visualisation of the changes in a geographical location, or to see your visitors’ behaviour on your website.
  • Go for a pie chart (especially in 3D) if you want to be shunned by others because it was never a good idea👇

example of how a bad pie chart represents the data in a complicated way

What is chart presentation?

A chart presentation is a way of presenting data or information using visual aids such as charts, graphs, and diagrams. The purpose of a chart presentation is to make complex information more accessible and understandable for the audience.

When can I use charts for presentation?

Charts can be used to compare data, show trends over time, highlight patterns, and simplify complex information.

Why should use charts for presentation?

You should use charts to ensure your contents and visual look clean, as they are the visual representative, provide clarity, simplicity, comparison, contrast and super time-saving!

What are the 4 graphical methods of presenting data?

Histogram, Smoothed frequency graph, Pie diagram or Pie chart, Cumulative or ogive frequency graph, and Frequency Polygon.

Leah Nguyen

Leah Nguyen

Words that convert, stories that stick. I turn complex ideas into engaging narratives - helping audiences learn, remember, and take action.

Tips to Engage with Polls & Trivia

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Your Modern Business Guide To Data Analysis Methods And Techniques

Data analysis methods and techniques blog post by datapine

Table of Contents

1) What Is Data Analysis?

2) Why Is Data Analysis Important?

3) What Is The Data Analysis Process?

4) Types Of Data Analysis Methods

5) Top Data Analysis Techniques To Apply

6) Quality Criteria For Data Analysis

7) Data Analysis Limitations & Barriers

8) Data Analysis Skills

9) Data Analysis In The Big Data Environment

In our data-rich age, understanding how to analyze and extract true meaning from our business’s digital insights is one of the primary drivers of success.

Despite the colossal volume of data we create every day, a mere 0.5% is actually analyzed and used for data discovery , improvement, and intelligence. While that may not seem like much, considering the amount of digital information we have at our fingertips, half a percent still accounts for a vast amount of data.

With so much data and so little time, knowing how to collect, curate, organize, and make sense of all of this potentially business-boosting information can be a minefield – but online data analysis is the solution.

In science, data analysis uses a more complex approach with advanced techniques to explore and experiment with data. On the other hand, in a business context, data is used to make data-driven decisions that will enable the company to improve its overall performance. In this post, we will cover the analysis of data from an organizational point of view while still going through the scientific and statistical foundations that are fundamental to understanding the basics of data analysis. 

To put all of that into perspective, we will answer a host of important analytical questions, explore analytical methods and techniques, while demonstrating how to perform analysis in the real world with a 17-step blueprint for success.

What Is Data Analysis?

Data analysis is the process of collecting, modeling, and analyzing data using various statistical and logical methods and techniques. Businesses rely on analytics processes and tools to extract insights that support strategic and operational decision-making.

All these various methods are largely based on two core areas: quantitative and qualitative research.

To explain the key differences between qualitative and quantitative research, here’s a video for your viewing pleasure:

Gaining a better understanding of different techniques and methods in quantitative research as well as qualitative insights will give your analyzing efforts a more clearly defined direction, so it’s worth taking the time to allow this particular knowledge to sink in. Additionally, you will be able to create a comprehensive analytical report that will skyrocket your analysis.

Apart from qualitative and quantitative categories, there are also other types of data that you should be aware of before dividing into complex data analysis processes. These categories include: 

  • Big data: Refers to massive data sets that need to be analyzed using advanced software to reveal patterns and trends. It is considered to be one of the best analytical assets as it provides larger volumes of data at a faster rate. 
  • Metadata: Putting it simply, metadata is data that provides insights about other data. It summarizes key information about specific data that makes it easier to find and reuse for later purposes. 
  • Real time data: As its name suggests, real time data is presented as soon as it is acquired. From an organizational perspective, this is the most valuable data as it can help you make important decisions based on the latest developments. Our guide on real time analytics will tell you more about the topic. 
  • Machine data: This is more complex data that is generated solely by a machine such as phones, computers, or even websites and embedded systems, without previous human interaction.

Why Is Data Analysis Important?

Before we go into detail about the categories of analysis along with its methods and techniques, you must understand the potential that analyzing data can bring to your organization.

  • Informed decision-making : From a management perspective, you can benefit from analyzing your data as it helps you make decisions based on facts and not simple intuition. For instance, you can understand where to invest your capital, detect growth opportunities, predict your income, or tackle uncommon situations before they become problems. Through this, you can extract relevant insights from all areas in your organization, and with the help of dashboard software , present the data in a professional and interactive way to different stakeholders.
  • Reduce costs : Another great benefit is to reduce costs. With the help of advanced technologies such as predictive analytics, businesses can spot improvement opportunities, trends, and patterns in their data and plan their strategies accordingly. In time, this will help you save money and resources on implementing the wrong strategies. And not just that, by predicting different scenarios such as sales and demand you can also anticipate production and supply. 
  • Target customers better : Customers are arguably the most crucial element in any business. By using analytics to get a 360° vision of all aspects related to your customers, you can understand which channels they use to communicate with you, their demographics, interests, habits, purchasing behaviors, and more. In the long run, it will drive success to your marketing strategies, allow you to identify new potential customers, and avoid wasting resources on targeting the wrong people or sending the wrong message. You can also track customer satisfaction by analyzing your client’s reviews or your customer service department’s performance.

What Is The Data Analysis Process?

Data analysis process graphic

When we talk about analyzing data there is an order to follow in order to extract the needed conclusions. The analysis process consists of 5 key stages. We will cover each of them more in detail later in the post, but to start providing the needed context to understand what is coming next, here is a rundown of the 5 essential steps of data analysis. 

  • Identify: Before you get your hands dirty with data, you first need to identify why you need it in the first place. The identification is the stage in which you establish the questions you will need to answer. For example, what is the customer's perception of our brand? Or what type of packaging is more engaging to our potential customers? Once the questions are outlined you are ready for the next step. 
  • Collect: As its name suggests, this is the stage where you start collecting the needed data. Here, you define which sources of data you will use and how you will use them. The collection of data can come in different forms such as internal or external sources, surveys, interviews, questionnaires, and focus groups, among others.  An important note here is that the way you collect the data will be different in a quantitative and qualitative scenario. 
  • Clean: Once you have the necessary data it is time to clean it and leave it ready for analysis. Not all the data you collect will be useful, when collecting big amounts of data in different formats it is very likely that you will find yourself with duplicate or badly formatted data. To avoid this, before you start working with your data you need to make sure to erase any white spaces, duplicate records, or formatting errors. This way you avoid hurting your analysis with bad-quality data. 
  • Analyze : With the help of various techniques such as statistical analysis, regressions, neural networks, text analysis, and more, you can start analyzing and manipulating your data to extract relevant conclusions. At this stage, you find trends, correlations, variations, and patterns that can help you answer the questions you first thought of in the identify stage. Various technologies in the market assist researchers and average users with the management of their data. Some of them include business intelligence and visualization software, predictive analytics, and data mining, among others. 
  • Interpret: Last but not least you have one of the most important steps: it is time to interpret your results. This stage is where the researcher comes up with courses of action based on the findings. For example, here you would understand if your clients prefer packaging that is red or green, plastic or paper, etc. Additionally, at this stage, you can also find some limitations and work on them. 

Now that you have a basic understanding of the key data analysis steps, let’s look at the top 17 essential methods.

17 Essential Types Of Data Analysis Methods

Before diving into the 17 essential types of methods, it is important that we go over really fast through the main analysis categories. Starting with the category of descriptive up to prescriptive analysis, the complexity and effort of data evaluation increases, but also the added value for the company.

a) Descriptive analysis - What happened.

The descriptive analysis method is the starting point for any analytic reflection, and it aims to answer the question of what happened? It does this by ordering, manipulating, and interpreting raw data from various sources to turn it into valuable insights for your organization.

Performing descriptive analysis is essential, as it enables us to present our insights in a meaningful way. Although it is relevant to mention that this analysis on its own will not allow you to predict future outcomes or tell you the answer to questions like why something happened, it will leave your data organized and ready to conduct further investigations.

b) Exploratory analysis - How to explore data relationships.

As its name suggests, the main aim of the exploratory analysis is to explore. Prior to it, there is still no notion of the relationship between the data and the variables. Once the data is investigated, exploratory analysis helps you to find connections and generate hypotheses and solutions for specific problems. A typical area of ​​application for it is data mining.

c) Diagnostic analysis - Why it happened.

Diagnostic data analytics empowers analysts and executives by helping them gain a firm contextual understanding of why something happened. If you know why something happened as well as how it happened, you will be able to pinpoint the exact ways of tackling the issue or challenge.

Designed to provide direct and actionable answers to specific questions, this is one of the world’s most important methods in research, among its other key organizational functions such as retail analytics , e.g.

c) Predictive analysis - What will happen.

The predictive method allows you to look into the future to answer the question: what will happen? In order to do this, it uses the results of the previously mentioned descriptive, exploratory, and diagnostic analysis, in addition to machine learning (ML) and artificial intelligence (AI). Through this, you can uncover future trends, potential problems or inefficiencies, connections, and casualties in your data.

With predictive analysis, you can unfold and develop initiatives that will not only enhance your various operational processes but also help you gain an all-important edge over the competition. If you understand why a trend, pattern, or event happened through data, you will be able to develop an informed projection of how things may unfold in particular areas of the business.

e) Prescriptive analysis - How will it happen.

Another of the most effective types of analysis methods in research. Prescriptive data techniques cross over from predictive analysis in the way that it revolves around using patterns or trends to develop responsive, practical business strategies.

By drilling down into prescriptive analysis, you will play an active role in the data consumption process by taking well-arranged sets of visual data and using it as a powerful fix to emerging issues in a number of key areas, including marketing, sales, customer experience, HR, fulfillment, finance, logistics analytics , and others.

Top 17 data analysis methods

As mentioned at the beginning of the post, data analysis methods can be divided into two big categories: quantitative and qualitative. Each of these categories holds a powerful analytical value that changes depending on the scenario and type of data you are working with. Below, we will discuss 17 methods that are divided into qualitative and quantitative approaches. 

Without further ado, here are the 17 essential types of data analysis methods with some use cases in the business world: 

A. Quantitative Methods 

To put it simply, quantitative analysis refers to all methods that use numerical data or data that can be turned into numbers (e.g. category variables like gender, age, etc.) to extract valuable insights. It is used to extract valuable conclusions about relationships, differences, and test hypotheses. Below we discuss some of the key quantitative methods. 

1. Cluster analysis

The action of grouping a set of data elements in a way that said elements are more similar (in a particular sense) to each other than to those in other groups – hence the term ‘cluster.’ Since there is no target variable when clustering, the method is often used to find hidden patterns in the data. The approach is also used to provide additional context to a trend or dataset.

Let's look at it from an organizational perspective. In a perfect world, marketers would be able to analyze each customer separately and give them the best-personalized service, but let's face it, with a large customer base, it is timely impossible to do that. That's where clustering comes in. By grouping customers into clusters based on demographics, purchasing behaviors, monetary value, or any other factor that might be relevant for your company, you will be able to immediately optimize your efforts and give your customers the best experience based on their needs.

2. Cohort analysis

This type of data analysis approach uses historical data to examine and compare a determined segment of users' behavior, which can then be grouped with others with similar characteristics. By using this methodology, it's possible to gain a wealth of insight into consumer needs or a firm understanding of a broader target group.

Cohort analysis can be really useful for performing analysis in marketing as it will allow you to understand the impact of your campaigns on specific groups of customers. To exemplify, imagine you send an email campaign encouraging customers to sign up for your site. For this, you create two versions of the campaign with different designs, CTAs, and ad content. Later on, you can use cohort analysis to track the performance of the campaign for a longer period of time and understand which type of content is driving your customers to sign up, repurchase, or engage in other ways.  

A useful tool to start performing cohort analysis method is Google Analytics. You can learn more about the benefits and limitations of using cohorts in GA in this useful guide . In the bottom image, you see an example of how you visualize a cohort in this tool. The segments (devices traffic) are divided into date cohorts (usage of devices) and then analyzed week by week to extract insights into performance.

Cohort analysis chart example from google analytics

3. Regression analysis

Regression uses historical data to understand how a dependent variable's value is affected when one (linear regression) or more independent variables (multiple regression) change or stay the same. By understanding each variable's relationship and how it developed in the past, you can anticipate possible outcomes and make better decisions in the future.

Let's bring it down with an example. Imagine you did a regression analysis of your sales in 2019 and discovered that variables like product quality, store design, customer service, marketing campaigns, and sales channels affected the overall result. Now you want to use regression to analyze which of these variables changed or if any new ones appeared during 2020. For example, you couldn’t sell as much in your physical store due to COVID lockdowns. Therefore, your sales could’ve either dropped in general or increased in your online channels. Through this, you can understand which independent variables affected the overall performance of your dependent variable, annual sales.

If you want to go deeper into this type of analysis, check out this article and learn more about how you can benefit from regression.

4. Neural networks

The neural network forms the basis for the intelligent algorithms of machine learning. It is a form of analytics that attempts, with minimal intervention, to understand how the human brain would generate insights and predict values. Neural networks learn from each and every data transaction, meaning that they evolve and advance over time.

A typical area of application for neural networks is predictive analytics. There are BI reporting tools that have this feature implemented within them, such as the Predictive Analytics Tool from datapine. This tool enables users to quickly and easily generate all kinds of predictions. All you have to do is select the data to be processed based on your KPIs, and the software automatically calculates forecasts based on historical and current data. Thanks to its user-friendly interface, anyone in your organization can manage it; there’s no need to be an advanced scientist. 

Here is an example of how you can use the predictive analysis tool from datapine:

Example on how to use predictive analytics tool from datapine

**click to enlarge**

5. Factor analysis

The factor analysis also called “dimension reduction” is a type of data analysis used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables called factors. The aim here is to uncover independent latent variables, an ideal method for streamlining specific segments.

A good way to understand this data analysis method is a customer evaluation of a product. The initial assessment is based on different variables like color, shape, wearability, current trends, materials, comfort, the place where they bought the product, and frequency of usage. Like this, the list can be endless, depending on what you want to track. In this case, factor analysis comes into the picture by summarizing all of these variables into homogenous groups, for example, by grouping the variables color, materials, quality, and trends into a brother latent variable of design.

If you want to start analyzing data using factor analysis we recommend you take a look at this practical guide from UCLA.

6. Data mining

A method of data analysis that is the umbrella term for engineering metrics and insights for additional value, direction, and context. By using exploratory statistical evaluation, data mining aims to identify dependencies, relations, patterns, and trends to generate advanced knowledge.  When considering how to analyze data, adopting a data mining mindset is essential to success - as such, it’s an area that is worth exploring in greater detail.

An excellent use case of data mining is datapine intelligent data alerts . With the help of artificial intelligence and machine learning, they provide automated signals based on particular commands or occurrences within a dataset. For example, if you’re monitoring supply chain KPIs , you could set an intelligent alarm to trigger when invalid or low-quality data appears. By doing so, you will be able to drill down deep into the issue and fix it swiftly and effectively.

In the following picture, you can see how the intelligent alarms from datapine work. By setting up ranges on daily orders, sessions, and revenues, the alarms will notify you if the goal was not completed or if it exceeded expectations.

Example on how to use intelligent alerts from datapine

7. Time series analysis

As its name suggests, time series analysis is used to analyze a set of data points collected over a specified period of time. Although analysts use this method to monitor the data points in a specific interval of time rather than just monitoring them intermittently, the time series analysis is not uniquely used for the purpose of collecting data over time. Instead, it allows researchers to understand if variables changed during the duration of the study, how the different variables are dependent, and how did it reach the end result. 

In a business context, this method is used to understand the causes of different trends and patterns to extract valuable insights. Another way of using this method is with the help of time series forecasting. Powered by predictive technologies, businesses can analyze various data sets over a period of time and forecast different future events. 

A great use case to put time series analysis into perspective is seasonality effects on sales. By using time series forecasting to analyze sales data of a specific product over time, you can understand if sales rise over a specific period of time (e.g. swimwear during summertime, or candy during Halloween). These insights allow you to predict demand and prepare production accordingly.  

8. Decision Trees 

The decision tree analysis aims to act as a support tool to make smart and strategic decisions. By visually displaying potential outcomes, consequences, and costs in a tree-like model, researchers and company users can easily evaluate all factors involved and choose the best course of action. Decision trees are helpful to analyze quantitative data and they allow for an improved decision-making process by helping you spot improvement opportunities, reduce costs, and enhance operational efficiency and production.

But how does a decision tree actually works? This method works like a flowchart that starts with the main decision that you need to make and branches out based on the different outcomes and consequences of each decision. Each outcome will outline its own consequences, costs, and gains and, at the end of the analysis, you can compare each of them and make the smartest decision. 

Businesses can use them to understand which project is more cost-effective and will bring more earnings in the long run. For example, imagine you need to decide if you want to update your software app or build a new app entirely.  Here you would compare the total costs, the time needed to be invested, potential revenue, and any other factor that might affect your decision.  In the end, you would be able to see which of these two options is more realistic and attainable for your company or research.

9. Conjoint analysis 

Last but not least, we have the conjoint analysis. This approach is usually used in surveys to understand how individuals value different attributes of a product or service and it is one of the most effective methods to extract consumer preferences. When it comes to purchasing, some clients might be more price-focused, others more features-focused, and others might have a sustainable focus. Whatever your customer's preferences are, you can find them with conjoint analysis. Through this, companies can define pricing strategies, packaging options, subscription packages, and more. 

A great example of conjoint analysis is in marketing and sales. For instance, a cupcake brand might use conjoint analysis and find that its clients prefer gluten-free options and cupcakes with healthier toppings over super sugary ones. Thus, the cupcake brand can turn these insights into advertisements and promotions to increase sales of this particular type of product. And not just that, conjoint analysis can also help businesses segment their customers based on their interests. This allows them to send different messaging that will bring value to each of the segments. 

10. Correspondence Analysis

Also known as reciprocal averaging, correspondence analysis is a method used to analyze the relationship between categorical variables presented within a contingency table. A contingency table is a table that displays two (simple correspondence analysis) or more (multiple correspondence analysis) categorical variables across rows and columns that show the distribution of the data, which is usually answers to a survey or questionnaire on a specific topic. 

This method starts by calculating an “expected value” which is done by multiplying row and column averages and dividing it by the overall original value of the specific table cell. The “expected value” is then subtracted from the original value resulting in a “residual number” which is what allows you to extract conclusions about relationships and distribution. The results of this analysis are later displayed using a map that represents the relationship between the different values. The closest two values are in the map, the bigger the relationship. Let’s put it into perspective with an example. 

Imagine you are carrying out a market research analysis about outdoor clothing brands and how they are perceived by the public. For this analysis, you ask a group of people to match each brand with a certain attribute which can be durability, innovation, quality materials, etc. When calculating the residual numbers, you can see that brand A has a positive residual for innovation but a negative one for durability. This means that brand A is not positioned as a durable brand in the market, something that competitors could take advantage of. 

11. Multidimensional Scaling (MDS)

MDS is a method used to observe the similarities or disparities between objects which can be colors, brands, people, geographical coordinates, and more. The objects are plotted using an “MDS map” that positions similar objects together and disparate ones far apart. The (dis) similarities between objects are represented using one or more dimensions that can be observed using a numerical scale. For example, if you want to know how people feel about the COVID-19 vaccine, you can use 1 for “don’t believe in the vaccine at all”  and 10 for “firmly believe in the vaccine” and a scale of 2 to 9 for in between responses.  When analyzing an MDS map the only thing that matters is the distance between the objects, the orientation of the dimensions is arbitrary and has no meaning at all. 

Multidimensional scaling is a valuable technique for market research, especially when it comes to evaluating product or brand positioning. For instance, if a cupcake brand wants to know how they are positioned compared to competitors, it can define 2-3 dimensions such as taste, ingredients, shopping experience, or more, and do a multidimensional scaling analysis to find improvement opportunities as well as areas in which competitors are currently leading. 

Another business example is in procurement when deciding on different suppliers. Decision makers can generate an MDS map to see how the different prices, delivery times, technical services, and more of the different suppliers differ and pick the one that suits their needs the best. 

A final example proposed by a research paper on "An Improved Study of Multilevel Semantic Network Visualization for Analyzing Sentiment Word of Movie Review Data". Researchers picked a two-dimensional MDS map to display the distances and relationships between different sentiments in movie reviews. They used 36 sentiment words and distributed them based on their emotional distance as we can see in the image below where the words "outraged" and "sweet" are on opposite sides of the map, marking the distance between the two emotions very clearly.

Example of multidimensional scaling analysis

Aside from being a valuable technique to analyze dissimilarities, MDS also serves as a dimension-reduction technique for large dimensional data. 

B. Qualitative Methods

Qualitative data analysis methods are defined as the observation of non-numerical data that is gathered and produced using methods of observation such as interviews, focus groups, questionnaires, and more. As opposed to quantitative methods, qualitative data is more subjective and highly valuable in analyzing customer retention and product development.

12. Text analysis

Text analysis, also known in the industry as text mining, works by taking large sets of textual data and arranging them in a way that makes it easier to manage. By working through this cleansing process in stringent detail, you will be able to extract the data that is truly relevant to your organization and use it to develop actionable insights that will propel you forward.

Modern software accelerate the application of text analytics. Thanks to the combination of machine learning and intelligent algorithms, you can perform advanced analytical processes such as sentiment analysis. This technique allows you to understand the intentions and emotions of a text, for example, if it's positive, negative, or neutral, and then give it a score depending on certain factors and categories that are relevant to your brand. Sentiment analysis is often used to monitor brand and product reputation and to understand how successful your customer experience is. To learn more about the topic check out this insightful article .

By analyzing data from various word-based sources, including product reviews, articles, social media communications, and survey responses, you will gain invaluable insights into your audience, as well as their needs, preferences, and pain points. This will allow you to create campaigns, services, and communications that meet your prospects’ needs on a personal level, growing your audience while boosting customer retention. There are various other “sub-methods” that are an extension of text analysis. Each of them serves a more specific purpose and we will look at them in detail next. 

13. Content Analysis

This is a straightforward and very popular method that examines the presence and frequency of certain words, concepts, and subjects in different content formats such as text, image, audio, or video. For example, the number of times the name of a celebrity is mentioned on social media or online tabloids. It does this by coding text data that is later categorized and tabulated in a way that can provide valuable insights, making it the perfect mix of quantitative and qualitative analysis.

There are two types of content analysis. The first one is the conceptual analysis which focuses on explicit data, for instance, the number of times a concept or word is mentioned in a piece of content. The second one is relational analysis, which focuses on the relationship between different concepts or words and how they are connected within a specific context. 

Content analysis is often used by marketers to measure brand reputation and customer behavior. For example, by analyzing customer reviews. It can also be used to analyze customer interviews and find directions for new product development. It is also important to note, that in order to extract the maximum potential out of this analysis method, it is necessary to have a clearly defined research question. 

14. Thematic Analysis

Very similar to content analysis, thematic analysis also helps in identifying and interpreting patterns in qualitative data with the main difference being that the first one can also be applied to quantitative analysis. The thematic method analyzes large pieces of text data such as focus group transcripts or interviews and groups them into themes or categories that come up frequently within the text. It is a great method when trying to figure out peoples view’s and opinions about a certain topic. For example, if you are a brand that cares about sustainability, you can do a survey of your customers to analyze their views and opinions about sustainability and how they apply it to their lives. You can also analyze customer service calls transcripts to find common issues and improve your service. 

Thematic analysis is a very subjective technique that relies on the researcher’s judgment. Therefore,  to avoid biases, it has 6 steps that include familiarization, coding, generating themes, reviewing themes, defining and naming themes, and writing up. It is also important to note that, because it is a flexible approach, the data can be interpreted in multiple ways and it can be hard to select what data is more important to emphasize. 

15. Narrative Analysis 

A bit more complex in nature than the two previous ones, narrative analysis is used to explore the meaning behind the stories that people tell and most importantly, how they tell them. By looking into the words that people use to describe a situation you can extract valuable conclusions about their perspective on a specific topic. Common sources for narrative data include autobiographies, family stories, opinion pieces, and testimonials, among others. 

From a business perspective, narrative analysis can be useful to analyze customer behaviors and feelings towards a specific product, service, feature, or others. It provides unique and deep insights that can be extremely valuable. However, it has some drawbacks.  

The biggest weakness of this method is that the sample sizes are usually very small due to the complexity and time-consuming nature of the collection of narrative data. Plus, the way a subject tells a story will be significantly influenced by his or her specific experiences, making it very hard to replicate in a subsequent study. 

16. Discourse Analysis

Discourse analysis is used to understand the meaning behind any type of written, verbal, or symbolic discourse based on its political, social, or cultural context. It mixes the analysis of languages and situations together. This means that the way the content is constructed and the meaning behind it is significantly influenced by the culture and society it takes place in. For example, if you are analyzing political speeches you need to consider different context elements such as the politician's background, the current political context of the country, the audience to which the speech is directed, and so on. 

From a business point of view, discourse analysis is a great market research tool. It allows marketers to understand how the norms and ideas of the specific market work and how their customers relate to those ideas. It can be very useful to build a brand mission or develop a unique tone of voice. 

17. Grounded Theory Analysis

Traditionally, researchers decide on a method and hypothesis and start to collect the data to prove that hypothesis. The grounded theory is the only method that doesn’t require an initial research question or hypothesis as its value lies in the generation of new theories. With the grounded theory method, you can go into the analysis process with an open mind and explore the data to generate new theories through tests and revisions. In fact, it is not necessary to collect the data and then start to analyze it. Researchers usually start to find valuable insights as they are gathering the data. 

All of these elements make grounded theory a very valuable method as theories are fully backed by data instead of initial assumptions. It is a great technique to analyze poorly researched topics or find the causes behind specific company outcomes. For example, product managers and marketers might use the grounded theory to find the causes of high levels of customer churn and look into customer surveys and reviews to develop new theories about the causes. 

How To Analyze Data? Top 17 Data Analysis Techniques To Apply

17 top data analysis techniques by datapine

Now that we’ve answered the questions “what is data analysis’”, why is it important, and covered the different data analysis types, it’s time to dig deeper into how to perform your analysis by working through these 17 essential techniques.

1. Collaborate your needs

Before you begin analyzing or drilling down into any techniques, it’s crucial to sit down collaboratively with all key stakeholders within your organization, decide on your primary campaign or strategic goals, and gain a fundamental understanding of the types of insights that will best benefit your progress or provide you with the level of vision you need to evolve your organization.

2. Establish your questions

Once you’ve outlined your core objectives, you should consider which questions will need answering to help you achieve your mission. This is one of the most important techniques as it will shape the very foundations of your success.

To help you ask the right things and ensure your data works for you, you have to ask the right data analysis questions .

3. Data democratization

After giving your data analytics methodology some real direction, and knowing which questions need answering to extract optimum value from the information available to your organization, you should continue with democratization.

Data democratization is an action that aims to connect data from various sources efficiently and quickly so that anyone in your organization can access it at any given moment. You can extract data in text, images, videos, numbers, or any other format. And then perform cross-database analysis to achieve more advanced insights to share with the rest of the company interactively.  

Once you have decided on your most valuable sources, you need to take all of this into a structured format to start collecting your insights. For this purpose, datapine offers an easy all-in-one data connectors feature to integrate all your internal and external sources and manage them at your will. Additionally, datapine’s end-to-end solution automatically updates your data, allowing you to save time and focus on performing the right analysis to grow your company.

data connectors from datapine

4. Think of governance 

When collecting data in a business or research context you always need to think about security and privacy. With data breaches becoming a topic of concern for businesses, the need to protect your client's or subject’s sensitive information becomes critical. 

To ensure that all this is taken care of, you need to think of a data governance strategy. According to Gartner , this concept refers to “ the specification of decision rights and an accountability framework to ensure the appropriate behavior in the valuation, creation, consumption, and control of data and analytics .” In simpler words, data governance is a collection of processes, roles, and policies, that ensure the efficient use of data while still achieving the main company goals. It ensures that clear roles are in place for who can access the information and how they can access it. In time, this not only ensures that sensitive information is protected but also allows for an efficient analysis as a whole. 

5. Clean your data

After harvesting from so many sources you will be left with a vast amount of information that can be overwhelming to deal with. At the same time, you can be faced with incorrect data that can be misleading to your analysis. The smartest thing you can do to avoid dealing with this in the future is to clean the data. This is fundamental before visualizing it, as it will ensure that the insights you extract from it are correct.

There are many things that you need to look for in the cleaning process. The most important one is to eliminate any duplicate observations; this usually appears when using multiple internal and external sources of information. You can also add any missing codes, fix empty fields, and eliminate incorrectly formatted data.

Another usual form of cleaning is done with text data. As we mentioned earlier, most companies today analyze customer reviews, social media comments, questionnaires, and several other text inputs. In order for algorithms to detect patterns, text data needs to be revised to avoid invalid characters or any syntax or spelling errors. 

Most importantly, the aim of cleaning is to prevent you from arriving at false conclusions that can damage your company in the long run. By using clean data, you will also help BI solutions to interact better with your information and create better reports for your organization.

6. Set your KPIs

Once you’ve set your sources, cleaned your data, and established clear-cut questions you want your insights to answer, you need to set a host of key performance indicators (KPIs) that will help you track, measure, and shape your progress in a number of key areas.

KPIs are critical to both qualitative and quantitative analysis research. This is one of the primary methods of data analysis you certainly shouldn’t overlook.

To help you set the best possible KPIs for your initiatives and activities, here is an example of a relevant logistics KPI : transportation-related costs. If you want to see more go explore our collection of key performance indicator examples .

Transportation costs logistics KPIs

7. Omit useless data

Having bestowed your data analysis tools and techniques with true purpose and defined your mission, you should explore the raw data you’ve collected from all sources and use your KPIs as a reference for chopping out any information you deem to be useless.

Trimming the informational fat is one of the most crucial methods of analysis as it will allow you to focus your analytical efforts and squeeze every drop of value from the remaining ‘lean’ information.

Any stats, facts, figures, or metrics that don’t align with your business goals or fit with your KPI management strategies should be eliminated from the equation.

8. Build a data management roadmap

While, at this point, this particular step is optional (you will have already gained a wealth of insight and formed a fairly sound strategy by now), creating a data governance roadmap will help your data analysis methods and techniques become successful on a more sustainable basis. These roadmaps, if developed properly, are also built so they can be tweaked and scaled over time.

Invest ample time in developing a roadmap that will help you store, manage, and handle your data internally, and you will make your analysis techniques all the more fluid and functional – one of the most powerful types of data analysis methods available today.

9. Integrate technology

There are many ways to analyze data, but one of the most vital aspects of analytical success in a business context is integrating the right decision support software and technology.

Robust analysis platforms will not only allow you to pull critical data from your most valuable sources while working with dynamic KPIs that will offer you actionable insights; it will also present them in a digestible, visual, interactive format from one central, live dashboard . A data methodology you can count on.

By integrating the right technology within your data analysis methodology, you’ll avoid fragmenting your insights, saving you time and effort while allowing you to enjoy the maximum value from your business’s most valuable insights.

For a look at the power of software for the purpose of analysis and to enhance your methods of analyzing, glance over our selection of dashboard examples .

10. Answer your questions

By considering each of the above efforts, working with the right technology, and fostering a cohesive internal culture where everyone buys into the different ways to analyze data as well as the power of digital intelligence, you will swiftly start to answer your most burning business questions. Arguably, the best way to make your data concepts accessible across the organization is through data visualization.

11. Visualize your data

Online data visualization is a powerful tool as it lets you tell a story with your metrics, allowing users across the organization to extract meaningful insights that aid business evolution – and it covers all the different ways to analyze data.

The purpose of analyzing is to make your entire organization more informed and intelligent, and with the right platform or dashboard, this is simpler than you think, as demonstrated by our marketing dashboard .

An executive dashboard example showcasing high-level marketing KPIs such as cost per lead, MQL, SQL, and cost per customer.

This visual, dynamic, and interactive online dashboard is a data analysis example designed to give Chief Marketing Officers (CMO) an overview of relevant metrics to help them understand if they achieved their monthly goals.

In detail, this example generated with a modern dashboard creator displays interactive charts for monthly revenues, costs, net income, and net income per customer; all of them are compared with the previous month so that you can understand how the data fluctuated. In addition, it shows a detailed summary of the number of users, customers, SQLs, and MQLs per month to visualize the whole picture and extract relevant insights or trends for your marketing reports .

The CMO dashboard is perfect for c-level management as it can help them monitor the strategic outcome of their marketing efforts and make data-driven decisions that can benefit the company exponentially.

12. Be careful with the interpretation

We already dedicated an entire post to data interpretation as it is a fundamental part of the process of data analysis. It gives meaning to the analytical information and aims to drive a concise conclusion from the analysis results. Since most of the time companies are dealing with data from many different sources, the interpretation stage needs to be done carefully and properly in order to avoid misinterpretations. 

To help you through the process, here we list three common practices that you need to avoid at all costs when looking at your data:

  • Correlation vs. causation: The human brain is formatted to find patterns. This behavior leads to one of the most common mistakes when performing interpretation: confusing correlation with causation. Although these two aspects can exist simultaneously, it is not correct to assume that because two things happened together, one provoked the other. A piece of advice to avoid falling into this mistake is never to trust just intuition, trust the data. If there is no objective evidence of causation, then always stick to correlation. 
  • Confirmation bias: This phenomenon describes the tendency to select and interpret only the data necessary to prove one hypothesis, often ignoring the elements that might disprove it. Even if it's not done on purpose, confirmation bias can represent a real problem, as excluding relevant information can lead to false conclusions and, therefore, bad business decisions. To avoid it, always try to disprove your hypothesis instead of proving it, share your analysis with other team members, and avoid drawing any conclusions before the entire analytical project is finalized.
  • Statistical significance: To put it in short words, statistical significance helps analysts understand if a result is actually accurate or if it happened because of a sampling error or pure chance. The level of statistical significance needed might depend on the sample size and the industry being analyzed. In any case, ignoring the significance of a result when it might influence decision-making can be a huge mistake.

13. Build a narrative

Now, we’re going to look at how you can bring all of these elements together in a way that will benefit your business - starting with a little something called data storytelling.

The human brain responds incredibly well to strong stories or narratives. Once you’ve cleansed, shaped, and visualized your most invaluable data using various BI dashboard tools , you should strive to tell a story - one with a clear-cut beginning, middle, and end.

By doing so, you will make your analytical efforts more accessible, digestible, and universal, empowering more people within your organization to use your discoveries to their actionable advantage.

14. Consider autonomous technology

Autonomous technologies, such as artificial intelligence (AI) and machine learning (ML), play a significant role in the advancement of understanding how to analyze data more effectively.

Gartner predicts that by the end of this year, 80% of emerging technologies will be developed with AI foundations. This is a testament to the ever-growing power and value of autonomous technologies.

At the moment, these technologies are revolutionizing the analysis industry. Some examples that we mentioned earlier are neural networks, intelligent alarms, and sentiment analysis.

15. Share the load

If you work with the right tools and dashboards, you will be able to present your metrics in a digestible, value-driven format, allowing almost everyone in the organization to connect with and use relevant data to their advantage.

Modern dashboards consolidate data from various sources, providing access to a wealth of insights in one centralized location, no matter if you need to monitor recruitment metrics or generate reports that need to be sent across numerous departments. Moreover, these cutting-edge tools offer access to dashboards from a multitude of devices, meaning that everyone within the business can connect with practical insights remotely - and share the load.

Once everyone is able to work with a data-driven mindset, you will catalyze the success of your business in ways you never thought possible. And when it comes to knowing how to analyze data, this kind of collaborative approach is essential.

16. Data analysis tools

In order to perform high-quality analysis of data, it is fundamental to use tools and software that will ensure the best results. Here we leave you a small summary of four fundamental categories of data analysis tools for your organization.

  • Business Intelligence: BI tools allow you to process significant amounts of data from several sources in any format. Through this, you can not only analyze and monitor your data to extract relevant insights but also create interactive reports and dashboards to visualize your KPIs and use them for your company's good. datapine is an amazing online BI software that is focused on delivering powerful online analysis features that are accessible to beginner and advanced users. Like this, it offers a full-service solution that includes cutting-edge analysis of data, KPIs visualization, live dashboards, reporting, and artificial intelligence technologies to predict trends and minimize risk.
  • Statistical analysis: These tools are usually designed for scientists, statisticians, market researchers, and mathematicians, as they allow them to perform complex statistical analyses with methods like regression analysis, predictive analysis, and statistical modeling. A good tool to perform this type of analysis is R-Studio as it offers a powerful data modeling and hypothesis testing feature that can cover both academic and general data analysis. This tool is one of the favorite ones in the industry, due to its capability for data cleaning, data reduction, and performing advanced analysis with several statistical methods. Another relevant tool to mention is SPSS from IBM. The software offers advanced statistical analysis for users of all skill levels. Thanks to a vast library of machine learning algorithms, text analysis, and a hypothesis testing approach it can help your company find relevant insights to drive better decisions. SPSS also works as a cloud service that enables you to run it anywhere.
  • SQL Consoles: SQL is a programming language often used to handle structured data in relational databases. Tools like these are popular among data scientists as they are extremely effective in unlocking these databases' value. Undoubtedly, one of the most used SQL software in the market is MySQL Workbench . This tool offers several features such as a visual tool for database modeling and monitoring, complete SQL optimization, administration tools, and visual performance dashboards to keep track of KPIs.
  • Data Visualization: These tools are used to represent your data through charts, graphs, and maps that allow you to find patterns and trends in the data. datapine's already mentioned BI platform also offers a wealth of powerful online data visualization tools with several benefits. Some of them include: delivering compelling data-driven presentations to share with your entire company, the ability to see your data online with any device wherever you are, an interactive dashboard design feature that enables you to showcase your results in an interactive and understandable way, and to perform online self-service reports that can be used simultaneously with several other people to enhance team productivity.

17. Refine your process constantly 

Last is a step that might seem obvious to some people, but it can be easily ignored if you think you are done. Once you have extracted the needed results, you should always take a retrospective look at your project and think about what you can improve. As you saw throughout this long list of techniques, data analysis is a complex process that requires constant refinement. For this reason, you should always go one step further and keep improving. 

Quality Criteria For Data Analysis

So far we’ve covered a list of methods and techniques that should help you perform efficient data analysis. But how do you measure the quality and validity of your results? This is done with the help of some science quality criteria. Here we will go into a more theoretical area that is critical to understanding the fundamentals of statistical analysis in science. However, you should also be aware of these steps in a business context, as they will allow you to assess the quality of your results in the correct way. Let’s dig in. 

  • Internal validity: The results of a survey are internally valid if they measure what they are supposed to measure and thus provide credible results. In other words , internal validity measures the trustworthiness of the results and how they can be affected by factors such as the research design, operational definitions, how the variables are measured, and more. For instance, imagine you are doing an interview to ask people if they brush their teeth two times a day. While most of them will answer yes, you can still notice that their answers correspond to what is socially acceptable, which is to brush your teeth at least twice a day. In this case, you can’t be 100% sure if respondents actually brush their teeth twice a day or if they just say that they do, therefore, the internal validity of this interview is very low. 
  • External validity: Essentially, external validity refers to the extent to which the results of your research can be applied to a broader context. It basically aims to prove that the findings of a study can be applied in the real world. If the research can be applied to other settings, individuals, and times, then the external validity is high. 
  • Reliability : If your research is reliable, it means that it can be reproduced. If your measurement were repeated under the same conditions, it would produce similar results. This means that your measuring instrument consistently produces reliable results. For example, imagine a doctor building a symptoms questionnaire to detect a specific disease in a patient. Then, various other doctors use this questionnaire but end up diagnosing the same patient with a different condition. This means the questionnaire is not reliable in detecting the initial disease. Another important note here is that in order for your research to be reliable, it also needs to be objective. If the results of a study are the same, independent of who assesses them or interprets them, the study can be considered reliable. Let’s see the objectivity criteria in more detail now. 
  • Objectivity: In data science, objectivity means that the researcher needs to stay fully objective when it comes to its analysis. The results of a study need to be affected by objective criteria and not by the beliefs, personality, or values of the researcher. Objectivity needs to be ensured when you are gathering the data, for example, when interviewing individuals, the questions need to be asked in a way that doesn't influence the results. Paired with this, objectivity also needs to be thought of when interpreting the data. If different researchers reach the same conclusions, then the study is objective. For this last point, you can set predefined criteria to interpret the results to ensure all researchers follow the same steps. 

The discussed quality criteria cover mostly potential influences in a quantitative context. Analysis in qualitative research has by default additional subjective influences that must be controlled in a different way. Therefore, there are other quality criteria for this kind of research such as credibility, transferability, dependability, and confirmability. You can see each of them more in detail on this resource . 

Data Analysis Limitations & Barriers

Analyzing data is not an easy task. As you’ve seen throughout this post, there are many steps and techniques that you need to apply in order to extract useful information from your research. While a well-performed analysis can bring various benefits to your organization it doesn't come without limitations. In this section, we will discuss some of the main barriers you might encounter when conducting an analysis. Let’s see them more in detail. 

  • Lack of clear goals: No matter how good your data or analysis might be if you don’t have clear goals or a hypothesis the process might be worthless. While we mentioned some methods that don’t require a predefined hypothesis, it is always better to enter the analytical process with some clear guidelines of what you are expecting to get out of it, especially in a business context in which data is utilized to support important strategic decisions. 
  • Objectivity: Arguably one of the biggest barriers when it comes to data analysis in research is to stay objective. When trying to prove a hypothesis, researchers might find themselves, intentionally or unintentionally, directing the results toward an outcome that they want. To avoid this, always question your assumptions and avoid confusing facts with opinions. You can also show your findings to a research partner or external person to confirm that your results are objective. 
  • Data representation: A fundamental part of the analytical procedure is the way you represent your data. You can use various graphs and charts to represent your findings, but not all of them will work for all purposes. Choosing the wrong visual can not only damage your analysis but can mislead your audience, therefore, it is important to understand when to use each type of data depending on your analytical goals. Our complete guide on the types of graphs and charts lists 20 different visuals with examples of when to use them. 
  • Flawed correlation : Misleading statistics can significantly damage your research. We’ve already pointed out a few interpretation issues previously in the post, but it is an important barrier that we can't avoid addressing here as well. Flawed correlations occur when two variables appear related to each other but they are not. Confusing correlations with causation can lead to a wrong interpretation of results which can lead to building wrong strategies and loss of resources, therefore, it is very important to identify the different interpretation mistakes and avoid them. 
  • Sample size: A very common barrier to a reliable and efficient analysis process is the sample size. In order for the results to be trustworthy, the sample size should be representative of what you are analyzing. For example, imagine you have a company of 1000 employees and you ask the question “do you like working here?” to 50 employees of which 49 say yes, which means 95%. Now, imagine you ask the same question to the 1000 employees and 950 say yes, which also means 95%. Saying that 95% of employees like working in the company when the sample size was only 50 is not a representative or trustworthy conclusion. The significance of the results is way more accurate when surveying a bigger sample size.   
  • Privacy concerns: In some cases, data collection can be subjected to privacy regulations. Businesses gather all kinds of information from their customers from purchasing behaviors to addresses and phone numbers. If this falls into the wrong hands due to a breach, it can affect the security and confidentiality of your clients. To avoid this issue, you need to collect only the data that is needed for your research and, if you are using sensitive facts, make it anonymous so customers are protected. The misuse of customer data can severely damage a business's reputation, so it is important to keep an eye on privacy. 
  • Lack of communication between teams : When it comes to performing data analysis on a business level, it is very likely that each department and team will have different goals and strategies. However, they are all working for the same common goal of helping the business run smoothly and keep growing. When teams are not connected and communicating with each other, it can directly affect the way general strategies are built. To avoid these issues, tools such as data dashboards enable teams to stay connected through data in a visually appealing way. 
  • Innumeracy : Businesses are working with data more and more every day. While there are many BI tools available to perform effective analysis, data literacy is still a constant barrier. Not all employees know how to apply analysis techniques or extract insights from them. To prevent this from happening, you can implement different training opportunities that will prepare every relevant user to deal with data. 

Key Data Analysis Skills

As you've learned throughout this lengthy guide, analyzing data is a complex task that requires a lot of knowledge and skills. That said, thanks to the rise of self-service tools the process is way more accessible and agile than it once was. Regardless, there are still some key skills that are valuable to have when working with data, we list the most important ones below.

  • Critical and statistical thinking: To successfully analyze data you need to be creative and think out of the box. Yes, that might sound like a weird statement considering that data is often tight to facts. However, a great level of critical thinking is required to uncover connections, come up with a valuable hypothesis, and extract conclusions that go a step further from the surface. This, of course, needs to be complemented by statistical thinking and an understanding of numbers. 
  • Data cleaning: Anyone who has ever worked with data before will tell you that the cleaning and preparation process accounts for 80% of a data analyst's work, therefore, the skill is fundamental. But not just that, not cleaning the data adequately can also significantly damage the analysis which can lead to poor decision-making in a business scenario. While there are multiple tools that automate the cleaning process and eliminate the possibility of human error, it is still a valuable skill to dominate. 
  • Data visualization: Visuals make the information easier to understand and analyze, not only for professional users but especially for non-technical ones. Having the necessary skills to not only choose the right chart type but know when to apply it correctly is key. This also means being able to design visually compelling charts that make the data exploration process more efficient. 
  • SQL: The Structured Query Language or SQL is a programming language used to communicate with databases. It is fundamental knowledge as it enables you to update, manipulate, and organize data from relational databases which are the most common databases used by companies. It is fairly easy to learn and one of the most valuable skills when it comes to data analysis. 
  • Communication skills: This is a skill that is especially valuable in a business environment. Being able to clearly communicate analytical outcomes to colleagues is incredibly important, especially when the information you are trying to convey is complex for non-technical people. This applies to in-person communication as well as written format, for example, when generating a dashboard or report. While this might be considered a “soft” skill compared to the other ones we mentioned, it should not be ignored as you most likely will need to share analytical findings with others no matter the context. 

Data Analysis In The Big Data Environment

Big data is invaluable to today’s businesses, and by using different methods for data analysis, it’s possible to view your data in a way that can help you turn insight into positive action.

To inspire your efforts and put the importance of big data into context, here are some insights that you should know:

  • By 2026 the industry of big data is expected to be worth approximately $273.4 billion.
  • 94% of enterprises say that analyzing data is important for their growth and digital transformation. 
  • Companies that exploit the full potential of their data can increase their operating margins by 60% .
  • We already told you the benefits of Artificial Intelligence through this article. This industry's financial impact is expected to grow up to $40 billion by 2025.

Data analysis concepts may come in many forms, but fundamentally, any solid methodology will help to make your business more streamlined, cohesive, insightful, and successful than ever before.

Key Takeaways From Data Analysis 

As we reach the end of our data analysis journey, we leave a small summary of the main methods and techniques to perform excellent analysis and grow your business.

17 Essential Types of Data Analysis Methods:

  • Cluster analysis
  • Cohort analysis
  • Regression analysis
  • Factor analysis
  • Neural Networks
  • Data Mining
  • Text analysis
  • Time series analysis
  • Decision trees
  • Conjoint analysis 
  • Correspondence Analysis
  • Multidimensional Scaling 
  • Content analysis 
  • Thematic analysis
  • Narrative analysis 
  • Grounded theory analysis
  • Discourse analysis 

Top 17 Data Analysis Techniques:

  • Collaborate your needs
  • Establish your questions
  • Data democratization
  • Think of data governance 
  • Clean your data
  • Set your KPIs
  • Omit useless data
  • Build a data management roadmap
  • Integrate technology
  • Answer your questions
  • Visualize your data
  • Interpretation of data
  • Consider autonomous technology
  • Build a narrative
  • Share the load
  • Data Analysis tools
  • Refine your process constantly 

We’ve pondered the data analysis definition and drilled down into the practical applications of data-centric analytics, and one thing is clear: by taking measures to arrange your data and making your metrics work for you, it’s possible to transform raw information into action - the kind of that will push your business to the next level.

Yes, good data analytics techniques result in enhanced business intelligence (BI). To help you understand this notion in more detail, read our exploration of business intelligence reporting .

And, if you’re ready to perform your own analysis, drill down into your facts and figures while interacting with your data on astonishing visuals, you can try our software for a free, 14-day trial .

different data representation techniques

  • How to Use Data Visualization Tools and Techniques for Business

different data representation techniques

Sep 29, 2023

data visualization

The ever-growing volume of data and its importance for business make data visualization an essential part of business strategy for many companies.

In this article, we review major data visualization instruments and name the key factors that influence the choice of visualization techniques and tools. You will learn about the most widely used tools for data visualization and get a few expert tips on how to combine data visualizations into effective dashboards. We will also show how the adoption of these tools can affect business outcomes and give several real-world examples from our experience.

What determines data visualization choices

Visualization is the first step to make sense of data. To translate and present complex data and relations in a simple way, data analysts use different methods of data visualization — charts, diagrams, maps, etc. Choosing the right technique and its setup is often the only way to make data understandable. Vice versa, poorly selected tactics won't let you unlock the full potential of data or even make it irrelevant.

5 factors that impact your choice of data visualization methods and techniques:

different data representation techniques

  • Audience. It’s important to adjust data representation to the specific target audience. For example, fitness mobile app users who browse through their progress would prefer easy-to-read uncomplicated visualizations on their phones. On the other hand, if data insights are intended for researchers, specialists and C-level decision-makers who regularly work with data, you can and often have to go beyond simple charts.
  • Content. The type of data you are dealing with will determine the tactics. For example, if it’s time-series metrics, you will use line charts to show the dynamics in many cases. To show the relationship between two elements, scatter plots are often used. In turn, bar charts work well for comparative analysis.
  • Context. You can use different data visualization approaches and read data depending on the context. To emphasize a certain figure, for example, significant profit growth, you can use the shades of one color on the chart and highlight the highest value with the brightest one. On the contrary, to differentiate elements, you can use contrast colors.
  • Dynamics. There are various types of data, and each type has a different rate of change. For example, financial results can be measured monthly or yearly, while time series and tracking data are changing constantly. Depending on the rate of change, you may consider dynamic representation (steaming) or static data visualization techniques in data mining.
  • Purpose. The goal of data visualization affects the way it is implemented. In order to make a complex analysis, visualizations are compiled into dynamic and controllable dashboards equipped with different tools for visual data analytics (comparison, formatting, filtering, etc.). However, dashboards are not necessary to show a single or occasional data insight.

Are you looking for a skillful team to create effective and responsive data visualization and dashboards to deliver important insights for your business? Contact our team and tell us about your needs and requirements. Our analysts, developers and data scientists have profound experience in working with different types of data and will find a way to help you get the most of your data assets.

Cut time to insight to make decisions faster

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Data visualization techniques

Depending on these factors, you can choose different data visualization techniques and configure their features. Here are the common types of data visualization techniques:

The easiest way to show the development of one or several data sets is a chart. Charts vary from bar and line charts that show the relationship between elements over time to pie charts that demonstrate the components or proportions between the elements of one whole.

different data representation techniques

Plots allow to distribute two or more data sets over a 2D or 3D space to show the relationship between these sets and the parameters on the plot. Plots also vary. Scatter and bubble plots are some of the most widely used visualizations. When it comes to big data, analysts often use more complex box plots to visualize the relationships between large volumes of data.

different data representation techniques

Maps are popular techniques used for data visualization in different industries. They allow locating elements on relevant objects and areas — geographical maps, building plans, website layouts, etc. Among the most popular map visualizations are heatmaps, dot distribution maps, and cartograms.

different data representation techniques

Diagrams and matrices

Diagrams are usually used to demonstrate complex data relationships and links and include various types of data in one visual representation. They can be hierarchical, multidimensional, or tree-like.

Matrix is one of the advanced data visualization techniques that help determine the correlation between multiple constantly updating (steaming) data sets.

different data representation techniques

Image credit: duke.edu

Data visualization tools

Together with the demand for data visualization and analysis, the tools and solutions in this area develop fast and extensively. Novel 3D visualizations, immersive experiences and shared VR offices are getting common alongside traditional web and desktop interfaces. Here are three categories of data visualization technologies and tools for different types of users and purposes.

Data visualization tools for everyone

Tableau is one of the leaders in this field. Startups and global conglomerates like Verizon and Henkel rely on this platform to derive meaning from data and use insights for effective decision making.

Apart from a user-friendly interface and a rich library of interactive visualizations and data representation techniques, Tableau stands out for its powerful capabilities. The platform provides diverse integration options with various data storage, management, and infrastructure solutions, including Microsoft SQL Server, Databricks, Google BigQuery, Teradata, Hadoop, and Amazon Web Services.

This is a great tool for both occasional data visualizations and professional data analytics. The system can easily handle any type of data, including streaming performance data, and allows to combine visualizations into functional dashboards. Tableau, as part of Salesforce since 2019, invests in AI and augmented analytics and equips customers with tools for advanced analytics and forecasting.

different data representation techniques

Image credit: Tableau

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Among popular all-purpose data visualization tools in this category are easy-to-learn Visme and Datawrapper which allow to create engaging visualizations without design and coding skills. Both tools offer free basic plans to start from. ChartBlocks and Infogram are other no-code tools with dozens of templates and customization options across various data visualization methods.

Marketer's favorite Canva is a popular solution with varied visualization designs and user-friendly editors. If you use Google suite for business, consider also Looker Studio for business intelligence and reporting.

Data visualization tools for coders

This category of tools includes more sophisticated platforms for presenting data. They stand out for rich functionality to fully unlock the benefits of data visualization . These tools are often used to add visual data analytics techniques and features to data applications and scalable systems built with modern web app architecture approaches and cloud technologies.

FusionCharts is a versatile platform for creating interactive dashboards on web and mobile. It offers rich integration capabilities with support for various frontend and backend frameworks and languages, including Angular, React, Vue, ASP.NET, and PHP.

FusionCharts caters to diverse data visualization needs, offering rich customization options, pre-built themes, 100 ready-to-use charts and 2000 maps, and extensive documentation to make developers' lives easier. This explains the popularity of the platform. Over 800,000 developers and 28,000 organizations such as Dell, Apple, Adobe, and Google, already use this platform.

different data representation techniques

Image credit: FusionCharts

Sisense is another industry-grade data visualization tool with rich analytics capabilities. This cloud-based platform has a drag-and-drop interface, can handle multiple data sources, and supports natural language queries.

Sisense dashboards are highly customizable. You can personalize the look and feel, add images, text, videos, and links, add filters and drill-down features, and transform static visualizations into interactive storytelling experiences.

The platform has a strong focus on AI and ML to provide actionable insights for users. The platform stands out for its scalability and flexibility. It's easy to integrate Sisense analytics and visualizations using their flexible developer toolkit and SDKs to either build a new data application or embed dashboards and visualizations into an existing one.

different data representation techniques

Image credit: Sisense

Plotly is a popular platform mainly focused on developing data apps with Python . It offers rich data visualization tools and techniques and enables integrations with ChatGPT and LLMS to create visualizations using prompts. Plotly's open-source libraries for Python, R, JavaScript, F#, Julia, and other programming languages help developers create various interactive visualizations, including complex maps, animations, and 3D charts.

IBM Cognos Analytics is known for its NLP capabilities. The platform supports conversational data control and provides versatile tools for dashboard building and data reporting. The AI assistant uses natural language queries to build stunning visualizations and can even choose optimal visual data analysis techniques based on what insights you need to get.

If MongoDB is a part of your stack, consider also MongoDB Charts for your MongoDB data. It seamlessly integrates with the core platform's tools and offers various features for creating charts and dashboards.

Tools for complex data visualization and analytics

The growing adoption of connected technology places a lot of opportunities before companies and organizations. To deal with large volumes of multi-source often unstructured data, businesses search for more complex visualization and analytics solutions. This category includes Microsoft Azure Power BI, ELK stack Kibana, and Grafana.

Power BI is exceptional for its highly intuitive drag-and-drop interface, short learning curve, and large integration capabilities, including Salesforce and MailChimp. Not to mention moderate pricing ($10 per month for a Pro version).

different data representation techniques

Image credit: Microsoft

Thanks to Azure services, Power BI became one of the most robust data visualization and analytics tools that can handle nearly any amount and any type of data.

First of all, the platform allows you to create customized reports from different data sources and get insights in a couple of clicks. Secondly, Power BI is powerful and can easily work with streaming real-time data. Finally, it’s not only fully compatible with Azure and other Microsoft services but also can directly connect to existing apps and drive analytics to custom systems. Watch the introduction to learn more here .

Kibana is the part of the ELK Stack that turns data into actionable insights. It’s built on and designed to work with Elasticsearch data. This exclusivity, however, does not prevent it from being one of the best data visualization tools for log data.

Kibana allows you to explore various big data visualization techniques in data science — interactive charts, maps, histograms, etc. Moreover, Kibana goes beyond building standard dashboards for data visualization and analytics.

This tool will help you leverage various visual data analysis techniques in big data: combine visualizations from multiple sources to find correlations, explore trends, and add machine learning features to reveal hidden relationships between events. Drag-and-drop Kibana Lens helps you explore visualized data and get quick insights in just a few clicks. And a rich toolkit for developers and APIs come as a cherry on top.

different data representation techniques

Image credit: Elasticsearch

Grafana — a professional data visualization and analytic tool that supports a wide range of data sources, including AWS, Elasticsearch, and Prometheus.

Even though Grafana is more flexible in terms of integrations compared to Kibana, each of the systems works best with its own type of data. In the case of Grafana, it’s metrics. This visualization software is popular for building IoT applications and creating dashboards and monitoring tools for telemetry systems that use different IoT data collection methods .

Grafana allows you to visualize and compile different types of metrics data into dynamic dashboards. It has a wide variation of features, plugins, and roles which makes it perfect for complex monitoring and control systems.

Additionally, it enables alerts and notifications based on predefined rules. And finally, Grafana has perks for fast data analytics, such as creating custom filters and making annotations — adding metadata to certain events on a dashboard.

Qlik Sense is a data intelligence platform with unique capabilities and features. It provides highly interactive visualizations and dashboards that enable fast insight discovery. Every time a user clicks on an event or metric, the platform refines the context on the spot to show unique dependencies and correlations.

Qlik stands out for lightning-fast calculations and AI technologies in the core analytics functions.

These are just the major tools and techniques of data visualization. All the mentioned platforms and services are evolving very fast and introduce new features and capabilities to keep up with the market needs. Especially when it comes to the growing pace of big data and Internet of Things development.

Tips to create efficient data dashboards

Choosing the right data visualization techniques and tools is the key point to figure out when working with data. However, it is not the only one.

Often visualizations are combined into dashboards to provide analysts, management and other users with complete information on a subject. Dashboards have different functions (show changes in conditions, help track activity and location in real-time, provide remote monitoring and control of a system, etc.) and specifics (dynamic vs. static, historical vs. real-time, KPI/goals dashboards, etc.) that determine their design and features. However, there are several important factors to consider when you create a data dashboard of any type or purpose:

Tip 1. Consistency

Consistency is the key to fluency and fast dashboard navigation. It’s important to stick to specific color-coding, fonts, styles, and visualization elements when showing the same metrics across different dashboards.

Tip 2. The right choice of visualizations

No visualization is one-size-fits-all, even a line chart. It’s crucial to choose the right visualization technique for each type of data on a dashboard to ensure its usability and avoid confusion or even misinterpretation. Check the examples in this article that support this point.

Tip 3. Personalization

Not only does the audience impact the choice of individual visualizations but also determines how to create a data analysis dashboard. It’s essential to keep the goals of different end-users in mind when deciding what visualizations and data should be included in a dashboard. After all, important information for one user can be unessential or even meaningless for others.

Example: A health tracking app used by patients and doctors should have two personalized dashboards. The patient’s dashboard can include basic health data such as blood pressure, medication intake, activity tracking, while the doctor’s dashboard can combine this data with test results, EHR notes and other medical information to provide a more comprehensive picture of the patient’s condition.

Tip 4. Device constraints

Screen size is an important parameter when we are talking about multifunctional dashboards that are supposed to be used on different devices. A dashboard on a small mobile screen should provide no less value than a dashboard on a desktop screen. For this purpose, designers should consider responsiveness and provide tools and features to easily manipulate dashboards on limited smartphone screens — quickly navigate between views, drill data, compile custom reports, etc. This point is particularly important when creating UX/UI design for IoT apps for they are usually data-heavy.

Tip 5. Value

A dashboard should provide value the moment the user accesses it. However, it does not necessarily mean that all the data should be stuffed into screen one. On the contrary, visualizations should be carefully selected, grouped and aligned on every screen to immediately answer all important questions and suggest ways to further explore the data.

Tip 6. Testing

Whether you create an automated data visualization dashboard using tools like Grafana or design a custom dashboard for your system, it’s important to test your visualizations on different volumes of data and in different conditions before going live. In many cases, dashboards are developed based on test data. Once released, dashboards show real data which can be quite different from the test data. As a result, these dashboards do not look and behave as intended. Testing in different conditions helps bridge this gap and avoid inconsistency.

Examples of data visualization: real stories from our experience

We have worked on a range of data visualization and analytics projects for cleantech, logistics, healthcare, retail and IT companies and successfully integrated custom data solutions into their operations. Here are a few use cases that show different approaches to data visualization and their effect on business outcomes.

Efficient performance monitoring helps a printing company handle a 200% traffic increase with a 0% slowdown

Our long-standing client, one of the leading printing companies in the U.S., was dealing with a traffic upsurge challenge every holiday season. They needed an effective monitoring and control solution to avoid website slowdown or performance decrease.

Our dedicated team has been working with the lab's tech infrastructure for a decade. To address the challenge, we developed a custom data analytics and visualization tool based on the combination of Elastic Stack solutions. It collects and analyzes real-time performance data – website traffic, backend load, the quantity and status of submitted orders, printing status and queue. The system then visualizes data for real-time monitoring, registers failed operations and sends alerts to the support team if any problem is spotted.

big data performance monitoring

From the moment this tool was integrated into the lab’s operations, the company could better manage growing traffic and go through intensive seasonal sales without slowdown or lost orders. Since then, we have upgraded the monitoring system and scaled the client's entire infrastructure to better address the constantly growing load ( microservices development services to enhance scalability and resilience, migration to modern .NET technologies , adoption of cloud and data services, etc.)

Monitor IT infrastructure in real-time to improve security and employee performance

A Finnish tech company developed a SaaS product for real-time IT infrastructure monitoring. The system was originally focused on Windows products, and we were asked to scale it up for the macOS platform.

We developed a multifunctional monitoring system to track performance and provide real-time data on the health and security of the customers' IT infrastructures. The system collects data on registered software and hardware performance, sends it to the cloud-based backend for analysis, and provides customers with visualized insights on dashboards and reports.

Infrastructure Monitoring data visualization

When integrated into the company’s operations, the system helps improve employee productivity and mitigate possible security and performance risks. It is an effective tool for IT asset tracking and management.

These are a few examples that demonstrate how effective data visualization systems and tools affect business operations and help companies deal with major performance challenges.

Leverage powerful data visualization tools with Digiteum

If you are looking for skilled tech experts to help you design and create a data visualization system for your business, consult on the use of professional data visualization tools, and integrate them into your decision-making process, we can help.

We work with a wide range of data visualization and analytics platforms (ELK Stack and Kibana, Grafana, Qlik, Sisense, Power BI, Tableau, etc.) and apply a range of techniques, including popular data visualization techniques in Machine Learning, IoT and big data. Our designers, software and data engineers create stunning visualizations and data tools for such companies as Oxford Languages, Printique, Origin Digital, feed.fm, and Diaceutics. We can help you:

  • Select, configure, and integrate the right data visualization and analytics tools according to your business needs and scale.
  • Design and build custom dashboards and add powerful features for data sorting, analysis, and reporting.
  • Integrate data visualization techniques in business analytics that best fit your requirements and BI goals.
  • Optimize time to insight and cut the cost of BI with robust data management and analytics tools.
  • Design, build, and support your entire data infrastructure and operations using modern cloud-based solutions.

Check our big data services  to learn how we can help you turn your data into a source of income and growth... without losing track of your costs.

Read and understand your data faster

Integrate powerful data visualization tools and techniques to your decision-making process.

In this article, we answered the most common questions about platforms, tools and methods of data visualization: What are data visualization techniques and tools? How to use different visualization tools to unlock the power of data? How to build effective data dashboards? You can use this foundational knowledge to start working with your data and select tools that will help you extract real value from your assets.

If you need help to find the right data tools for your specific business needs, contact our team .

This post was originally published on August 10, 2018 and updated on September 16, 2021 and September 29, 2023.

different data representation techniques

Post by Digiteum Team

  • FusionCharts
  • Create consistent color coding, styles and visualization elements for easy navigation.
  • Choose the right data visualization techniques for different types of data and use cases.
  • Personalize dashboard layout and functionality to specific audience.
  • Take into consideration device constraints and provide additional tools to manipulate data on small screens.
  • Group and align data to maximize value, not volume.
  • Test dashboard performance using different volumes of data to see how it can handle the real data flow and dynamics.

different data representation techniques

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different data representation techniques

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NCBI Bookshelf. A service of the National Library of Medicine, National Institutes of Health.

National Research Council; Division of Behavioral and Social Sciences and Education; Commission on Behavioral and Social Sciences and Education; Committee on Basic Research in the Behavioral and Social Sciences; Gerstein DR, Luce RD, Smelser NJ, et al., editors. The Behavioral and Social Sciences: Achievements and Opportunities. Washington (DC): National Academies Press (US); 1988.

Cover of The Behavioral and Social Sciences: Achievements and Opportunities

The Behavioral and Social Sciences: Achievements and Opportunities.

  • Hardcopy Version at National Academies Press

5 Methods of Data Collection, Representation, and Analysis

This chapter concerns research on collecting, representing, and analyzing the data that underlie behavioral and social sciences knowledge. Such research, methodological in character, includes ethnographic and historical approaches, scaling, axiomatic measurement, and statistics, with its important relatives, econometrics and psychometrics. The field can be described as including the self-conscious study of how scientists draw inferences and reach conclusions from observations. Since statistics is the largest and most prominent of methodological approaches and is used by researchers in virtually every discipline, statistical work draws the lion’s share of this chapter’s attention.

Problems of interpreting data arise whenever inherent variation or measurement fluctuations create challenges to understand data or to judge whether observed relationships are significant, durable, or general. Some examples: Is a sharp monthly (or yearly) increase in the rate of juvenile delinquency (or unemployment) in a particular area a matter for alarm, an ordinary periodic or random fluctuation, or the result of a change or quirk in reporting method? Do the temporal patterns seen in such repeated observations reflect a direct causal mechanism, a complex of indirect ones, or just imperfections in the data? Is a decrease in auto injuries an effect of a new seat-belt law? Are the disagreements among people describing some aspect of a subculture too great to draw valid inferences about that aspect of the culture?

Such issues of inference are often closely connected to substantive theory and specific data, and to some extent it is difficult and perhaps misleading to treat methods of data collection, representation, and analysis separately. This report does so, as do all sciences to some extent, because the methods developed often are far more general than the specific problems that originally gave rise to them. There is much transfer of new ideas from one substantive field to another—and to and from fields outside the behavioral and social sciences. Some of the classical methods of statistics arose in studies of astronomical observations, biological variability, and human diversity. The major growth of the classical methods occurred in the twentieth century, greatly stimulated by problems in agriculture and genetics. Some methods for uncovering geometric structures in data, such as multidimensional scaling and factor analysis, originated in research on psychological problems, but have been applied in many other sciences. Some time-series methods were developed originally to deal with economic data, but they are equally applicable to many other kinds of data.

  • In economics: large-scale models of the U.S. economy; effects of taxation, money supply, and other government fiscal and monetary policies; theories of duopoly, oligopoly, and rational expectations; economic effects of slavery.
  • In psychology: test calibration; the formation of subjective probabilities, their revision in the light of new information, and their use in decision making; psychiatric epidemiology and mental health program evaluation.
  • In sociology and other fields: victimization and crime rates; effects of incarceration and sentencing policies; deployment of police and fire-fighting forces; discrimination, antitrust, and regulatory court cases; social networks; population growth and forecasting; and voting behavior.

Even such an abridged listing makes clear that improvements in methodology are valuable across the spectrum of empirical research in the behavioral and social sciences as well as in application to policy questions. Clearly, methodological research serves many different purposes, and there is a need to develop different approaches to serve those different purposes, including exploratory data analysis, scientific inference about hypotheses and population parameters, individual decision making, forecasting what will happen in the event or absence of intervention, and assessing causality from both randomized experiments and observational data.

This discussion of methodological research is divided into three areas: design, representation, and analysis. The efficient design of investigations must take place before data are collected because it involves how much, what kind of, and how data are to be collected. What type of study is feasible: experimental, sample survey, field observation, or other? What variables should be measured, controlled, and randomized? How extensive a subject pool or observational period is appropriate? How can study resources be allocated most effectively among various sites, instruments, and subsamples?

The construction of useful representations of the data involves deciding what kind of formal structure best expresses the underlying qualitative and quantitative concepts that are being used in a given study. For example, cost of living is a simple concept to quantify if it applies to a single individual with unchanging tastes in stable markets (that is, markets offering the same array of goods from year to year at varying prices), but as a national aggregate for millions of households and constantly changing consumer product markets, the cost of living is not easy to specify clearly or measure reliably. Statisticians, economists, sociologists, and other experts have long struggled to make the cost of living a precise yet practicable concept that is also efficient to measure, and they must continually modify it to reflect changing circumstances.

Data analysis covers the final step of characterizing and interpreting research findings: Can estimates of the relations between variables be made? Can some conclusion be drawn about correlation, cause and effect, or trends over time? How uncertain are the estimates and conclusions and can that uncertainty be reduced by analyzing the data in a different way? Can computers be used to display complex results graphically for quicker or better understanding or to suggest different ways of proceeding?

Advances in analysis, data representation, and research design feed into and reinforce one another in the course of actual scientific work. The intersections between methodological improvements and empirical advances are an important aspect of the multidisciplinary thrust of progress in the behavioral and social sciences.

  • Designs for Data Collection

Four broad kinds of research designs are used in the behavioral and social sciences: experimental, survey, comparative, and ethnographic.

Experimental designs, in either the laboratory or field settings, systematically manipulate a few variables while others that may affect the outcome are held constant, randomized, or otherwise controlled. The purpose of randomized experiments is to ensure that only one or a few variables can systematically affect the results, so that causes can be attributed. Survey designs include the collection and analysis of data from censuses, sample surveys, and longitudinal studies and the examination of various relationships among the observed phenomena. Randomization plays a different role here than in experimental designs: it is used to select members of a sample so that the sample is as representative of the whole population as possible. Comparative designs involve the retrieval of evidence that is recorded in the flow of current or past events in different times or places and the interpretation and analysis of this evidence. Ethnographic designs, also known as participant-observation designs, involve a researcher in intensive and direct contact with a group, community, or population being studied, through participation, observation, and extended interviewing.

Experimental Designs

Laboratory experiments.

Laboratory experiments underlie most of the work reported in Chapter 1 , significant parts of Chapter 2 , and some of the newest lines of research in Chapter 3 . Laboratory experiments extend and adapt classical methods of design first developed, for the most part, in the physical and life sciences and agricultural research. Their main feature is the systematic and independent manipulation of a few variables and the strict control or randomization of all other variables that might affect the phenomenon under study. For example, some studies of animal motivation involve the systematic manipulation of amounts of food and feeding schedules while other factors that may also affect motivation, such as body weight, deprivation, and so on, are held constant. New designs are currently coming into play largely because of new analytic and computational methods (discussed below, in “Advances in Statistical Inference and Analysis”).

Two examples of empirically important issues that demonstrate the need for broadening classical experimental approaches are open-ended responses and lack of independence of successive experimental trials. The first concerns the design of research protocols that do not require the strict segregation of the events of an experiment into well-defined trials, but permit a subject to respond at will. These methods are needed when what is of interest is how the respondent chooses to allocate behavior in real time and across continuously available alternatives. Such empirical methods have long been used, but they can generate very subtle and difficult problems in experimental design and subsequent analysis. As theories of allocative behavior of all sorts become more sophisticated and precise, the experimental requirements become more demanding, so the need to better understand and solve this range of design issues is an outstanding challenge to methodological ingenuity.

The second issue arises in repeated-trial designs when the behavior on successive trials, even if it does not exhibit a secular trend (such as a learning curve), is markedly influenced by what has happened in the preceding trial or trials. The more naturalistic the experiment and the more sensitive the meas urements taken, the more likely it is that such effects will occur. But such sequential dependencies in observations cause a number of important conceptual and technical problems in summarizing the data and in testing analytical models, which are not yet completely understood. In the absence of clear solutions, such effects are sometimes ignored by investigators, simplifying the data analysis but leaving residues of skepticism about the reliability and significance of the experimental results. With continuing development of sensitive measures in repeated-trial designs, there is a growing need for more advanced concepts and methods for dealing with experimental results that may be influenced by sequential dependencies.

Randomized Field Experiments

The state of the art in randomized field experiments, in which different policies or procedures are tested in controlled trials under real conditions, has advanced dramatically over the past two decades. Problems that were once considered major methodological obstacles—such as implementing randomized field assignment to treatment and control groups and protecting the randomization procedure from corruption—have been largely overcome. While state-of-the-art standards are not achieved in every field experiment, the commitment to reaching them is rising steadily, not only among researchers but also among customer agencies and sponsors.

The health insurance experiment described in Chapter 2 is an example of a major randomized field experiment that has had and will continue to have important policy reverberations in the design of health care financing. Field experiments with the negative income tax (guaranteed minimum income) conducted in the 1970s were significant in policy debates, even before their completion, and provided the most solid evidence available on how tax-based income support programs and marginal tax rates can affect the work incentives and family structures of the poor. Important field experiments have also been carried out on alternative strategies for the prevention of delinquency and other criminal behavior, reform of court procedures, rehabilitative programs in mental health, family planning, and special educational programs, among other areas.

In planning field experiments, much hinges on the definition and design of the experimental cells, the particular combinations needed of treatment and control conditions for each set of demographic or other client sample characteristics, including specification of the minimum number of cases needed in each cell to test for the presence of effects. Considerations of statistical power, client availability, and the theoretical structure of the inquiry enter into such specifications. Current important methodological thresholds are to find better ways of predicting recruitment and attrition patterns in the sample, of designing experiments that will be statistically robust in the face of problematic sample recruitment or excessive attrition, and of ensuring appropriate acquisition and analysis of data on the attrition component of the sample.

Also of major significance are improvements in integrating detailed process and outcome measurements in field experiments. To conduct research on program effects under field conditions requires continual monitoring to determine exactly what is being done—the process—how it corresponds to what was projected at the outset. Relatively unintrusive, inexpensive, and effective implementation measures are of great interest. There is, in parallel, a growing emphasis on designing experiments to evaluate distinct program components in contrast to summary measures of net program effects.

Finally, there is an important opportunity now for further theoretical work to model organizational processes in social settings and to design and select outcome variables that, in the relatively short time of most field experiments, can predict longer-term effects: For example, in job-training programs, what are the effects on the community (role models, morale, referral networks) or on individual skills, motives, or knowledge levels that are likely to translate into sustained changes in career paths and income levels?

Survey Designs

Many people have opinions about how societal mores, economic conditions, and social programs shape lives and encourage or discourage various kinds of behavior. People generalize from their own cases, and from the groups to which they belong, about such matters as how much it costs to raise a child, the extent to which unemployment contributes to divorce, and so on. In fact, however, effects vary so much from one group to another that homespun generalizations are of little use. Fortunately, behavioral and social scientists have been able to bridge the gaps between personal perspectives and collective realities by means of survey research. In particular, governmental information systems include volumes of extremely valuable survey data, and the facility of modern computers to store, disseminate, and analyze such data has significantly improved empirical tests and led to new understandings of social processes.

Within this category of research designs, two major types are distinguished: repeated cross-sectional surveys and longitudinal panel surveys. In addition, and cross-cutting these types, there is a major effort under way to improve and refine the quality of survey data by investigating features of human memory and of question formation that affect survey response.

Repeated cross-sectional designs can either attempt to measure an entire population—as does the oldest U.S. example, the national decennial census—or they can rest on samples drawn from a population. The general principle is to take independent samples at two or more times, measuring the variables of interest, such as income levels, housing plans, or opinions about public affairs, in the same way. The General Social Survey, collected by the National Opinion Research Center with National Science Foundation support, is a repeated cross sectional data base that was begun in 1972. One methodological question of particular salience in such data is how to adjust for nonresponses and “don’t know” responses. Another is how to deal with self-selection bias. For example, to compare the earnings of women and men in the labor force, it would be mistaken to first assume that the two samples of labor-force participants are randomly selected from the larger populations of men and women; instead, one has to consider and incorporate in the analysis the factors that determine who is in the labor force.

In longitudinal panels, a sample is drawn at one point in time and the relevant variables are measured at this and subsequent times for the same people. In more complex versions, some fraction of each panel may be replaced or added to periodically, such as expanding the sample to include households formed by the children of the original sample. An example of panel data developed in this way is the Panel Study of Income Dynamics (PSID), conducted by the University of Michigan since 1968 (discussed in Chapter 3 ).

Comparing the fertility or income of different people in different circumstances at the same time to find correlations always leaves a large proportion of the variability unexplained, but common sense suggests that much of the unexplained variability is actually explicable. There are systematic reasons for individual outcomes in each person’s past achievements, in parental models, upbringing, and earlier sequences of experiences. Unfortunately, asking people about the past is not particularly helpful: people remake their views of the past to rationalize the present and so retrospective data are often of uncertain validity. In contrast, generation-long longitudinal data allow readings on the sequence of past circumstances uncolored by later outcomes. Such data are uniquely useful for studying the causes and consequences of naturally occurring decisions and transitions. Thus, as longitudinal studies continue, quantitative analysis is becoming feasible about such questions as: How are the decisions of individuals affected by parental experience? Which aspects of early decisions constrain later opportunities? And how does detailed background experience leave its imprint? Studies like the two-decade-long PSID are bringing within grasp a complete generational cycle of detailed data on fertility, work life, household structure, and income.

Advances in Longitudinal Designs

Large-scale longitudinal data collection projects are uniquely valuable as vehicles for testing and improving survey research methodology. In ways that lie beyond the scope of a cross-sectional survey, longitudinal studies can sometimes be designed—without significant detriment to their substantive interests—to facilitate the evaluation and upgrading of data quality; the analysis of relative costs and effectiveness of alternative techniques of inquiry; and the standardization or coordination of solutions to problems of method, concept, and measurement across different research domains.

Some areas of methodological improvement include discoveries about the impact of interview mode on response (mail, telephone, face-to-face); the effects of nonresponse on the representativeness of a sample (due to respondents’ refusal or interviewers’ failure to contact); the effects on behavior of continued participation over time in a sample survey; the value of alternative methods of adjusting for nonresponse and incomplete observations (such as imputation of missing data, variable case weighting); the impact on response of specifying different recall periods, varying the intervals between interviews, or changing the length of interviews; and the comparison and calibration of results obtained by longitudinal surveys, randomized field experiments, laboratory studies, onetime surveys, and administrative records.

It should be especially noted that incorporating improvements in methodology and data quality has been and will no doubt continue to be crucial to the growing success of longitudinal studies. Panel designs are intrinsically more vulnerable than other designs to statistical biases due to cumulative item non-response, sample attrition, time-in-sample effects, and error margins in repeated measures, all of which may produce exaggerated estimates of change. Over time, a panel that was initially representative may become much less representative of a population, not only because of attrition in the sample, but also because of changes in immigration patterns, age structure, and the like. Longitudinal studies are also subject to changes in scientific and societal contexts that may create uncontrolled drifts over time in the meaning of nominally stable questions or concepts as well as in the underlying behavior. Also, a natural tendency to expand over time the range of topics and thus the interview lengths, which increases the burdens on respondents, may lead to deterioration of data quality or relevance. Careful methodological research to understand and overcome these problems has been done, and continued work as a component of new longitudinal studies is certain to advance the overall state of the art.

Longitudinal studies are sometimes pressed for evidence they are not designed to produce: for example, in important public policy questions concerning the impact of government programs in such areas as health promotion, disease prevention, or criminal justice. By using research designs that combine field experiments (with randomized assignment to program and control conditions) and longitudinal surveys, one can capitalize on the strongest merits of each: the experimental component provides stronger evidence for casual statements that are critical for evaluating programs and for illuminating some fundamental theories; the longitudinal component helps in the estimation of long-term program effects and their attenuation. Coupling experiments to ongoing longitudinal studies is not often feasible, given the multiple constraints of not disrupting the survey, developing all the complicated arrangements that go into a large-scale field experiment, and having the populations of interest overlap in useful ways. Yet opportunities to join field experiments to surveys are of great importance. Coupled studies can produce vital knowledge about the empirical conditions under which the results of longitudinal surveys turn out to be similar to—or divergent from—those produced by randomized field experiments. A pattern of divergence and similarity has begun to emerge in coupled studies; additional cases are needed to understand why some naturally occurring social processes and longitudinal design features seem to approximate formal random allocation and others do not. The methodological implications of such new knowledge go well beyond program evaluation and survey research. These findings bear directly on the confidence scientists—and others—can have in conclusions from observational studies of complex behavioral and social processes, particularly ones that cannot be controlled or simulated within the confines of a laboratory environment.

Memory and the Framing of Questions

A very important opportunity to improve survey methods lies in the reduction of nonsampling error due to questionnaire context, phrasing of questions, and, generally, the semantic and social-psychological aspects of surveys. Survey data are particularly affected by the fallibility of human memory and the sensitivity of respondents to the framework in which a question is asked. This sensitivity is especially strong for certain types of attitudinal and opinion questions. Efforts are now being made to bring survey specialists into closer contact with researchers working on memory function, knowledge representation, and language in order to uncover and reduce this kind of error.

Memory for events is often inaccurate, biased toward what respondents believe to be true—or should be true—about the world. In many cases in which data are based on recollection, improvements can be achieved by shifting to techniques of structured interviewing and calibrated forms of memory elicitation, such as specifying recent, brief time periods (for example, in the last seven days) within which respondents recall certain types of events with acceptable accuracy.

  • “Taking things altogether, how would you describe your marriage? Would you say that your marriage is very happy, pretty happy, or not too happy?”
  • “Taken altogether how would you say things are these days—would you say you are very happy, pretty happy, or not too happy?”

Presenting this sequence in both directions on different forms showed that the order affected answers to the general happiness question but did not change the marital happiness question: responses to the specific issue swayed subsequent responses to the general one, but not vice versa. The explanations for and implications of such order effects on the many kinds of questions and sequences that can be used are not simple matters. Further experimentation on the design of survey instruments promises not only to improve the accuracy and reliability of survey research, but also to advance understanding of how people think about and evaluate their behavior from day to day.

Comparative Designs

Both experiments and surveys involve interventions or questions by the scientist, who then records and analyzes the responses. In contrast, many bodies of social and behavioral data of considerable value are originally derived from records or collections that have accumulated for various nonscientific reasons, quite often administrative in nature, in firms, churches, military organizations, and governments at all levels. Data of this kind can sometimes be subjected to careful scrutiny, summary, and inquiry by historians and social scientists, and statistical methods have increasingly been used to develop and evaluate inferences drawn from such data. Some of the main comparative approaches are cross-national aggregate comparisons, selective comparison of a limited number of cases, and historical case studies.

Among the more striking problems facing the scientist using such data are the vast differences in what has been recorded by different agencies whose behavior is being compared (this is especially true for parallel agencies in different nations), the highly unrepresentative or idiosyncratic sampling that can occur in the collection of such data, and the selective preservation and destruction of records. Means to overcome these problems form a substantial methodological research agenda in comparative research. An example of the method of cross-national aggregative comparisons is found in investigations by political scientists and sociologists of the factors that underlie differences in the vitality of institutions of political democracy in different societies. Some investigators have stressed the existence of a large middle class, others the level of education of a population, and still others the development of systems of mass communication. In cross-national aggregate comparisons, a large number of nations are arrayed according to some measures of political democracy and then attempts are made to ascertain the strength of correlations between these and the other variables. In this line of analysis it is possible to use a variety of statistical cluster and regression techniques to isolate and assess the possible impact of certain variables on the institutions under study. While this kind of research is cross-sectional in character, statements about historical processes are often invoked to explain the correlations.

More limited selective comparisons, applied by many of the classic theorists, involve asking similar kinds of questions but over a smaller range of societies. Why did democracy develop in such different ways in America, France, and England? Why did northeastern Europe develop rational bourgeois capitalism, in contrast to the Mediterranean and Asian nations? Modern scholars have turned their attention to explaining, for example, differences among types of fascism between the two World Wars, and similarities and differences among modern state welfare systems, using these comparisons to unravel the salient causes. The questions asked in these instances are inevitably historical ones.

Historical case studies involve only one nation or region, and so they may not be geographically comparative. However, insofar as they involve tracing the transformation of a society’s major institutions and the role of its main shaping events, they involve a comparison of different periods of a nation’s or a region’s history. The goal of such comparisons is to give a systematic account of the relevant differences. Sometimes, particularly with respect to the ancient societies, the historical record is very sparse, and the methods of history and archaeology mesh in the reconstruction of complex social arrangements and patterns of change on the basis of few fragments.

Like all research designs, comparative ones have distinctive vulnerabilities and advantages: One of the main advantages of using comparative designs is that they greatly expand the range of data, as well as the amount of variation in those data, for study. Consequently, they allow for more encompassing explanations and theories that can relate highly divergent outcomes to one another in the same framework. They also contribute to reducing any cultural biases or tendencies toward parochialism among scientists studying common human phenomena.

One main vulnerability in such designs arises from the problem of achieving comparability. Because comparative study involves studying societies and other units that are dissimilar from one another, the phenomena under study usually occur in very different contexts—so different that in some cases what is called an event in one society cannot really be regarded as the same type of event in another. For example, a vote in a Western democracy is different from a vote in an Eastern bloc country, and a voluntary vote in the United States means something different from a compulsory vote in Australia. These circumstances make for interpretive difficulties in comparing aggregate rates of voter turnout in different countries.

The problem of achieving comparability appears in historical analysis as well. For example, changes in laws and enforcement and recording procedures over time change the definition of what is and what is not a crime, and for that reason it is difficult to compare the crime rates over time. Comparative researchers struggle with this problem continually, working to fashion equivalent measures; some have suggested the use of different measures (voting, letters to the editor, street demonstration) in different societies for common variables (political participation), to try to take contextual factors into account and to achieve truer comparability.

A second vulnerability is controlling variation. Traditional experiments make conscious and elaborate efforts to control the variation of some factors and thereby assess the causal significance of others. In surveys as well as experiments, statistical methods are used to control sources of variation and assess suspected causal significance. In comparative and historical designs, this kind of control is often difficult to attain because the sources of variation are many and the number of cases few. Scientists have made efforts to approximate such control in these cases of “many variables, small N.” One is the method of paired comparisons. If an investigator isolates 15 American cities in which racial violence has been recurrent in the past 30 years, for example, it is helpful to match them with 15 cities of similar population size, geographical region, and size of minorities—such characteristics are controls—and then search for systematic differences between the two sets of cities. Another method is to select, for comparative purposes, a sample of societies that resemble one another in certain critical ways, such as size, common language, and common level of development, thus attempting to hold these factors roughly constant, and then seeking explanations among other factors in which the sampled societies differ from one another.

Ethnographic Designs

Traditionally identified with anthropology, ethnographic research designs are playing increasingly significant roles in most of the behavioral and social sciences. The core of this methodology is participant-observation, in which a researcher spends an extended period of time with the group under study, ideally mastering the local language, dialect, or special vocabulary, and participating in as many activities of the group as possible. This kind of participant-observation is normally coupled with extensive open-ended interviewing, in which people are asked to explain in depth the rules, norms, practices, and beliefs through which (from their point of view) they conduct their lives. A principal aim of ethnographic study is to discover the premises on which those rules, norms, practices, and beliefs are built.

The use of ethnographic designs by anthropologists has contributed significantly to the building of knowledge about social and cultural variation. And while these designs continue to center on certain long-standing features—extensive face-to-face experience in the community, linguistic competence, participation, and open-ended interviewing—there are newer trends in ethnographic work. One major trend concerns its scale. Ethnographic methods were originally developed largely for studying small-scale groupings known variously as village, folk, primitive, preliterate, or simple societies. Over the decades, these methods have increasingly been applied to the study of small groups and networks within modern (urban, industrial, complex) society, including the contemporary United States. The typical subjects of ethnographic study in modern society are small groups or relatively small social networks, such as outpatient clinics, medical schools, religious cults and churches, ethnically distinctive urban neighborhoods, corporate offices and factories, and government bureaus and legislatures.

As anthropologists moved into the study of modern societies, researchers in other disciplines—particularly sociology, psychology, and political science—began using ethnographic methods to enrich and focus their own insights and findings. At the same time, studies of large-scale structures and processes have been aided by the use of ethnographic methods, since most large-scale changes work their way into the fabric of community, neighborhood, and family, affecting the daily lives of people. Ethnographers have studied, for example, the impact of new industry and new forms of labor in “backward” regions; the impact of state-level birth control policies on ethnic groups; and the impact on residents in a region of building a dam or establishing a nuclear waste dump. Ethnographic methods have also been used to study a number of social processes that lend themselves to its particular techniques of observation and interview—processes such as the formation of class and racial identities, bureaucratic behavior, legislative coalitions and outcomes, and the formation and shifting of consumer tastes.

Advances in structured interviewing (see above) have proven especially powerful in the study of culture. Techniques for understanding kinship systems, concepts of disease, color terminologies, ethnobotany, and ethnozoology have been radically transformed and strengthened by coupling new interviewing methods with modem measurement and scaling techniques (see below). These techniques have made possible more precise comparisons among cultures and identification of the most competent and expert persons within a culture. The next step is to extend these methods to study the ways in which networks of propositions (such as boys like sports, girls like babies) are organized to form belief systems. Much evidence suggests that people typically represent the world around them by means of relatively complex cognitive models that involve interlocking propositions. The techniques of scaling have been used to develop models of how people categorize objects, and they have great potential for further development, to analyze data pertaining to cultural propositions.

Ideological Systems

Perhaps the most fruitful area for the application of ethnographic methods in recent years has been the systematic study of ideologies in modern society. Earlier studies of ideology were in small-scale societies that were rather homogeneous. In these studies researchers could report on a single culture, a uniform system of beliefs and values for the society as a whole. Modern societies are much more diverse both in origins and number of subcultures, related to different regions, communities, occupations, or ethnic groups. Yet these subcultures and ideologies share certain underlying assumptions or at least must find some accommodation with the dominant value and belief systems in the society.

The challenge is to incorporate this greater complexity of structure and process into systematic descriptions and interpretations. One line of work carried out by researchers has tried to track the ways in which ideologies are created, transmitted, and shared among large populations that have traditionally lacked the social mobility and communications technologies of the West. This work has concentrated on large-scale civilizations such as China, India, and Central America. Gradually, the focus has generalized into a concern with the relationship between the great traditions—the central lines of cosmopolitan Confucian, Hindu, or Mayan culture, including aesthetic standards, irrigation technologies, medical systems, cosmologies and calendars, legal codes, poetic genres, and religious doctrines and rites—and the little traditions, those identified with rural, peasant communities. How are the ideological doctrines and cultural values of the urban elites, the great traditions, transmitted to local communities? How are the little traditions, the ideas from the more isolated, less literate, and politically weaker groups in society, transmitted to the elites?

India and southern Asia have been fruitful areas for ethnographic research on these questions. The great Hindu tradition was present in virtually all local contexts through the presence of high-caste individuals in every community. It operated as a pervasive standard of value for all members of society, even in the face of strong little traditions. The situation is surprisingly akin to that of modern, industrialized societies. The central research questions are the degree and the nature of penetration of dominant ideology, even in groups that appear marginal and subordinate and have no strong interest in sharing the dominant value system. In this connection the lowest and poorest occupational caste—the untouchables—serves as an ultimate test of the power of ideology and cultural beliefs to unify complex hierarchical social systems.

Historical Reconstruction

Another current trend in ethnographic methods is its convergence with archival methods. One joining point is the application of descriptive and interpretative procedures used by ethnographers to reconstruct the cultures that created historical documents, diaries, and other records, to interview history, so to speak. For example, a revealing study showed how the Inquisition in the Italian countryside between the 1570s and 1640s gradually worked subtle changes in an ancient fertility cult in peasant communities; the peasant beliefs and rituals assimilated many elements of witchcraft after learning them from their persecutors. A good deal of social history—particularly that of the family—has drawn on discoveries made in the ethnographic study of primitive societies. As described in Chapter 4 , this particular line of inquiry rests on a marriage of ethnographic, archival, and demographic approaches.

Other lines of ethnographic work have focused on the historical dimensions of nonliterate societies. A strikingly successful example in this kind of effort is a study of head-hunting. By combining an interpretation of local oral tradition with the fragmentary observations that were made by outside observers (such as missionaries, traders, colonial officials), historical fluctuations in the rate and significance of head-hunting were shown to be partly in response to such international forces as the great depression and World War II. Researchers are also investigating the ways in which various groups in contemporary societies invent versions of traditions that may or may not reflect the actual history of the group. This process has been observed among elites seeking political and cultural legitimation and among hard-pressed minorities (for example, the Basque in Spain, the Welsh in Great Britain) seeking roots and political mobilization in a larger society.

Ethnography is a powerful method to record, describe, and interpret the system of meanings held by groups and to discover how those meanings affect the lives of group members. It is a method well adapted to the study of situations in which people interact with one another and the researcher can interact with them as well, so that information about meanings can be evoked and observed. Ethnography is especially suited to exploration and elucidation of unsuspected connections; ideally, it is used in combination with other methods—experimental, survey, or comparative—to establish with precision the relative strengths and weaknesses of such connections. By the same token, experimental, survey, and comparative methods frequently yield connections, the meaning of which is unknown; ethnographic methods are a valuable way to determine them.

  • Models for Representing Phenomena

The objective of any science is to uncover the structure and dynamics of the phenomena that are its subject, as they are exhibited in the data. Scientists continuously try to describe possible structures and ask whether the data can, with allowance for errors of measurement, be described adequately in terms of them. Over a long time, various families of structures have recurred throughout many fields of science; these structures have become objects of study in their own right, principally by statisticians, other methodological specialists, applied mathematicians, and philosophers of logic and science. Methods have evolved to evaluate the adequacy of particular structures to account for particular types of data. In the interest of clarity we discuss these structures in this section and the analytical methods used for estimation and evaluation of them in the next section, although in practice they are closely intertwined.

A good deal of mathematical and statistical modeling attempts to describe the relations, both structural and dynamic, that hold among variables that are presumed to be representable by numbers. Such models are applicable in the behavioral and social sciences only to the extent that appropriate numerical measurement can be devised for the relevant variables. In many studies the phenomena in question and the raw data obtained are not intrinsically numerical, but qualitative, such as ethnic group identifications. The identifying numbers used to code such questionnaire categories for computers are no more than labels, which could just as well be letters or colors. One key question is whether there is some natural way to move from the qualitative aspects of such data to a structural representation that involves one of the well-understood numerical or geometric models or whether such an attempt would be inherently inappropriate for the data in question. The decision as to whether or not particular empirical data can be represented in particular numerical or more complex structures is seldom simple, and strong intuitive biases or a priori assumptions about what can and cannot be done may be misleading.

Recent decades have seen rapid and extensive development and application of analytical methods attuned to the nature and complexity of social science data. Examples of nonnumerical modeling are increasing. Moreover, the widespread availability of powerful computers is probably leading to a qualitative revolution, it is affecting not only the ability to compute numerical solutions to numerical models, but also to work out the consequences of all sorts of structures that do not involve numbers at all. The following discussion gives some indication of the richness of past progress and of future prospects although it is by necessity far from exhaustive.

In describing some of the areas of new and continuing research, we have organized this section on the basis of whether the representations are fundamentally probabilistic or not. A further useful distinction is between representations of data that are highly discrete or categorical in nature (such as whether a person is male or female) and those that are continuous in nature (such as a person’s height). Of course, there are intermediate cases involving both types of variables, such as color stimuli that are characterized by discrete hues (red, green) and a continuous luminance measure. Probabilistic models lead very naturally to questions of estimation and statistical evaluation of the correspondence between data and model. Those that are not probabilistic involve additional problems of dealing with and representing sources of variability that are not explicitly modeled. At the present time, scientists understand some aspects of structure, such as geometries, and some aspects of randomness, as embodied in probability models, but do not yet adequately understand how to put the two together in a single unified model. Table 5-1 outlines the way we have organized this discussion and shows where the examples in this section lie.

Table 5-1. A Classification of Structural Models.

A Classification of Structural Models.

Probability Models

Some behavioral and social sciences variables appear to be more or less continuous, for example, utility of goods, loudness of sounds, or risk associated with uncertain alternatives. Many other variables, however, are inherently categorical, often with only two or a few values possible: for example, whether a person is in or out of school, employed or not employed, identifies with a major political party or political ideology. And some variables, such as moral attitudes, are typically measured in research with survey questions that allow only categorical responses. Much of the early probability theory was formulated only for continuous variables; its use with categorical variables was not really justified, and in some cases it may have been misleading. Recently, very significant advances have been made in how to deal explicitly with categorical variables. This section first describes several contemporary approaches to models involving categorical variables, followed by ones involving continuous representations.

Log-Linear Models for Categorical Variables

Many recent models for analyzing categorical data of the kind usually displayed as counts (cell frequencies) in multidimensional contingency tables are subsumed under the general heading of log-linear models, that is, linear models in the natural logarithms of the expected counts in each cell in the table. These recently developed forms of statistical analysis allow one to partition variability due to various sources in the distribution of categorical attributes, and to isolate the effects of particular variables or combinations of them.

Present log-linear models were first developed and used by statisticians and sociologists and then found extensive application in other social and behavioral sciences disciplines. When applied, for instance, to the analysis of social mobility, such models separate factors of occupational supply and demand from other factors that impede or propel movement up and down the social hierarchy. With such models, for example, researchers discovered the surprising fact that occupational mobility patterns are strikingly similar in many nations of the world (even among disparate nations like the United States and most of the Eastern European socialist countries), and from one time period to another, once allowance is made for differences in the distributions of occupations. The log-linear and related kinds of models have also made it possible to identify and analyze systematic differences in mobility among nations and across time. As another example of applications, psychologists and others have used log-linear models to analyze attitudes and their determinants and to link attitudes to behavior. These methods have also diffused to and been used extensively in the medical and biological sciences.

Regression Models for Categorical Variables

Models that permit one variable to be explained or predicted by means of others, called regression models, are the workhorses of much applied statistics; this is especially true when the dependent (explained) variable is continuous. For a two-valued dependent variable, such as alive or dead, models and approximate theory and computational methods for one explanatory variable were developed in biometry about 50 years ago. Computer programs able to handle many explanatory variables, continuous or categorical, are readily available today. Even now, however, the accuracy of the approximate theory on given data is an open question.

Using classical utility theory, economists have developed discrete choice models that turn out to be somewhat related to the log-linear and categorical regression models. Models for limited dependent variables, especially those that cannot take on values above or below a certain level (such as weeks unemployed, number of children, and years of schooling) have been used profitably in economics and in some other areas. For example, censored normal variables (called tobits in economics), in which observed values outside certain limits are simply counted, have been used in studying decisions to go on in school. It will require further research and development to incorporate information about limited ranges of variables fully into the main multivariate methodologies. In addition, with respect to the assumptions about distribution and functional form conventionally made in discrete response models, some new methods are now being developed that show promise of yielding reliable inferences without making unrealistic assumptions; further research in this area promises significant progress.

One problem arises from the fact that many of the categorical variables collected by the major data bases are ordered. For example, attitude surveys frequently use a 3-, 5-, or 7-point scale (from high to low) without specifying numerical intervals between levels. Social class and educational levels are often described by ordered categories. Ignoring order information, which many traditional statistical methods do, may be inefficient or inappropriate, but replacing the categories by successive integers or other arbitrary scores may distort the results. (For additional approaches to this question, see sections below on ordered structures.) Regression-like analysis of ordinal categorical variables is quite well developed, but their multivariate analysis needs further research. New log-bilinear models have been proposed, but to date they deal specifically with only two or three categorical variables. Additional research extending the new models, improving computational algorithms, and integrating the models with work on scaling promise to lead to valuable new knowledge.

Models for Event Histories

Event-history studies yield the sequence of events that respondents to a survey sample experience over a period of time; for example, the timing of marriage, childbearing, or labor force participation. Event-history data can be used to study educational progress, demographic processes (migration, fertility, and mortality), mergers of firms, labor market behavior, and even riots, strikes, and revolutions. As interest in such data has grown, many researchers have turned to models that pertain to changes in probabilities over time to describe when and how individuals move among a set of qualitative states.

Much of the progress in models for event-history data builds on recent developments in statistics and biostatistics for life-time, failure-time, and hazard models. Such models permit the analysis of qualitative transitions in a population whose members are undergoing partially random organic deterioration, mechanical wear, or other risks over time. With the increased complexity of event-history data that are now being collected, and the extension of event-history data bases over very long periods of time, new problems arise that cannot be effectively handled by older types of analysis. Among the problems are repeated transitions, such as between unemployment and employment or marriage and divorce; more than one time variable (such as biological age, calendar time, duration in a stage, and time exposed to some specified condition); latent variables (variables that are explicitly modeled even though not observed); gaps in the data; sample attrition that is not randomly distributed over the categories; and respondent difficulties in recalling the exact timing of events.

Models for Multiple-Item Measurement

For a variety of reasons, researchers typically use multiple measures (or multiple indicators) to represent theoretical concepts. Sociologists, for example, often rely on two or more variables (such as occupation and education) to measure an individual’s socioeconomic position; educational psychologists ordinarily measure a student’s ability with multiple test items. Despite the fact that the basic observations are categorical, in a number of applications this is interpreted as a partitioning of something continuous. For example, in test theory one thinks of the measures of both item difficulty and respondent ability as continuous variables, possibly multidimensional in character.

Classical test theory and newer item-response theories in psychometrics deal with the extraction of information from multiple measures. Testing, which is a major source of data in education and other areas, results in millions of test items stored in archives each year for purposes ranging from college admissions to job-training programs for industry. One goal of research on such test data is to be able to make comparisons among persons or groups even when different test items are used. Although the information collected from each respondent is intentionally incomplete in order to keep the tests short and simple, item-response techniques permit researchers to reconstitute the fragments into an accurate picture of overall group proficiencies. These new methods provide a better theoretical handle on individual differences, and they are expected to be extremely important in developing and using tests. For example, they have been used in attempts to equate different forms of a test given in successive waves during a year, a procedure made necessary in large-scale testing programs by legislation requiring disclosure of test-scoring keys at the time results are given.

An example of the use of item-response theory in a significant research effort is the National Assessment of Educational Progress (NAEP). The goal of this project is to provide accurate, nationally representative information on the average (rather than individual) proficiency of American children in a wide variety of academic subjects as they progress through elementary and secondary school. This approach is an improvement over the use of trend data on university entrance exams, because NAEP estimates of academic achievements (by broad characteristics such as age, grade, region, ethnic background, and so on) are not distorted by the self-selected character of those students who seek admission to college, graduate, and professional programs.

Item-response theory also forms the basis of many new psychometric instruments, known as computerized adaptive testing, currently being implemented by the U.S. military services and under additional development in many testing organizations. In adaptive tests, a computer program selects items for each examinee based upon the examinee’s success with previous items. Generally, each person gets a slightly different set of items and the equivalence of scale scores is established by using item-response theory. Adaptive testing can greatly reduce the number of items needed to achieve a given level of measurement accuracy.

Nonlinear, Nonadditive Models

Virtually all statistical models now in use impose a linearity or additivity assumption of some kind, sometimes after a nonlinear transformation of variables. Imposing these forms on relationships that do not, in fact, possess them may well result in false descriptions and spurious effects. Unwary users, especially of computer software packages, can easily be misled. But more realistic nonlinear and nonadditive multivariate models are becoming available. Extensive use with empirical data is likely to force many changes and enhancements in such models and stimulate quite different approaches to nonlinear multivariate analysis in the next decade.

Geometric and Algebraic Models

Geometric and algebraic models attempt to describe underlying structural relations among variables. In some cases they are part of a probabilistic approach, such as the algebraic models underlying regression or the geometric representations of correlations between items in a technique called factor analysis. In other cases, geometric and algebraic models are developed without explicitly modeling the element of randomness or uncertainty that is always present in the data. Although this latter approach to behavioral and social sciences problems has been less researched than the probabilistic one, there are some advantages in developing the structural aspects independent of the statistical ones. We begin the discussion with some inherently geometric representations and then turn to numerical representations for ordered data.

Although geometry is a huge mathematical topic, little of it seems directly applicable to the kinds of data encountered in the behavioral and social sciences. A major reason is that the primitive concepts normally used in geometry—points, lines, coincidence—do not correspond naturally to the kinds of qualitative observations usually obtained in behavioral and social sciences contexts. Nevertheless, since geometric representations are used to reduce bodies of data, there is a real need to develop a deeper understanding of when such representations of social or psychological data make sense. Moreover, there is a practical need to understand why geometric computer algorithms, such as those of multidimensional scaling, work as well as they apparently do. A better understanding of the algorithms will increase the efficiency and appropriateness of their use, which becomes increasingly important with the widespread availability of scaling programs for microcomputers.

Over the past 50 years several kinds of well-understood scaling techniques have been developed and widely used to assist in the search for appropriate geometric representations of empirical data. The whole field of scaling is now entering a critical juncture in terms of unifying and synthesizing what earlier appeared to be disparate contributions. Within the past few years it has become apparent that several major methods of analysis, including some that are based on probabilistic assumptions, can be unified under the rubric of a single generalized mathematical structure. For example, it has recently been demonstrated that such diverse approaches as nonmetric multidimensional scaling, principal-components analysis, factor analysis, correspondence analysis, and log-linear analysis have more in common in terms of underlying mathematical structure than had earlier been realized.

Nonmetric multidimensional scaling is a method that begins with data about the ordering established by subjective similarity (or nearness) between pairs of stimuli. The idea is to embed the stimuli into a metric space (that is, a geometry with a measure of distance between points) in such a way that distances between points corresponding to stimuli exhibit the same ordering as do the data. This method has been successfully applied to phenomena that, on other grounds, are known to be describable in terms of a specific geometric structure; such applications were used to validate the procedures. Such validation was done, for example, with respect to the perception of colors, which are known to be describable in terms of a particular three-dimensional structure known as the Euclidean color coordinates. Similar applications have been made with Morse code symbols and spoken phonemes. The technique is now used in some biological and engineering applications, as well as in some of the social sciences, as a method of data exploration and simplification.

One question of interest is how to develop an axiomatic basis for various geometries using as a primitive concept an observable such as the subject’s ordering of the relative similarity of one pair of stimuli to another, which is the typical starting point of such scaling. The general task is to discover properties of the qualitative data sufficient to ensure that a mapping into the geometric structure exists and, ideally, to discover an algorithm for finding it. Some work of this general type has been carried out: for example, there is an elegant set of axioms based on laws of color matching that yields the three-dimensional vectorial representation of color space. But the more general problem of understanding the conditions under which the multidimensional scaling algorithms are suitable remains unsolved. In addition, work is needed on understanding more general, non-Euclidean spatial models.

Ordered Factorial Systems

One type of structure common throughout the sciences arises when an ordered dependent variable is affected by two or more ordered independent variables. This is the situation to which regression and analysis-of-variance models are often applied; it is also the structure underlying the familiar physical identities, in which physical units are expressed as products of the powers of other units (for example, energy has the unit of mass times the square of the unit of distance divided by the square of the unit of time).

There are many examples of these types of structures in the behavioral and social sciences. One example is the ordering of preference of commodity bundles—collections of various amounts of commodities—which may be revealed directly by expressions of preference or indirectly by choices among alternative sets of bundles. A related example is preferences among alternative courses of action that involve various outcomes with differing degrees of uncertainty; this is one of the more thoroughly investigated problems because of its potential importance in decision making. A psychological example is the trade-off between delay and amount of reward, yielding those combinations that are equally reinforcing. In a common, applied kind of problem, a subject is given descriptions of people in terms of several factors, for example, intelligence, creativity, diligence, and honesty, and is asked to rate them according to a criterion such as suitability for a particular job.

In all these cases and a myriad of others like them the question is whether the regularities of the data permit a numerical representation. Initially, three types of representations were studied quite fully: the dependent variable as a sum, a product, or a weighted average of the measures associated with the independent variables. The first two representations underlie some psychological and economic investigations, as well as a considerable portion of physical measurement and modeling in classical statistics. The third representation, averaging, has proved most useful in understanding preferences among uncertain outcomes and the amalgamation of verbally described traits, as well as some physical variables.

For each of these three cases—adding, multiplying, and averaging—researchers know what properties or axioms of order the data must satisfy for such a numerical representation to be appropriate. On the assumption that one or another of these representations exists, and using numerical ratings by subjects instead of ordering, a scaling technique called functional measurement (referring to the function that describes how the dependent variable relates to the independent ones) has been developed and applied in a number of domains. What remains problematic is how to encompass at the ordinal level the fact that some random error intrudes into nearly all observations and then to show how that randomness is represented at the numerical level; this continues to be an unresolved and challenging research issue.

During the past few years considerable progress has been made in understanding certain representations inherently different from those just discussed. The work has involved three related thrusts. The first is a scheme of classifying structures according to how uniquely their representation is constrained. The three classical numerical representations are known as ordinal, interval, and ratio scale types. For systems with continuous numerical representations and of scale type at least as rich as the ratio one, it has been shown that only one additional type can exist. A second thrust is to accept structural assumptions, like factorial ones, and to derive for each scale the possible functional relations among the independent variables. And the third thrust is to develop axioms for the properties of an order relation that leads to the possible representations. Much is now known about the possible nonadditive representations of both the multifactor case and the one where stimuli can be combined, such as combining sound intensities.

Closely related to this classification of structures is the question: What statements, formulated in terms of the measures arising in such representations, can be viewed as meaningful in the sense of corresponding to something empirical? Statements here refer to any scientific assertions, including statistical ones, formulated in terms of the measures of the variables and logical and mathematical connectives. These are statements for which asserting truth or falsity makes sense. In particular, statements that remain invariant under certain symmetries of structure have played an important role in classical geometry, dimensional analysis in physics, and in relating measurement and statistical models applied to the same phenomenon. In addition, these ideas have been used to construct models in more formally developed areas of the behavioral and social sciences, such as psychophysics. Current research has emphasized the communality of these historically independent developments and is attempting both to uncover systematic, philosophically sound arguments as to why invariance under symmetries is as important as it appears to be and to understand what to do when structures lack symmetry, as, for example, when variables have an inherent upper bound.

Many subjects do not seem to be correctly represented in terms of distances in continuous geometric space. Rather, in some cases, such as the relations among meanings of words—which is of great interest in the study of memory representations—a description in terms of tree-like, hierarchial structures appears to be more illuminating. This kind of description appears appropriate both because of the categorical nature of the judgments and the hierarchial, rather than trade-off, nature of the structure. Individual items are represented as the terminal nodes of the tree, and groupings by different degrees of similarity are shown as intermediate nodes, with the more general groupings occurring nearer the root of the tree. Clustering techniques, requiring considerable computational power, have been and are being developed. Some successful applications exist, but much more refinement is anticipated.

Network Models

Several other lines of advanced modeling have progressed in recent years, opening new possibilities for empirical specification and testing of a variety of theories. In social network data, relationships among units, rather than the units themselves, are the primary objects of study: friendships among persons, trade ties among nations, cocitation clusters among research scientists, interlocking among corporate boards of directors. Special models for social network data have been developed in the past decade, and they give, among other things, precise new measures of the strengths of relational ties among units. A major challenge in social network data at present is to handle the statistical dependence that arises when the units sampled are related in complex ways.

  • Statistical Inference and Analysis

As was noted earlier, questions of design, representation, and analysis are intimately intertwined. Some issues of inference and analysis have been discussed above as related to specific data collection and modeling approaches. This section discusses some more general issues of statistical inference and advances in several current approaches to them.

Causal Inference

Behavioral and social scientists use statistical methods primarily to infer the effects of treatments, interventions, or policy factors. Previous chapters included many instances of causal knowledge gained this way. As noted above, the large experimental study of alternative health care financing discussed in Chapter 2 relied heavily on statistical principles and techniques, including randomization, in the design of the experiment and the analysis of the resulting data. Sophisticated designs were necessary in order to answer a variety of questions in a single large study without confusing the effects of one program difference (such as prepayment or fee for service) with the effects of another (such as different levels of deductible costs), or with effects of unobserved variables (such as genetic differences). Statistical techniques were also used to ascertain which results applied across the whole enrolled population and which were confined to certain subgroups (such as individuals with high blood pressure) and to translate utilization rates across different programs and types of patients into comparable overall dollar costs and health outcomes for alternative financing options.

A classical experiment, with systematic but randomly assigned variation of the variables of interest (or some reasonable approach to this), is usually considered the most rigorous basis from which to draw such inferences. But random samples or randomized experimental manipulations are not always feasible or ethically acceptable. Then, causal inferences must be drawn from observational studies, which, however well designed, are less able to ensure that the observed (or inferred) relationships among variables provide clear evidence on the underlying mechanisms of cause and effect.

Certain recurrent challenges have been identified in studying causal inference. One challenge arises from the selection of background variables to be measured, such as the sex, nativity, or parental religion of individuals in a comparative study of how education affects occupational success. The adequacy of classical methods of matching groups in background variables and adjusting for covariates needs further investigation. Statistical adjustment of biases linked to measured background variables is possible, but it can become complicated. Current work in adjustment for selectivity bias is aimed at weakening implausible assumptions, such as normality, when carrying out these adjustments. Even after adjustment has been made for the measured background variables, other, unmeasured variables are almost always still affecting the results (such as family transfers of wealth or reading habits). Analyses of how the conclusions might change if such unmeasured variables could be taken into account is essential in attempting to make causal inferences from an observational study, and systematic work on useful statistical models for such sensitivity analyses is just beginning.

The third important issue arises from the necessity for distinguishing among competing hypotheses when the explanatory variables are measured with different degrees of precision. Both the estimated size and significance of an effect are diminished when it has large measurement error, and the coefficients of other correlated variables are affected even when the other variables are measured perfectly. Similar results arise from conceptual errors, when one measures only proxies for a theoretical construct (such as years of education to represent amount of learning). In some cases, there are procedures for simultaneously or iteratively estimating both the precision of complex measures and their effect on a particular criterion.

Although complex models are often necessary to infer causes, once their output is available, it should be translated into understandable displays for evaluation. Results that depend on the accuracy of a multivariate model and the associated software need to be subjected to appropriate checks, including the evaluation of graphical displays, group comparisons, and other analyses.

New Statistical Techniques

Internal resampling.

One of the great contributions of twentieth-century statistics was to demonstrate how a properly drawn sample of sufficient size, even if it is only a tiny fraction of the population of interest, can yield very good estimates of most population characteristics. When enough is known at the outset about the characteristic in question—for example, that its distribution is roughly normal—inference from the sample data to the population as a whole is straightforward, and one can easily compute measures of the certainty of inference, a common example being the 95 percent confidence interval around an estimate. But population shapes are sometimes unknown or uncertain, and so inference procedures cannot be so simple. Furthermore, more often than not, it is difficult to assess even the degree of uncertainty associated with complex data and with the statistics needed to unravel complex social and behavioral phenomena.

Internal resampling methods attempt to assess this uncertainty by generating a number of simulated data sets similar to the one actually observed. The definition of similar is crucial, and many methods that exploit different types of similarity have been devised. These methods provide researchers the freedom to choose scientifically appropriate procedures and to replace procedures that are valid under assumed distributional shapes with ones that are not so restricted. Flexible and imaginative computer simulation is the key to these methods. For a simple random sample, the “bootstrap” method repeatedly resamples the obtained data (with replacement) to generate a distribution of possible data sets. The distribution of any estimator can thereby be simulated and measures of the certainty of inference be derived. The “jackknife” method repeatedly omits a fraction of the data and in this way generates a distribution of possible data sets that can also be used to estimate variability. These methods can also be used to remove or reduce bias. For example, the ratio-estimator, a statistic that is commonly used in analyzing sample surveys and censuses, is known to be biased, and the jackknife method can usually remedy this defect. The methods have been extended to other situations and types of analysis, such as multiple regression.

There are indications that under relatively general conditions, these methods, and others related to them, allow more accurate estimates of the uncertainty of inferences than do the traditional ones that are based on assumed (usually, normal) distributions when that distributional assumption is unwarranted. For complex samples, such internal resampling or subsampling facilitates estimating the sampling variances of complex statistics.

An older and simpler, but equally important, idea is to use one independent subsample in searching the data to develop a model and at least one separate subsample for estimating and testing a selected model. Otherwise, it is next to impossible to make allowances for the excessively close fitting of the model that occurs as a result of the creative search for the exact characteristics of the sample data—characteristics that are to some degree random and will not predict well to other samples.

Robust Techniques

Many technical assumptions underlie the analysis of data. Some, like the assumption that each item in a sample is drawn independently of other items, can be weakened when the data are sufficiently structured to admit simple alternative models, such as serial correlation. Usually, these models require that a few parameters be estimated. Assumptions about shapes of distributions, normality being the most common, have proved to be particularly important, and considerable progress has been made in dealing with the consequences of different assumptions.

More recently, robust techniques have been designed that permit sharp, valid discriminations among possible values of parameters of central tendency for a wide variety of alternative distributions by reducing the weight given to occasional extreme deviations. It turns out that by giving up, say, 10 percent of the discrimination that could be provided under the rather unrealistic assumption of normality, one can greatly improve performance in more realistic situations, especially when unusually large deviations are relatively common.

These valuable modifications of classical statistical techniques have been extended to multiple regression, in which procedures of iterative reweighting can now offer relatively good performance for a variety of underlying distributional shapes. They should be extended to more general schemes of analysis.

In some contexts—notably the most classical uses of analysis of variance—the use of adequate robust techniques should help to bring conventional statistical practice closer to the best standards that experts can now achieve.

Many Interrelated Parameters

In trying to give a more accurate representation of the real world than is possible with simple models, researchers sometimes use models with many parameters, all of which must be estimated from the data. Classical principles of estimation, such as straightforward maximum-likelihood, do not yield reliable estimates unless either the number of observations is much larger than the number of parameters to be estimated or special designs are used in conjunction with strong assumptions. Bayesian methods do not draw a distinction between fixed and random parameters, and so may be especially appropriate for such problems.

A variety of statistical methods have recently been developed that can be interpreted as treating many of the parameters as or similar to random quantities, even if they are regarded as representing fixed quantities to be estimated. Theory and practice demonstrate that such methods can improve the simpler fixed-parameter methods from which they evolved, especially when the number of observations is not large relative to the number of parameters. Successful applications include college and graduate school admissions, where quality of previous school is treated as a random parameter when the data are insufficient to separately estimate it well. Efforts to create appropriate models using this general approach for small-area estimation and undercount adjustment in the census are important potential applications.

Missing Data

In data analysis, serious problems can arise when certain kinds of (quantitative or qualitative) information is partially or wholly missing. Various approaches to dealing with these problems have been or are being developed. One of the methods developed recently for dealing with certain aspects of missing data is called multiple imputation: each missing value in a data set is replaced by several values representing a range of possibilities, with statistical dependence among missing values reflected by linkage among their replacements. It is currently being used to handle a major problem of incompatibility between the 1980 and previous Bureau of Census public-use tapes with respect to occupation codes. The extension of these techniques to address such problems as nonresponse to income questions in the Current Population Survey has been examined in exploratory applications with great promise.

Computer Packages and Expert Systems

The development of high-speed computing and data handling has fundamentally changed statistical analysis. Methodologies for all kinds of situations are rapidly being developed and made available for use in computer packages that may be incorporated into interactive expert systems. This computing capability offers the hope that much data analyses will be more carefully and more effectively done than previously and that better strategies for data analysis will move from the practice of expert statisticians, some of whom may not have tried to articulate their own strategies, to both wide discussion and general use.

But powerful tools can be hazardous, as witnessed by occasional dire misuses of existing statistical packages. Until recently the only strategies available were to train more expert methodologists or to train substantive scientists in more methodology, but without the updating of their training it tends to become outmoded. Now there is the opportunity to capture in expert systems the current best methodological advice and practice. If that opportunity is exploited, standard methodological training of social scientists will shift to emphasizing strategies in using good expert systems—including understanding the nature and importance of the comments it provides—rather than in how to patch together something on one’s own. With expert systems, almost all behavioral and social scientists should become able to conduct any of the more common styles of data analysis more effectively and with more confidence than all but the most expert do today. However, the difficulties in developing expert systems that work as hoped for should not be underestimated. Human experts cannot readily explicate all of the complex cognitive network that constitutes an important part of their knowledge. As a result, the first attempts at expert systems were not especially successful (as discussed in Chapter 1 ). Additional work is expected to overcome these limitations, but it is not clear how long it will take.

Exploratory Analysis and Graphic Presentation

The formal focus of much statistics research in the middle half of the twentieth century was on procedures to confirm or reject precise, a priori hypotheses developed in advance of collecting data—that is, procedures to determine statistical significance. There was relatively little systematic work on realistically rich strategies for the applied researcher to use when attacking real-world problems with their multiplicity of objectives and sources of evidence. More recently, a species of quantitative detective work, called exploratory data analysis, has received increasing attention. In this approach, the researcher seeks out possible quantitative relations that may be present in the data. The techniques are flexible and include an important component of graphic representations. While current techniques have evolved for single responses in situations of modest complexity, extensions to multiple responses and to single responses in more complex situations are now possible.

Graphic and tabular presentation is a research domain in active renaissance, stemming in part from suggestions for new kinds of graphics made possible by computer capabilities, for example, hanging histograms and easily assimilated representations of numerical vectors. Research on data presentation has been carried out by statisticians, psychologists, cartographers, and other specialists, and attempts are now being made to incorporate findings and concepts from linguistics, industrial and publishing design, aesthetics, and classification studies in library science. Another influence has been the rapidly increasing availability of powerful computational hardware and software, now available even on desktop computers. These ideas and capabilities are leading to an increasing number of behavioral experiments with substantial statistical input. Nonetheless, criteria of good graphic and tabular practice are still too much matters of tradition and dogma, without adequate empirical evidence or theoretical coherence. To broaden the respective research outlooks and vigorously develop such evidence and coherence, extended collaborations between statistical and mathematical specialists and other scientists are needed, a major objective being to understand better the visual and cognitive processes (see Chapter 1 ) relevant to effective use of graphic or tabular approaches.

Combining Evidence

Combining evidence from separate sources is a recurrent scientific task, and formal statistical methods for doing so go back 30 years or more. These methods include the theory and practice of combining tests of individual hypotheses, sequential design and analysis of experiments, comparisons of laboratories, and Bayesian and likelihood paradigms.

There is now growing interest in more ambitious analytical syntheses, which are often called meta-analyses. One stimulus has been the appearance of syntheses explicitly combining all existing investigations in particular fields, such as prison parole policy, classroom size in primary schools, cooperative studies of therapeutic treatments for coronary heart disease, early childhood education interventions, and weather modification experiments. In such fields, a serious approach to even the simplest question—how to put together separate estimates of effect size from separate investigations—leads quickly to difficult and interesting issues. One issue involves the lack of independence among the available studies, due, for example, to the effect of influential teachers on the research projects of their students. Another issue is selection bias, because only some of the studies carried out, usually those with “significant” findings, are available and because the literature search may not find out all relevant studies that are available. In addition, experts agree, although informally, that the quality of studies from different laboratories and facilities differ appreciably and that such information probably should be taken into account. Inevitably, the studies to be included used different designs and concepts and controlled or measured different variables, making it difficult to know how to combine them.

Rich, informal syntheses, allowing for individual appraisal, may be better than catch-all formal modeling, but the literature on formal meta-analytic models is growing and may be an important area of discovery in the next decade, relevant both to statistical analysis per se and to improved syntheses in the behavioral and social and other sciences.

  • Opportunities and Needs

This chapter has cited a number of methodological topics associated with behavioral and social sciences research that appear to be particularly active and promising at the present time. As throughout the report, they constitute illustrative examples of what the committee believes to be important areas of research in the coming decade. In this section we describe recommendations for an additional $16 million annually to facilitate both the development of methodologically oriented research and, equally important, its communication throughout the research community.

Methodological studies, including early computer implementations, have for the most part been carried out by individual investigators with small teams of colleagues or students. Occasionally, such research has been associated with quite large substantive projects, and some of the current developments of computer packages, graphics, and expert systems clearly require large, organized efforts, which often lie at the boundary between grant-supported work and commercial development. As such research is often a key to understanding complex bodies of behavioral and social sciences data, it is vital to the health of these sciences that research support continue on methods relevant to problems of modeling, statistical analysis, representation, and related aspects of behavioral and social sciences data. Researchers and funding agencies should also be especially sympathetic to the inclusion of such basic methodological work in large experimental and longitudinal studies. Additional funding for work in this area, both in terms of individual research grants on methodological issues and in terms of augmentation of large projects to include additional methodological aspects, should be provided largely in the form of investigator-initiated project grants.

Ethnographic and comparative studies also typically rely on project grants to individuals and small groups of investigators. While this type of support should continue, provision should also be made to facilitate the execution of studies using these methods by research teams and to provide appropriate methodological training through the mechanisms outlined below.

Overall, we recommend an increase of $4 million in the level of investigator-initiated grant support for methodological work. An additional $1 million should be devoted to a program of centers for methodological research.

Many of the new methods and models described in the chapter, if and when adopted to any large extent, will demand substantially greater amounts of research devoted to appropriate analysis and computer implementation. New user interfaces and numerical algorithms will need to be designed and new computer programs written. And even when generally available methods (such as maximum-likelihood) are applicable, model application still requires skillful development in particular contexts. Many of the familiar general methods that are applied in the statistical analysis of data are known to provide good approximations when sample sizes are sufficiently large, but their accuracy varies with the specific model and data used. To estimate the accuracy requires extensive numerical exploration. Investigating the sensitivity of results to the assumptions of the models is important and requires still more creative, thoughtful research. It takes substantial efforts of these kinds to bring any new model on line, and the need becomes increasingly important and difficult as statistical models move toward greater realism, usefulness, complexity, and availability in computer form. More complexity in turn will increase the demand for computational power. Although most of this demand can be satisfied by increasingly powerful desktop computers, some access to mainframe and even supercomputers will be needed in selected cases. We recommend an additional $4 million annually to cover the growth in computational demands for model development and testing.

Interaction and cooperation between the developers and the users of statistical and mathematical methods need continual stimulation—both ways. Efforts should be made to teach new methods to a wider variety of potential users than is now the case. Several ways appear effective for methodologists to communicate to empirical scientists: running summer training programs for graduate students, faculty, and other researchers; encouraging graduate students, perhaps through degree requirements, to make greater use of the statistical, mathematical, and methodological resources at their own or affiliated universities; associating statistical and mathematical research specialists with large-scale data collection projects; and developing statistical packages that incorporate expert systems in applying the methods.

Methodologists, in turn, need to become more familiar with the problems actually faced by empirical scientists in the laboratory and especially in the field. Several ways appear useful for communication in this direction: encouraging graduate students in methodological specialties, perhaps through degree requirements, to work directly on empirical research; creating postdoctoral fellowships aimed at integrating such specialists into ongoing data collection projects; and providing for large data collection projects to engage relevant methodological specialists. In addition, research on and development of statistical packages and expert systems should be encouraged to involve the multidisciplinary collaboration of experts with experience in statistical, computer, and cognitive sciences.

A final point has to do with the promise held out by bringing different research methods to bear on the same problems. As our discussions of research methods in this and other chapters have emphasized, different methods have different powers and limitations, and each is designed especially to elucidate one or more particular facets of a subject. An important type of interdisciplinary work is the collaboration of specialists in different research methodologies on a substantive issue, examples of which have been noted throughout this report. If more such research were conducted cooperatively, the power of each method pursued separately would be increased. To encourage such multidisciplinary work, we recommend increased support for fellowships, research workshops, and training institutes.

Funding for fellowships, both pre-and postdoctoral, should be aimed at giving methodologists experience with substantive problems and at upgrading the methodological capabilities of substantive scientists. Such targeted fellowship support should be increased by $4 million annually, of which $3 million should be for predoctoral fellowships emphasizing the enrichment of methodological concentrations. The new support needed for research workshops is estimated to be $1 million annually. And new support needed for various kinds of advanced training institutes aimed at rapidly diffusing new methodological findings among substantive scientists is estimated to be $2 million annually.

  • Cite this Page National Research Council; Division of Behavioral and Social Sciences and Education; Commission on Behavioral and Social Sciences and Education; Committee on Basic Research in the Behavioral and Social Sciences; Gerstein DR, Luce RD, Smelser NJ, et al., editors. The Behavioral and Social Sciences: Achievements and Opportunities. Washington (DC): National Academies Press (US); 1988. 5, Methods of Data Collection, Representation, and Analysis.
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Research Article

Network representation of multicellular activity in pancreatic islets: Technical considerations for functional connectivity analysis

Roles Conceptualization, Formal analysis, Investigation, Methodology, Software, Visualization, Writing – original draft, Writing – review & editing

Affiliations Faculty of Natural Sciences and Mathematics, University of Maribor, Maribor, Slovenia, Faculty of Medicine, University of Maribor, Maribor, Slovenia

Roles Data curation, Investigation

Affiliation Department of Pediatrics, Department of Bioengineering, University of California, San Diego, La Jolla, California, United States of America

Affiliation Faculty of Medicine, University of Maribor, Maribor, Slovenia

Roles Data curation, Investigation, Writing – review & editing

Roles Investigation, Writing – review & editing

Roles Investigation, Resources, Writing – review & editing

Affiliation Department of Bioengineering, Barbara Davis Center for Diabetes, Aurora, Colorado, United States of America

Roles Conceptualization, Funding acquisition, Investigation, Validation, Writing – review & editing

* E-mail: [email protected] (AS); [email protected] (VK); [email protected] (MG)

Roles Conceptualization, Funding acquisition, Investigation, Resources, Validation, Writing – review & editing

Roles Conceptualization, Funding acquisition, Investigation, Methodology, Software, Supervision, Validation, Writing – original draft

Affiliations Faculty of Natural Sciences and Mathematics, University of Maribor, Maribor, Slovenia, Faculty of Medicine, University of Maribor, Maribor, Slovenia, Alma Mater Europaea, Maribor, Slovenia

ORCID logo

  • Marko Šterk, 
  • Yaowen Zhang, 
  • Viljem Pohorec, 
  • Eva Paradiž Leitgeb, 
  • Jurij Dolenšek, 
  • Richard K. P. Benninger, 
  • Andraž Stožer, 
  • Vira Kravets, 
  • Marko Gosak


  • Published: May 13, 2024
  • https://doi.org/10.1371/journal.pcbi.1012130
  • Reader Comments

This is an uncorrected proof.

Fig 1

Within the islets of Langerhans, beta cells orchestrate synchronized insulin secretion, a pivotal aspect of metabolic homeostasis. Despite the inherent heterogeneity and multimodal activity of individual cells, intercellular coupling acts as a homogenizing force, enabling coordinated responses through the propagation of intercellular waves. Disruptions in this coordination are implicated in irregular insulin secretion, a hallmark of diabetes. Recently, innovative approaches, such as integrating multicellular calcium imaging with network analysis, have emerged for a quantitative assessment of the cellular activity in islets. However, different groups use distinct experimental preparations, microscopic techniques, apply different methods to process the measured signals and use various methods to derive functional connectivity patterns. This makes comparisons between findings and their integration into a bigger picture difficult and has led to disputes in functional connectivity interpretations. To address these issues, we present here a systematic analysis of how different approaches influence the network representation of islet activity. Our findings show that the choice of methods used to construct networks is not crucial, although care is needed when combining data from different islets. Conversely, the conclusions drawn from network analysis can be heavily affected by the pre-processing of the time series, the type of the oscillatory component in the signals, and by the experimental preparation. Our tutorial-like investigation aims to resolve interpretational issues, reconcile conflicting views, advance functional implications, and encourage researchers to adopt connectivity analysis. As we conclude, we outline challenges for future research, emphasizing the broader applicability of our conclusions to other tissues exhibiting complex multicellular dynamics.

Author summary

Islets of Langerhans, multicellular microorgans in the pancreas, are pivotal for whole-body energy homeostasis. Hundreds of beta cells within these networks synchronize to produce insulin, a crucial hormone for metabolic control. Coordinated activity disruptions in these multicellular networks contribute to irregular insulin secretion, a hallmark of diabetes. Recognizing the significance of collective activity, network science approaches have been increasingly applied in islet research. However, variations in experimental setups, imaging techniques, signal processing, and connectivity analysis methods across different research groups pose challenges for integrating findings into a comprehensive picture. Therefore, we present here a systematic analysis of various approaches impacting results in islet activity network representation. We find that methods for constructing functional connectivity maps aren’t critical, but caution is necessary when aggregating data from different islets. Network analysis conclusions are notably influenced by factors such as time series pre-processing, the oscillatory component of signals, and experimental preparation. Despite these challenges, this paper advocates for the adoption of connectivity analysis in future islet research, emphasizing that the insights gained extend beyond pancreatic islets to provide valuable contributions for understanding connectivity in other multicellular systems.

Citation: Šterk M, Zhang Y, Pohorec V, Leitgeb EP, Dolenšek J, Benninger RKP, et al. (2024) Network representation of multicellular activity in pancreatic islets: Technical considerations for functional connectivity analysis. PLoS Comput Biol 20(5): e1012130. https://doi.org/10.1371/journal.pcbi.1012130

Editor: Jonathan Rubin, University of Pittsburgh, UNITED STATES

Received: January 3, 2024; Accepted: May 2, 2024; Published: May 13, 2024

Copyright: © 2024 Šterk et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All code written in support of this publication is publicly available at https://github.com/MarkoSterk/beta_cell_analysis_suite .

Funding: The authors acknowledge the financial support provided from the Slovenian Research and Innovation Agency (grants num. P3-3096 (AS), J3-3077 (MG), N3-0133 (AS), IO-0029), by the Burroughs Wellcome Fund Grant (grant num. 25B1756 (VK)), and from Foundation for the National Institutes of Health (grant numb. R01 DK102950 (RKPB), R01 DK106412 (RKPB). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.


Proper insulin secretion and insulin sensitivity of peripheral tissues are crucial in regulating uptake and disposal of energy rich molecules, thereby sustaining metabolic homeostasis [ 1 ]. Pancreatic beta cells constitute part of a crucial negative feedback loop, sensing changes in plasma levels of energy-rich nutrients and accordingly adjusting release of insulin into the bloodstream [ 2 ]. The cascade of cellular events connecting the changes in plasma nutrient levels with the proper insulin secretion have been studied in detail [ 3 – 10 ]. The crucial steps in the stimulus-secretion coupling cascade involve an increase in intracellular ATP concentration, closure of ATP-sensitive potassium channels, membrane depolarization, opening of voltage-activated Ca 2+ channels and an increase in intracellular calcium concentration ([Ca 2+ ] i ), leading ultimately to exocytosis of insulin-containing vesicles. Beta cell response is further modulated by homo- and heterologous cell-to-cell interactions within islets [ 11 – 14 ], by autonomous nerve control [ 15 – 17 ], and by hormones released by the gut [ 18 – 20 ]. Of importance, beta cells display complex oscillatory activity and are intrinsically heterogeneous [ 8 , 21 , 22 ], with differences observed on molecular [ 23 ], morphological [ 24 , 25 ] and functional level [ 26 , 27 ], and it is only due to strong coupling within the islets that beta cells properly respond to glucose excursions.

In a coupled system of beta cells, glucose stimulation triggers two distinct and qualitatively different phases [ 8 , 27 – 32 ]. The initial response consists of a phasic increase in activity, characterized by membrane depolarization and increase in [Ca 2+ ] i which occurs sooner in higher glucose concentrations [ 27 , 33 ]. In case that the stimulus is still present, a complex tonic activity follows. This second phase is characterized by repetitive membrane potential and [Ca 2+ ] i oscillations, as well as pulses of insulin secretion. These oscillations are not generated randomly among cells. Rather, they are phase-lagged between cells, such that waves of membrane depolarization and [Ca 2+ ] i are formed, spreading from cell to cell from different wave-initiating cells near the islet periphery [ 13 , 16 , 34 – 36 ]. An increase in glucose concentration is coded as a fractional increase in activity within a time period, termed also relative active time [ 16 , 27 , 33 ] or duty cycle. The mechanistic substrate for such cohesive functioning of beta cells is intercellular communication via gap junction channels, consisting of connexin 36 (Cx36) [ 34 , 37 – 40 ]. Cx36 provides both metabolic and electrical coupling between spatially organized heterogenous beta cells [ 34 , 39 , 41 ]. While other mechanism, such as autonomic innervation [ 42 ], autocrine and paracrine [ 43 , 44 ] signaling also contribute to cell-cell communication, Cx36 have been shown to play the main role in synchronizing beta cell collectives and maintaining proper insulin secretion [ 45 , 46 ]. Indeed, expression of Cx36 is decreased in diabetic conditions [ 47 , 48 ] leading to desynchronisation of [Ca 2+ ] i oscillations and perturbations in pulsatile insulin secretion [ 37 , 40 , 49 – 52 ]. Hence, the gap-junctional connections among beta cells are imperative for optimal beta cell function and comprehending their collective dynamics holds significance in elucidating the mechanisms underlying diabetes pathogenesis and its treatment.

Due to their highly heterogeneous nature, the presence of distinct subpopulations, and an ever-changing environment, beta cells display intricate yet coherent intercellular activity patterns [ 8 , 53 ]. Because coordinated intercellular activity is not only crucial for tightly regulated insulin secretion but is also known to be altered in diabetes, researchers are investing considerable effort in describing and studying how collective rhythmicity is established in beta cell populations and how the underlying mechanisms change in disease. In recent years, the emergence of network analyses has provided a promising tool for evaluating data obtained through advanced multicellular imaging, with the goal to objectively characterize collective activity in islets [ 16 , 42 , 54 – 58 ]. In this approach, individual cells serve as nodes, and their positions correspond to their physical locations within the tissue. The connections between cells reflect functional associations and are determined based on the temporal similarity of the measured cellular dynamics, most often [Ca 2+ ] i activity [ 56 ]. The application of network approaches has uncovered a modular organization in the functional beta cell networks, that exhibit greater heterogeneity than anticipated in a gap junction coupled syncytium. The identified indicators of small-worldness and a heavy-tailed degree distribution imply the existence of highly connected cells, called hubs [ 54 , 56 ]. Although their precise function remains somewhat enigmatic, these hubs are believed to represent a subpopulation with distinct attributes that confer upon them an above-average impact on the synchronized behavior [ 8 , 13 , 55 , 59 – 61 ]. Furthermore, the collective responses to stimulation and the mediation of intercellular signals were also found to be influenced by other beta cell subpopulations. Specifically, the first responder cells were found crucial in mediating the responses to increasing stimulation during first phase of the islet’s response [ 57 ], whilst the wave initiator cells act as triggers of intercellular signals that synchronize the cells [ 13 , 34 , 62 ], being thereby presumably implicated in the regulation of pulsatile insulin release during the second phase [ 14 , 41 ]. In recent years, advanced methodological approaches, including optogenetics, photopharmacological methods, and RNA sequencing, along with network analyses, have unveiled specific characteristics within these subpopulations [ 8 , 13 , 41 , 54 , 63 ]. Acknowledging their unique attributes and significant contribution to shaping overall islet activity, there is a growing interest in their role in diabetes development [ 45 , 54 , 64 ].For this reason, it becomes even more important to precisely define these subpopulations, and objectively determine them through network analyses.

Nevertheless, due to variations in experimental preparations, microscopic imaging techniques, the nature of recorded signals, the following signal processing techniques, and the methods for deriving functional connectivity patterns that are employed by different research groups, comparing findings and integrating them into a comprehensive bigger picture becomes challenging even for experts in islet research. Additionally, the introduction of new terminology has further contributed to disputes in data interpretation, as well as to apparent contradictions regarding functional connectivity and the role of different beta cell subpopulations, which can be in part attributed to aforementioned methodological discrepancies [ 8 , 13 , 27 , 53 , 65 – 67 ]. To at least partly address these issues, we present here a systematic analysis of how different experimental designs and computational approaches impact the results obtained from network representations of multicellular islet activity. Specifically, we analyze how the results are affected by different methods used to evaluate coordinated cellular behavior and network construction, different timescales of observed oscillatory calcium activity, different mouse strains used for tissue slice preparation, and the type of experimental preparation (i.e., tissue slices vs. isolated islets). All of the above represents some of the most prevalent genuine variations due to the diverse nature of work, experimental techniques, and the availability of equipment in laboratories worldwide.

The role of different methods for the evaluation of time series similarities

We start by examining the effect of the type of time series similarity measure used to extract functional beta cell networks. We analyzed the beta cell [Ca 2+ ] i responses to glucose stimulation obtained by means of multicellular confocal imaging in acute tissue slices from NMRI mice. The stimulatory glucose concentration was 12 mM and a 15-minute interval of sustained oscillatory activity (i.e., plateau phase) was used for the analysis, as indicated in Fig 1A . Fig 1B shows the extracted functional networks obtained by three different techniques: Pearson correlation coefficient (left panel, red), coactivity coefficient (blue, middle-left panel), and mutual information (purple, middle-right panel). A variable threshold was used so that roughly the same average node degrees ( k avg between 8 and 9) were obtained in all three networks, facilitating a robust inter-network comparison. The comparison of methods for constructing networks from similarity matrices is analyzed separately in continuation. The right-most panel of Fig 1B shows calculated network parameters. Upon visual inspection of the networks and their corresponding parameters we can see a high degree of similarity between all displayed networks. Consistent with previous findings, all the networks exhibit high levels of clustering, modularity, and small-worldness [ 16 , 27 , 56 , 68 – 70 ]. In Fig 1C and 1D the degree and edge length distributions of the same networks as in Fig 1B are presented, and Fig 1E shows the calculated internetwork similarities (see Methods section for details). All three panels further underline the observed resemblance between the networks extracted from different methods. Furthermore, the similarity in the degree distribution of all three networks suggests comparable variations in the number of functional connections, and, in addition, the level of heterogeneity indicates the presence of hub cells in all three cases.


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(A) Average signals of unprocessed (black line) and fast oscillatory activity (grey line) (upper panel) and the raster plot (lower panel) showing the binarized fast beta cell dynamics of all cells in the slice. (B) Functional networks designed with fixed average network node degrees ( k avg ≈ 8) based on three distinct time series similarity measures, and the corresponding network parameters. The correlation method (red) is represented on the left, the coactivity method (blue) in the middle, and the mutual information method (purple) on the right. Colored dots indicate physical locations of cells within islets, while grey lines represent functional connections between them. The table on the right shows the average network node degree ( k avg ), average clustering coefficient ( C avg ), modularity ( Q ), global efficiency ( E ), relative largest component ( S max ), and the small-world coefficient ( SW ) for each network. Degree distributions (C) and distribution of functional connection lengths (D) for networks presented in panel A. Boxes on panels (D) determine the 25 th and 75 th percentile, whiskers denote the 10 th and 90 th percentile, and the lines within boxes indicate the median values. E) Jaccard internetwork similarity of the three networks extracted from different methods. (F) Pairwise comparison of node degrees in all networks from panel (B). Gray dots represent the node degree of the same cells in the graphs. (G) Relative active time as a function of node degree in networks built with the correlation method (red), coactivity method (blue) and mutual information method (purple). Dots denote average values and bars denote the standard error.


To investigate the level of similarity between networks further, we present the pair-wise relationships between the node degrees of the same cells in all three constructed networks in Fig 1F . A clear relationship can be observed, with cells having a high degree in one network also having a high degree in the other. There is a consistent trend of matching connection numbers between different networks. The highest overlap is noticed between the coactivity and mutual information, as both these methods rely on binarized signals. Furthermore, previous studies have indicated that there is a tendency that the cells with many functional connections exhibit a higher-than-average activity [ 13 , 27 ]. This characteristic was alluded to when describing hub cell [Ca 2+ ] i dynamics as “preceding and outlasting that of the follower cells” in Johnston et al, 2016 [ 54 ]. As such, hub cells typically manifest durations of oscillations exceeding the average, which contributes to higher cellular activity. Nevertheless, it is crucial to emphasize that this does not inherently suggest the role of hubs as wave initiators. In other words, the cells with longest oscillations do not necessarily initiate the intercellular waves. Subsequent analyses conducted on more extensive datasets in human [ 61 ] and mouse [ 13 ] islets revealed a distinct lack of overlap between wave initiators and hubs, although they confirmed that hubs tend to have the longest oscillations. Here, we aimed to confirm whether a comparable relation between relative active time and node degrees can be obtained when employing different techniques to quantify the similarity between [Ca 2+ ] i signals. In Fig 1G the relationships between the relative active time and node degree of the same cells are depicted. For all three methods, very similar trends are observed. Specifically, there is a positive relationship between the relative active times of cells and their corresponding node degrees, which indicates that the extracted functional relationships are roughly independent of the methods used to evaluate synchronous cellular activity.

The role of different techniques used for network construction

Since no significant differences were found among various methods for evaluating intercellular synchronicity, we proceeded with the correlation method in subsequent analyses, which also has the advantage of not requiring signal binarization. In Fig 2 we explore the influence of network construction methods on the functional beta cell network structure and their relationship with the cellular activity parameter. Fig 2A displays functional networks of three different islets (rows) constructed using a fixed similarity threshold (left column), fixed average node degree (middle column), and the multilayer minimum spanning tree (MST) technique (right column). Due to the differences in [Ca 2+ ] i signals, such as differences in overall activity, nature of [Ca 2+ ] i signals (e.g., fast electrical vs. slow metabolic oscillations, see below for more details), presence of noise, etc., the average node degrees of networks constructed with the fixed similarity thresholds can vary greatly (between 4.4 and 17.5 in the three islets analyzed, see red networks in Fig 2A ), whereas the average degrees for the other two techniques are fixed around 8 (see blue and purple networks in Fig 2A ). In Fig 2B, 2C and 2D , we present the relationships between the relative active times for the data pooled from all three islets. Notably, a positive correlation between node degree and relative active time is inferred only for the variable threshold, i.e., a fixed average degree, and multilayer MST techniques. This tendency of hub cells exhibiting higher-than-average activity is in accordance with previous reports. In contrast, this relationship is apparently blurred for the fixed threshold technique due to variations in the number of connections along with differences in intrinsic activities among different islets, and hence even an opposite trend can be obtained, despite the fact that the relation is positive in all individual islets.


(A) Networks of three different islets (rows) constructed with three distinct construction methods (columns) with indicated average network node degrees ( k avg ). Construction methods are fixed correlation threshold (left), fixed average network node degree (middle), and multilayer minimum-spanning tree (right). (B-D) Relative active time of cells as a function of the relative node degree for networks constructed with the fixed correlation threshold method (B; red), fixed average network node degree method (C; blue) and multilayer minimum-spanning tree method (D; purple). Dots indicate the average values of cells within the same degree intervals and the error bars the corresponding SE. Note that the node degrees were normalized to facilitate the comparison of different islets.


To further illustrate the challenge of pooling data from different islets, we present experimental data in Fig 3 , where islets were subjected to stimulatory glucose in the first interval and subsequently treated with the GLP-1 receptor agonist exendin-4 (Ex-4) in the second interval. The precise protocol and the average unprocessed Ca 2+ signal of all cells is shown in Fig 3A . Previous research has demonstrated that Ex-4 increases the density of functional beta cell networks [ 71 ], and our results validate this observation under both fixed threshold R th and fixed average degree k avg methods ( Fig 3B ). However, the fixed R th approach encapsulates substantial heterogeneity among islets, obscuring the effects of Ex-4 stimulation when aggregating data across multiple islets, despite discernible trends at the individual islet level. In such scenarios, employing a fixed k avg technique proves to be a more appropriate option for analyzing network differences induced by the pharmacological agent. Specifically, utilizing a variable threshold to maintain a consistent average degree in the initial interval mitigates inter-islet heterogeneity. Subsequently applying the same threshold in the second interval enables an unbiased assessment of the pharmacological intervention, normalized by the network characteristics observed in the first interval of each respective islet. This not only facilitates a robust comparison of network parameters across different islets but also ensures a more accurate statistical evaluation, as demonstrated in Fig 3C .


A) Average Ca 2+ signals from all cells within a representative islet are depicted, stimulated with 10 mM glucose and 10 mM glucose + the GLP-1 receptor agonist exendin-4 (Ex-4), with specified intervals for network analysis. B) Functional networks were constructed for two intervals (Interval 1: 10 mM glucose only; Interval 2: 10 mM glucose + 20 nM Ex-4) across two different islets, utilizing two distinct network design techniques (fixed R th = 0.75 and fixed average degree with k avg (Int. 1) = 8). The use of the fixed R th method resulted in significant variations in network density between the two islets, complicating the comparison of network metrics. Conversely, fixing the average degree in Interval 1 normalized inherent differences in overall coherence of intercellular Ca 2+ activity, facilitating the assessment of the pharmacological manipulation effect across different islets. C) To illustrate the issue posed by the fixed R th method and the resulting high disparities in network densities, we compared the pooling of data from 10 islets subjected to closely matched protocols using both thresholding techniques (i.e., fixed R th and fixed k avg ). While both methods revealed a denser network in Interval 2 in response to Ex-4, these differences were almost completely masked by inter-islet variability inherent in the fixed R th method. In such scenarios, normalizing the average degree proves to be the superior approach, as it facilitates a robust evaluation of data from different islets. Data used for this analysis is from Ref [ 71 ].


It is worth noting, however, that in some cases, the fixed R th method is the more suitable choice. It is known that with gradually increasing glucose concentration, both activity and intercellular communication levels increase, leading to denser functional networks in these cases [ 72 ]. When using fixed k avg or multilayer MST methods, which impose a specific number of connections regardless of the nature of activity, such networks do not differ in the number of connections, which is incorrect in these scenarios. Moreover, under conditions of low stimulation, an unjustifiably large number of connections is obtained due to, for example, a low threshold, which does not reflect correlations in dynamics but rather random associations. Such an example is illustrated in S1 Fig .

The role of the mouse strain used in tissue slice preparation

Laboratory mice are a vital source of islets of Langerhans in beta cell physiology research; however, various laboratories employ various mouse models. Previous research has indicated that there is a considerable phenotypic variation between different mouse strains as well as substrains of the inbred strains [ 73 – 75 ] which manifest themselves also in beta cell responses to glucose and [Ca 2+ ] i signalling characteristics [ 33 , 76 ]. For that reason, we investigate here whether the functional beta cell network structure extracted from multicellular [Ca 2+ ] i recordings in tissue slices depends on the mouse strain. To this purpose, we compared the beta cell networks from outbred NMRI mice and inbred C57BL/6J mice. We used the correlation method to evaluate similarity between [Ca 2+ ] i signals and the fixed average degree method ( k avg = 8) to construct networks. In all recordings we used a 6-10-6 mM glucose protocol, as presented in Fig 4A . Intervals of 10–20 min sustained activity in the plateau phase were then used for the analysis. In Fig 4B we show typical networks from both strains, which, upon visual inspection, exhibit a rather similar topological organization. To provide a more detailed and quantitative insight, we computed various network metrics from pooled data from multiple islets. Results in Fig 4C, 4D and 4E indicate that the edge length, clustering coefficient, and degree distributions are very similar. Furthermore, the computation of network parameters presented in Fig 4F has revealed that beta cell networks from different mouse strains exhibit a similar degree of functional segregation, efficiency, and small-worldness; none of the results were identified as significant.


(A) Average signals of unprocessed and fast oscillatory activity and the raster plot showing the binarized fast beta cell dynamics of all cells in slices from NMRI mice islets (upper panel, blue) and C57BL/6J mice islets (lower panel, purple). (B) Functional networks derived from representative recordings in islet from NMRI (blue, upper panel) and C57BL/6J (purple, lower panel) mice. (C) Edge length distributions, (D) clustering coefficient distributions, and (E) node degree distributions from a pooled data set from NMRI (blue) and BL6J (purple) mouse recordings. (F) Network parameters for extracted networks from NMRI and BL6J mouse recordings: modularity (left), relative largest component (middle left), global efficiency (middle right), and small-worldness coefficient (right). Dots represent values of individual recordings with horizontal lines indicating median values. Boxes on panels (B) and (C) determine the 25 th and 75 th percentile, whiskers denote the 10 th and 90 th percentile and the lines within boxes indicate median values. Data was pooled from islets/cells: 6/779 (NMRI), 6/617 (C57BL/6J). In all recordings, the islets were stimulated with 10 mM glucose and 10–20 minute intervals in the plateau phase were used for the analysis.


The role of different time scales of oscillatory [Ca 2+ ] i activity and time series preparation

Next, we investigate how the type of oscillatory activity and signal preparation impact the functional beta cell network topology. To this purpose, we performed prolonged multicellular imaging in tissue slices from NMRI mice. Fig 5A displays an average [Ca 2+ ] i signal of all cells in a representative islet under stimulation with 8 mM glucose. Three different temporal traces are presented: the unprocessed (i.e., raw recorded) signal (top, red), the filtered slow oscillations (middle, purple), and the filtered fast oscillations (middle, blue).The fast and slow oscillations principally represent the electrical and metabolic activity of cells, respectively [ 77 ]. The lower panels in Fig 5A feature raster plots depicting binarized activity of the slow and fast oscillatory component. Notably, both types of oscillatory activity exhibit distinct, regular patterns. In Fig 5B we present correlation-based functional networks constructed with the fixed average degree technique for the three distinct signal types. A visual assessment points out a clear difference between the three extracted networks. The fast oscillation-based network (middle panel) exhibits shorter edge lengths and a more clustered, localized, structure, while the slow oscillation-based network (right panel) shows more long-range edges and a less clustered structure. A quantitative assessment of the networks confirms the observed differences. The slow oscillatory component network is more heterogeneous, less clustered and exhibits longer functional connections ( Fig 5C, 5D and 5E ). The reason for this is in the type of cellular dynamics the networks encode. The fast oscillations are representative of the electrical activity of cells, which is mediated by gap-junction-driven intercellular waves and thus contributes to the shorter, more clustered network structure which is quite similar to the underlying physical network. On the other hand, the slow component signal is associated with cellular metabolism which is to a greater extent affected by the similarity of intrinsic metabolic characteristics of cells and less by cell-to-cell coupling [ 56 , 70 , 78 , 79 ]. Interestingly, the raw-signal-derived functional network appears to be poised in between, which is somehow expected, as it encompasses both types of oscillatory activity. To evaluate the properties of different networks further, we quantified the extracted functional connectivity patterns using conventional network metrics ( Fig 5F ). The results indicate that the networks derived from different dynamical components have comparable values of the small-world coefficient and the relative largest component, but there are profound differences in modularity and global efficiency. Namely, the fast oscillations-derived network is more segregated and exhibits lower efficiency, primarily due to the less pronounced long-range connections. Moreover, we present in Fig 5G the relationship between the relative active time of cells and their corresponding node degrees in all three types of networks. The tendency of hub cells being the most active is most pronounced in the case of fast oscillations, whereas the relation is less apparent for the raw and slow component. Notably, the latter aligns with recent theoretical predictions [ 78 ]. Finally, we assess the similarities between the three networks and present in Fig 5H the pair-wise relationships between the node degrees in different networks. The results indicate that the strongest relation exists between the fast and raw oscillatory signals, while the relationship is the weakest between the fast and the slow component. To investigate this in further detail, we quantified the overlap between different networks, including the hypothesized structural network that was modeled as a geometric network in which nearby cells are connected. From Fig 5I , we can observe a substantial similarity in both inter-network similarity and overlap of hub cells between the unprocessed signal and the signals of both oscillatory components, with a higher level of similarity observed in the fast component. This is expected in signals from slices, as the fast component is very pronounced. However, the key point is that the highest level of similarity between the structural network and the functional networks is obtained from fast oscillations, while the similarity between the structural and slow networks is substantially lower. Similarly, the connection between the fast and slow component-derived networks is relatively low, as previously indicated by the results in Fig 5H . These quantitative results can be further visually assessed with the illustrations in S2 Fig , depicting all four types of networks for all 5 islets included in the analysis. It can be observed that the networks of unprocessed signals and signals of the slow component contain many long-range connections, while those in the fast component network are significantly fewer, making it visually more similar to the structural network. Importantly, fast oscillations may be more strongly determined by slow oscillations, such as in the case of compound oscillations [ 80 , 81 ]. Such an example is depicted in S3 Fig and in this case, the functional network based on slow oscillations rather than fast oscillations is most similar to the functional network based on the raw signal. However, the functional network based on fast oscillations remains the one that is most similar to the structural network ( S3B and S3C Fig ).


(A) Unprocessed (red), fast-component only (blue), and slow-component only (purple) average [Ca 2+ ] i signal of all cells in the islet from acute tissue slice from NMRI mouse. The lower panels display raster plots that represent the binarized activity of the slow and fast oscillatory components. (B) Functional networks designed based on raw cellular signals (left), fast-component only signals (middle), and slow-component only signals (right). Networks were constructed with the fixed average network node degree method ( k avg ≈8.0) based on time series correlations as the similarity measure. Distribution of node degrees (C), clustering coefficients (D) and functional connection lengths (E) for the three networks presented in panel B. (F) Network parameters extracted from functional connectivity maps derived from different oscillatory components. (G) Relative active time of cells as a function of their corresponding node degrees in networks constructed based on raw signals (red), fast-component only signals (blue) and slow-component only signals (purple). Colored dots represent average values of cells within the same degree intervals and the error bars denote SE. Individual values were normalized by the average value of the relative active time within the given islet so to ease comparison between different islets. (H) The pairwise relationships between node degrees in different networks. The grey dots denote values from individual cells and the black line indicates the linear fit, whereby R 2 indicates goodness-of-fit. I) Similarity between different types of networks (left) and the relative overlap of hub cells (right), identified as the top 1/6 of the most connected cells. The structural networks were modeled as equivalent geometric networks, in which nearby cells are deemed connected (see Materials and Methods and S4 Fig ). Boxes in panels (D) and (E) determine the 25 th and 75 th percentile, whiskers denote the 10 th and 90 th percentile and the horizontal lines within boxes indicate the median values. Dots in panel (F) indicate the values from individual islets and the horizontal line denote the median. Stars denote statistical differences; *p<0.05,**p<0.01. Data presented in panels (F-I) is based on 5 different islets.


Functional connectivity networks in isolated islets

In addition to acute tissue slices, isolated islets play a prevalent role in beta cell research, including in the context of collective activity network analyses. Thus, we proceed with analyzing the nature of multicellular dynamics and the underlying functional networks within islets isolated from C57BL/6J mice. In Fig 6A , we present the responses of a representative isolated islet upon transitioning from 2 mM to 11 mM glucose. The cells exhibit an initial, profound elevation in [Ca 2+ ] i levels, followed by the emergence of coordinated [Ca 2+ ] i oscillations after approximately 8–10 minutes. The raster plots indicate that these oscillations frequently span the entire islet. The functional network extracted from the phase of sustained oscillatory activity, constructed based on time series correlation as the similarity measure along with the fixed average network node degree method, is shown in Fig 6B . The characterization of beta cell networks was based on 5 different isolated islets subjected to the same protocol. In the table shown in Fig 6C the average values of network parameters are provided and Fig 6D shows the pooled degree distributions. We can observe that the topological parameters of networks from isolated islets do not differ much from those in slice-based networks: they are quite modular and exhibit features of small-world networks. However, upon visually evaluating the network illustrated in Fig 6B and considering the characteristics of clustering coefficient ( Fig 6E ) and functional connection length distributions ( Fig 6F ), it becomes evident that the networks observed in isolated islets exhibit properties that are more similar to the networks characterized by slow activity in slices. Note that for comparison the data on fast and slow activity-derived networks from slices from C57BL/6J mice are provided separately. For this comparison, the same dataset was used as in Fig 4 , where also the stimulatory glucose concertation was similar (i.e., 10 mM). In contrast to fast oscillation-based networks in slices, isolated islet networks manifest a higher efficiency, a reduced modularity, and low clustering coefficient values. Moreover, the distribution of relative connection lengths indicates that there is a larger fraction of long-range connections in isolated islets. All these attributes can be observed in slow oscillation-based networks in slices. Notably, within isolated islets, a discernible trend emerges where cells with an increased number of functional connections consistently demonstrate higher relative active times—reminiscent of the observed behavior in slices—regardless of the temporal aspect ( Fig 6G ).


(A) Average [Ca 2+ ] i signal of a representative isolated islet recording with indicated plateau phase for signal analysis (upper panel) and corresponding binarized oscillatory activity of all cells in the recording (lower panel). (B) Extracted functional network based on cellular signals in panel (A) constructed with the fixed average network node degree method with an average network node degree k avg ≈8.0. Green dots represent physical locations of cells within the islet and grey lines indicate functional connections between them. (C) Extracted average functional network parameters: average network node degree ( k avg ), average clustering coefficient ( C avg ), modularity ( Q ), global efficiency ( E ), average shortest path length ( L avg ), small-world coefficient ( SW ), and relative largest component ( S max ). Degree distributions of all extracted functional networks (D), and corresponding distributions of clustering coefficients (E), and relative edge lengths (F). To ease comparison between different islets, the physical lengths of connections were normalized with the average distance to the eight nearest neighbors. Additionally, in panels (D-F) data illustrating network attributes derived from fast and slow activities in slices from C57BL/6J mice are presented for comparison. (G) Relative active time as a function of node degree for all extracted functional networks. Boxes on panels (E-F) determine the 25 th and 75 th percentile, whiskers denote the 10 th and 90 th percentile, and the lines within boxes indicate the median values. Dots in panel (G) represent average values and vertical bars denote the standard error. Data for panels (C-G) for isolated islets was pooled from islets/cells: 5/468 and for slices the same dataset was used as in Fig 4 (islets/cells: 6/617). *p<0.05,**p<0.01, ***p<0.001.


Furthermore, to further assess the differences and similarities between beta cell networks from slices and isolated islets and how they relate to different types of oscillatory activity, we present in S3 Fig an analysis of an isolated islet where the fast component of oscillations was relatively well present, which is frequency-wise highly comparable to that in tissue slices. This enabled the separate consideration of individual oscillatory components, and similarly to tissue slices, it was found in this case as well that there is a significant similarity between the structural network and the functional network obtained from the fast component, while the similarity between the slow and structural is considerably lower. It is also worth mentioning that in isolated islets, there is much greater similarity between networks derived from unprocessed signals and the slow component, whereas in tissue slices, there is greater similarity between networks based on unprocessed signals and the fast component. The reason for this is that in tissue slices, fast oscillations are the more dominant type of signal, while in isolated islets, slow oscillatory activity prevails.

Functional connectivity analysis is a powerful tool applicable to studying the interactions between different components in a plethora of real-life systems. In recent years, it is becoming increasingly more popular to describe interactions between individual cells, particularly within the islets (for review see [ 56 ]). However, due to relatively demanding computational approaches, encompassing both data extraction and subsequent analyses of coordinated functioning, obtaining patterns of functional connectivity is not straightforward and can easily become ambiguous. In neuroscience, where the greatest progress in this field has been made, it has become evident that objectively assessing connectivity patterns is challenged by various objective reasons tied to experimental variations and computational methodologies, such as thresholding techniques [ 82 – 84 ], techniques used for data pooling [ 85 , 86 ], number of sensors used to record brain activity [ 87 , 88 ], and the selection of frequency intervals [ 89 , 90 ]. Most importantly, similar issues are witnessed in the network-based analysis of spatiotemporal cellular dynamics in islets. More specifically, different research groups employ diverse experimental techniques and preparations leading to discrepancies in types of oscillatory signals and the multicellular activity is recorded at varying spatial and temporal resolutions. There are also variations in how recordings are preprocessed before network analysis, as well as in the techniques used for the analysis itself. These, along with some terminological discrepancies in the scientific literature, are the primary reasons why we chose to investigate how various factors influence network analyses and their interpretation.

First, we evaluated the role of metrics that are used for the evaluation of synchronized activity between the measured cellular dynamics. We compared three different methods, namely one that is based directly on the recorded [Ca 2+ ] i activity (Pearson’s correlation), and two that are based on binarized time series (coactivity and mutual information). It turned out that irrespective of the method used to quantify synchronous behavior, similar networks are obtained, characterized by small-worldness, modularity, high degree of clustering, a heavy-tailed degree distribution which indicates the presence of hub cells, and a similar relation between the relative active time and the node degree (see Fig 1 ). Another crucial aspect in the process of extracting functional connectivity maps involves the thresholding of similarity matrices. As highlighted in Figs 2A and 3B , utilizing a fixed threshold can yield significant disparities among different islets, potentially introducing biases into the relations drawn from aggregated data. To mitigate this concern, using a variable threshold and a fixed average degree proves advantageous. With this approach we can firmly evaluate the effect of pharmacological interventions or extract the relations between network and classical physiological parameters when data is pooled from multiple islets, as a variable threshold can mask the inter-islet heterogeneity (see Figs 2C and 3 ). Specifically in multi-phase experiments, where consecutive intervals have to be analyzed [ 71 , 91 ], application of a variable threshold has proven beneficial, as it overcomes inter-islet variability. For example, by establishing the variable threshold based on the first interval, thereby maintaining a fixed average node degree, one can consistently apply the same threshold to construct networks during the second interval. This normalization procedure facilitates an objective assessment of alterations in islet network structure, despite inherent differences in networks from different islets ( Fig 3 ). It is important to note, however, that this method has a limitation: its fixed average number of connections prevents it from capturing the variations in overall synchronicity that are depicted by the network density. For instance, it is known that an increase in glucose concentration leads to increased and more global spatiotemporal activity, resulting in denser functional networks [ 27 , 72 ]. If a fixed average degree is then employed, these differences become obscured, and in conditions of low stimulation, numerous connections emerge that lack statistical significance. This occurs because, with a low threshold, these connections predominantly signify random associations rather than synchronized activity ( S1 Fig ). In this study we have also introduced a third option encompassing the construction of functional networks through a multilayered MST. A notable advantage of this method lies in its absence of explicit thresholding, with the singular free parameter being the number of layers, which in turn specify the average degree. Nonetheless, the drawback of the minimum spanning tree method is that it enforces at least one connection to each cell (or more in case of multilayered MST), so that even the cells which are completely desynchronized can have a comparable number of functional connections as an average cell. Therefore, while the method is attractive for its apparent objectivity, its appropriateness diminishes when the signals are rather heterogeneous and if there are subpopulations of cells whose dynamics are weakly or not at all correlated with the rest of the cells (such as those of alpha cells, see S4 Fig ). To sum up at this point, the choice of the best method to construct networks is not always straightforward and may depend on the context, i.e., both the experimental protocol and the parameters we want to objectively describe through network analysis. In doing so, we must, of course, be aware of the strengths and weaknesses of different approaches.

In previous studies, variations in glucose-induced [Ca 2+ ] i activity among different mouse strains and substrains have been reported. Compared to outbred NMRI mice, cells from the inbred C57BL/6J and C57BL/6N substrains show a rightward shift in activations and earlier deactivations. In addition, during the plateau phase, the encoding mechanisms to enhance calcium activity in response to glucose differ quantitatively in all three groups [ 33 ]. Secretagogues other than glucose also cause [Ca 2+ ] i oscillations to vary greatly [ 76 ]. Generally, however, there are similarities between C57BL/6J, C57BL/6N, and NMRI mice in the sense that all three groups showed glucose-dependent activation and deactivation responses, as well as a 3% increase in relative active time per millimole of glucose [ 33 ]. Notably, up until now, differences between strains of mice at the level of multicellular activity have not been studied. In this study, we addressed these questions using network analyses and found that the functional networks of islets in different mice are structurally very similar. Apparently, the mechanisms that coordinate fast oscillatory activity across the islets from NMRI or C57BL/6N mice, i.e., gap-junction mediated depolarization and [Ca 2+ ] i waves, are the same and do not differ between mouse strains.

In response to glucose and many other secretagogues, electrical activity, intracellular calcium, and insulin secretion oscillate in synchrony at two different time scales [ 92 , 93 ]. The first are the so-called metabolic or slow oscillations with a frequency of around 0.1–0.2 min -1 , and the second the so-called electrical or fast oscillations with a frequency of around 1–5 min -1 [ 53 , 94 ]. Noteworthy, fast oscillations show variations and have the highest frequency rates around the peaks of the slow component and the lowest around the nadirs [ 77 , 95 , 96 ]. Additionally, the relative active time or duty cycle of the fast component characteristically increases with increasing stimulation, whereas the frequency of slow oscillations remain unaltered [ 27 , 33 , 96 – 98 ]. According to the recent metronome model of beta cell function, slow oscillations set the pace for insulin pulses, whereas the fast oscillations fine-tune their amplitude [ 94 ]. Both slow and fast oscillations are phase-locked between different beta cells within a given islet by means of intercellular waves [ 14 , 34 , 35 , 55 , 62 , 68 ]. In accordance with this, the average correlation between calcium traces of different cells from the same islet decreases with intercellular distance for both the slow and the fast component, implying that intercellular coupling mediates the synchronicity of both types of oscillations [ 78 , 96 ].

If one constructs and compares functional connectivity maps for the raw signal and both dynamic components separately ( Fig 5 ), the distributions of node degrees do not differ significantly. However, the networks of fast oscillatory activity are more locally clustered and segregated, more modular, and have lower average edge lengths and global efficiency, while the slow oscillations are principally more global, resulting in numerous long-range connections and consequently a more cohesive structure that shows a lower modularity and higher global efficiency. Importantly, for the raw signal, it seems that except for the node degree, most of the network measures are more strongly determined by the slow component [ 56 ]. A logical consequence of the abovementioned differences in functional network structure is the finding that there is a relatively weak correlation between the fast and slow network layer [ 96 ], implying that different synchronization principles are at work [ 70 , 78 ], and one should not directly compare results of studies relying on fast oscillations with the ones relying on slow oscillations. Importantly, even with the same experimental model, e.g., isolated mouse islets, and set of analytical tools applied to extracting and analyzing [Ca 2+ ] i oscillations, islets with preponderance of fast, mixed or slow oscillations might coexist [ 99 – 101 ], and in this case, data should not be simply pooled, since this may obscure relevant biological differences, but analyzed for the two temporal components and for oscillatory phenotypes separately. Extrapolating this reasoning further, the caveats we pointed out in this paragraph should also be kept in mind when comparing experimental traces from different animal models, even when using the same experimental approach and the same set of analytical tools. For instance, the presence and relative importance of fast and slow oscillations may vary between beta cells from zebrafish [ 55 , 102 ], mice [ 100 , 103 ], rats [ 104 , 105 ], sand rats [ 106 , 107 ], pigs [ 108 , 109 ], and humans [ 61 , 110 ], to name only a few. To facilitate interspecies comparison, future studies shall clearly specify the type of oscillations they are addressing. Finally, at present, it is difficult to experimentally compare the relationship between the structural networks of beta cells and their functional counterparts, but modelling studies suggest that the intricate structure of functional beta cell networks based on fast and slow oscillations may be at least partly explained by heterogeneity in beta cell activity and heterogenous intercellular coupling [ 68 , 70 , 78 ].

Different groups that employ network measures in their analyses typically use different experimental approaches to obtain [Ca 2+ ] i traces. While most groups use cultured isolated islets in combination with CCD camera-based or confocal imaging, some use the acute tissue slices in combination with confocal imaging. To be able to compare findings from different groups or combine them into a coherent bigger picture of islet network properties, these differences also need to be addressed as they are an important possible systematic confounding variable. Essentially, the methodology and experimental setup would not seem to be key parameters if they did not entail differences in the nature of the oscillatory signals. In tissue slices fast or mixed oscillations are more predominant (see Figs 4A or 5A ), whereas in isolated islets the slow oscillations are predominant (see Fig 6A ). Here, we explicitly demonstrated that the distinct nature of oscillations leads to different functional beta cell networks. While some network properties in fast-derived and slow-derived networks are similar, such as heterogeneity and small-worldness, they fundamentally differ from each other, and the significance of certain subpopulations in one network is therefore not equivalent to that in the other network. Moreover, even if oscillations qualify as fast, in isolated islets, they are typically longer than 10 seconds at concentrations > 10 mM glucose [ 34 , 92 , 99 ], whereas in slices, they tend to be shorter than 10 seconds [ 16 , 27 , 33 , 111 ]. The exact mechanism behind these differences remains to be explained, but in addition to possible differences in ionic composition and the presence of additional secretagogues in the extracellular fluid that can affect the patterns of oscillations [ 92 , 112 , 113 ], the mechanical and enzymatic stress during preparation of isolated islets [ 114 , 115 ], as well as culture conditions and duration [ 99 , 116 ] have been put forward as possible sources of these differences. More specifically, alpha cells have been suggested as a potential source of local proglucagon peptides [ 117 ]. They are primarily situated in the mantle of pancreatic islets in mice, and this outer region is particularly susceptible to damage during the islet isolation process, potentially resulting in the loss of alpha cells during islet preparation. Given that both glucagon and GLP-1 have been shown to elevate the frequency of oscillations in beta cells, the diminished intra-islet alpha cell signalling could be a contributing factor to the observed decrease in beta cell oscillatory frequency in isolated islets [ 71 , 91 , 118 , 119 ]. Further, there may be a run-down of certain ion channels and changes in the expression [ 120 , 121 ] with time, which obviously impact the identity and physiology of beta cells in the cultured isolated islets more than in the immediately used islets in slices [ 122 , 123 ]. This theory is at least partly supported by the finding that oscillations in mouse islets cultured for less than one day closely resemble oscillations in non-cultured islets [ 103 , 124 ] and in islets studied in vivo [ 125 , 126 ] or rapidly after the death of the animal [ 92 ], as well as the oscillations in tissue slices [ 27 ]. Until the influence of the above factors is fully understood, we can provide at least two practical suggestions. First, studies on isolated islets and tissue slices should always exactly state what the composition of the extracellular fluid was, and which type of oscillations were used for the network analyses, as well as provide details about the basic characteristics of these oscillations, i.e., their frequency and duration. Second, freshly microdissected islets or islets cultured for shorter time periods may yield results that are more closely comparable with results from tissue slices. Finally, the above advice also applies for studies utilizing yet other experimental preparations, such as in vivo imaging of isolated and transplanted mouse islets in the anterior chamber of the eye [ 127 ] and islets from other species, as mentioned in the preceding paragraph. In the present study, we used a range of different stimulatory concentrations. They are not intended to illustrate possible glucose-dependencies of different physiological and network metrics as these are covered elsewhere [ 13 , 27 , 33 ], but to demonstrate that the analytical tools work robustly across a range of frequently used stimulatory conditions. Given that the slow oscillations are rather glucose-insensitive in terms of their frequency in both slices [ 96 ] and islets [ 98 ] and that fast oscillations have comparable dose-response relationships in slices [ 27 , 33 ] and isolated islets [ 128 – 130 ], we believe that the different concentrations we used did not introduce any critical bias and that most of our findings are applicable to concentrations beyond the range used here.

In conclusion, we would like to stress that the scope of network analyses has, in recent years, been extended to investigate intercellular interactions and functional connectivity patterns in different types of tissues. These encompass various kinds of neural assemblies [ 131 ], pituitary endocrine cells [ 132 , 133 ], astrocytes [ 134 ], yeast cells [ 135 ], distinct epithelial cell types [ 136 , 137 ], acinar cells [ 138 ], and hepatocytes [ 139 ]. As such, the insights we present herein hold relevance for comprehending the intricacies of collective cellular activity across diverse contexts, where the assessment of multicellular dynamics can be achieved through suitable imaging techniques. Moreover, in tandem with advancements in imaging methods, which are expected to soon enable the simultaneous high-resolution assessment of multiple variables defining multicellular activity, potentially even in three dimensions, it is imperative to stay attuned to progress on the computational front. Over recent years, a plethora of sophisticated methods has emerged for evaluating dynamic interactions within complex systems, such as multilayer networks [ 140 , 141 ], detection of higher-order interactions [ 142 , 143 ], information-theoretic metrics describing causal relationships [ 144 , 145 ], and deep learning-based methods [ 146 , 147 ]. These approaches hold substantial potential for further and more profound research, extending even into the realm of multicellular systems, as already demonstrated by some recent studies [ 13 , 56 , 148 – 150 ]. We strongly believe that future progress in this field will rely on such interdisciplinary endeavors that combine cutting-edge experiments with innovative computational procedures. Along these lines, we anticipate a deeper understanding of how heterogeneous populations of interacting cells, placed within a dynamic and noisy environment, operate to ensure proper functionality, and how the regulatory mechanisms are altered in disease.

Materials and methods

Ethics statement.

We conducted the study in strict accordance with all national and European recommendations on care and handling experimental animals, and all efforts were made to minimize the suffering of animals. Mice were used under protocols approved by the University of Colorado Institutional Animal Care and Use Committee (IACUC Protocol number: 00024) and The Administration of the Republic of Slovenia for Food Safety, Veterinary and Plant Protection (permit numbers: U34401-35/2018-2).

Animals and [Ca2+]i imaging in tissue slices

Slice preparation..

C57Bl6J and NMRI male and female mice were held in a temperature-controlled environment with a 12 h light/dark cycle and given continuous access to food and water. Preparation of mouse-derived acute pancreas tissue slices was executed as described previously in full [ 122 ]. In brief, after sacrifice with CO 2 and cervical dislocation, the abdominal cavity is accessed via laparotomy and the papilla Vateri is clamped. 1.9% Low melting agarose dissolved in ECS containing (in mM) 125 NaCl, 26 NaHCO3, 6 glucose, 6 lactic acid, 3 myo-inositol, 2.5 KCl, 2 Na-pyruvate, 2 CaCl2, 1.25 NaH2PO4, 1 MgCl2, 0.5 ascorbic acid is heated to 40°C and injected through the bile duct. The pancreas is cooled with ice-cold ECS, extracted, and cut into tissue blocks, which are embedded in low melting point agarose and cut with a vibratome (VT 1000 S, Leica) to yield 140 μm slices. The slices are kept in HEPES-buffered saline (HBS) consisting of (in mM) 150 NaCl, 10 HEPES, 6 glucose, 5 KCl, 2 CaCl2, 1 MgCl2; titrated to pH = 7.4 with 1 M NaOH at room temperature and stained with a HBS staining solution containing 7 μM Calbryte 520 AM (AAT Bioquest), 0.03% Pluronic F-127 (w/v), and 0.12% dimethyl sulfoxide (v/v) for 50 min at room temperature. All chemicals were obtained from Sigma-Aldrich (St. Louis, Missouri, USA) unless stated otherwise. Individual tissue slices were placed into the recording chamber and used for one stimulation protocol. The recording chamber was continuously perifused with carbogenated ECS containing 6 mM glucose heated to 37°C at basal conditions. At 20–40 minutes, the perifusion was manually changed to stimulatory (8–12) mM glucose before it was returned to the basal glucose concentration.

Beta cell calcium dynamics were imaged using an upright confocal microscope system Leica TCS SP5 AOBS Tandem II with a 20X HCX APO L water immersion objective, NA 1.0, and an inverted confocal system Leica TCS SP5 DMI6000 CS with a 20X HC PL APO water/oil immersion objective, NA 0.7 (all from Leica Microsystems, Germany). A 488 nm argon laser was used to excite the fluorescent dye, and a Leica HyD hybrid detector operating in the 500–700 nm range was used to detect the fluorescence that was released (all from Leica Microsystems, Germany), as previously described [ 27 , 122 ]. The resolution used for time series acquisition was 512 X 512 pixels with a frequency of 2–10 Hz.

[Ca 2+ ] i imaging in isolated islets

Islet isolation and culture..

Islets were isolated from mice under ketamine/xylazine anaesthesia (80 and 16 mg/kg) by collagenase delivery into the pancreas via injection into the bile duct. The collagenase-inflated pancreas was surgically removed and digested. Islets were handpicked and planted into the glass-bottom dishes (MatTek) using CellTak cell tissue adhesive (Sigma-Aldrich). Islets were cultured in RPMI medium (Corning, Tewksbury, MA) containing 10% fetal bovine serum, 100 U/mL penicillin, and 100 mg/mL streptomycin. Islets were incubated at 37C, 5% CO2 for 24–72 h before imaging.

An hour prior to imaging nutrition media from the isolated islets was replaced by an imaging solution (125 mM NaCl, 5.7 mM KCl, 2.5 mM CaCl2, 1.2 mM MgCl2, 10 mM HEPES, and 0.1% BSA, pH 7.4) containing 2 mM glucose and fluo4 AM [Ca 2+ ] i sensitive dye (4 mM). After one hour the solution was replaced by dye-free imaging solution. During imaging the glucose level was raised from 2 mM to 11 mM. Islets were imaged using either a LSM780 system (Carl Zeiss, Oberkochen, Germany) with a 40x 1.2 NA objective or with an LSM800 system (Carl Zeiss) with 20x 0.8 NA PlanApochromat objective or a 40x 1.2 NA objective, with samples held at 37°C. The resolution was 512x512 pixels and time series were recorded with frequencies 1–2 Hz.

Pre-processing of recorded [Ca 2+ ] i time series

Fluorescence signals of Calbryte 520 AM or Fluo-4 representing time series for manually selected regions of interest (ROIs), i.e., individual beta cells, were exported along with their corresponding coordinates using a custom software called ImageFiltering (copyright Denis Špelič) or ImageJ [ 151 ]. As both dyes can detect both fast- and slow-component in beta [ 61 , 152 ], data obtained by either dye was pre-processed equally. Time series that exhibited large artifacts, low signal-to-noise ratio, or dynamics inconsistent with beta cells were excluded after visual inspection. The recordings from tissue slices underwent band-pass filtering using a zero-lag filter to extract either the fast-activity component (with typical cut-off frequencies of 0.05 and 2.0 Hz) or the slow-activity component (with cut-off frequencies of 0.001 and 0.07 Hz). Similarly, the recordings from isolated islets underwent band-pass filtering to eliminate baseline drifts and capture the oscillatory component (with typical cut-off frequencies of 0.005 and 0.25 Hz). Fast-component signals from slices and oscillatory signals from isolated islets were further smoothed using an adjacency averaging procedure and then binarized by setting values to 1 (active state) for periods of increased [Ca 2+ ] i signals or 0 (inactivity) for periods of low-amplitude signals. All subsequent analyses were performed either on the raw, filtered (fast or slow oscillatory component), or binarized cellular signals. The binarized signals were also used to calculate the relative active time. This metric represents the ratio of the time a given cell is in an active state, indicating thereby the overall cellular activity.

Evaluating synchronicity between [Ca 2+ ] i traces

different data representation techniques

By using Eqs ( 1 ), ( 2 ), and ( 6 ), we can construct similarity matrices of size ( N , N ), whereby N stands for the number of cells, that encode the correlation, coactivity, and normalized mutual information between all cell pairs in individual recordings, respectively. Notably, MI captures also non-linear relationships between the discretized time series.

Network construction and analysis

different data representation techniques

Alternatively, a variable similarity threshold technique can be used instead of a fixed threshold, which can create a network with a pre-set target average node degree, so the threshold is varied until a network with the target average node degree is designed. In our analyses the variable threshold was determined so that the resulting network had an average degree 8. This value was used to mimic the connectivity of realistic beta cell network architectures [ 156 ] and to obtain adequately dens networks suitable for analyses. However, it should be noted that previous studies have demonstrated that, within reasonable limits, the conclusions drawn from network analyses are not significantly influenced by the somewhat arbitrary choice of the average degree [ 13 , 96 ].

different data representation techniques

Based on the computed abstract distances, an MST can be constructed with so-called greedy algorithms such as Kruskal’s [ 157 ] or Prim’s [ 158 ] algorithm. These algorithms create graphs with N -1 edges ( N –number of nodes) which contain the lowest possible sum of edge weights (Σ D i , j ) without creating any cycles. We expand this idea for the generation of a multilayer MST, where a single MST is computed sequentially for the same network, but already existing edges (i.e., cell pairs) are excluded from the calculation of the next MST layer. In our analyses we calculated four layers of MST’s, which yielded an average node degree of 8 (the average degree of the original MST is 2, and each of the three subsequent layers contributes an additional 2 degrees).

For each extracted network, we calculated several basic network parameters, such as average network node degree ( k avg ) and degree distribution, average clustering coefficient ( C avg ) and clustering coefficient distribution, modularity ( Q ), global efficiency ( E ), relative largest component ( S max ), and edge length distribution, and small-world coefficient ( SW ). See Ref. [ 159 ] for technical details and Ref. [ 56 ] for a physiological meaning of these specific network parameters.

Quantifying inter-network similarity

different data representation techniques

In other words, inter-network similarity is defined as the ratio between the cardinality, i.e., the total number of edges of the intersection of edges in networks α and α ′, and the cardinality of the corresponding union. The resulting value of NSI ranges from 0 to 1, where 0 indicates no common edges between the networks and 1 indicates identical networks. This method was used to assess the similarity between functional networks derived from various oscillatory components and constructed with the above-described construction techniques. We additionally quantified the similarity of these networks with the postulated structural networks of islet cells, which we constructed as geometric networks by appropriate intercellular distance thresholding.

Methods for the time series processing, analyses of cellular signals, and network analyses were designed with Python programming language version 3.11.1, using the following packages: Numpy ( https://numpy.org/ ), Matplotlib ( https://matplotlib.org/ ), and NetworkX ( https://networkx.org/ ). All code is available on the GitHub repository: https://github.com/MarkoSterk/beta_cell_analysis_suite

Supporting information

S1 fig. collective beta cell activity under the protocol of a glucose ramp..

A) Ca 2+ traces of all responding beta cells in the slice (upper panel) and the corresponding raster plot of binarized fast Ca 2+ oscillations. The glucose concentration was ramped from 6 mM to 12 mM, as indicated at the top. B) Functional beta cell networks extracted in different glucose concentrations and with different thresholding techniques. The fixed threshold approach ( R th = 0.8) leads to very different network structures under different stimulation levels. Under lower glucose, when the degree of correlated beta cell dynamics is low, the networks are sparse and segregated. With increasing stimulation, the networks become progressively more integrated and dense (i.e., average node degree k avg is increasing), highlighting the heightened intercellular coordination. Conversely, the fixed avg. degree and multilayer MST approaches fail to capture this behavior, as they enforce a fixed number of connections, irrespective of the level of coordinated intercellular activity. Furthermore, utilizing a fixed average degree under conditions of low multicellular activity results in exceedingly low thresholds ( R th < 0.5), thereby promoting the establishment of functional connections by chance, which introduces unpredictability into the network analysis. Consequently, techniques that enforce a fixed number of connections are unsuitable for experiments where the level of activity changes significantly.


S2 Fig. Exploring the Impact of Oscillatory Components and Calcium Signal Processing on Functional Network Structure.

The figure presents four types of networks derived from analysis of the five different islets examined in Fig 5 : i) A structural network modelled as a geometric network, wherein nearby cells are deemed connected. ii) A functional network derived from unprocessed signals. iii) A functional network extracted from the fast oscillatory component. iv) A functional network constructed based on the slow oscillatory component. All four networks were designed with a fixed average degree k avg = 8. Remarkably, across all five islets, the functional network based on the fast oscillatory component exhibits the fewest long-range connections and shows the highest similarity to the hypothesized structural network. In contrast, networks derived from unprocessed or slow-component signals display a greater proportion of long-range connections, exhibit similar characteristics to each other, and diverge significantly from the structural network.


S3 Fig. Investigating the influence of oscillatory component on functional network structure in an isolated islet.

A) The average unprocessed (black) and extracted slow-component Ca 2+ signal (blue) from a Gcamp mouse islet are depicted. The inset shows the corresponding derived fast-component signal (red). B) Different types of beta cell networks: structural (modelled as a geometric network) and three functional networks derived from the unprocessed, slow-component, and fast-component Ca 2+ dynamics. Hub cells are highlighted in red. C) Inter-network similarity matrix quantifying the degree of overlap between the four networks. Evidently, the networks extracted from the unprocessed and slow-component traces are very similar, while the fast component network exhibits the highest degree of similarity with the structural network. In contrast, the similarity between the networks derived from unprocessed and slow-component signals and the structural network is notably lower, mirroring observations in tissue slices (see Figs 6 and S2 ).


S4 Fig. Comparative analysis of functional intercellular network design methods.

A) Three representative beta cell signals (red line) and three alpha cell signals (blue line) subjected to the indicated stimulation protocol: 9 mM -> 10 mM -> 11 mM -> 11 mM glucose + μM epinephrine. This protocol was used to functionally discriminate alpha and beta cells, as the addition of 1 μM epinephrine activates alpha cells and inhibits beta cells. B) Functional networks were extracted using two methods: the fixed average degree method (left) and the four-layered multilayer minimum spanning tree (MST) method (right). The multilayer MST method enforced connections to all cells, including those with asynchronous dynamics, such as alpha cells. Consequently, alpha cells were integrated into the functional network despite their lack of correlation with the rest of the syncytium. This highlights the unsuitability of the MST method for network analyses involving elements with diverse dynamics. Alpha cells are indicated with blue circles and beta cells with red circles.



We thank Jasmina Jakopiček, Nika Polšak, Rudi Mlakar, and Maruša Plesnik Rošer for their excellent technical assistance.

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  • 28. Kravets V, Dwulet JM, Schleicher WE, Hodson DJ, Davis AM, Pyle L, et al. Functional architecture of the pancreatic islets reveals first responder cells which drive the first-phase [Ca2+] response. bioRxiv. 2021; 2020.12.22.424082. https://doi.org/10.1101/2020.12.22.424082
  • 50. Clair JRS, Westacott MJ, Miranda J, Farnsworth NL, Kravets V, Schleicher WE, et al. Restoring Connexin-36 Function in Diabetogenic Environments Precludes Mouse and Human Islet Dysfunction. bioRxiv; 2023. p. 2020.11.03.366179. https://doi.org/10.1101/2020.11.03.366179


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