case study master data management

7 Master Data Management Use Cases in 2024

Figure 1. Worldwide interest in master data management since 2004. 1

Master data management (MDM) is the process of collecting, storing, organizing, and maintaining a company’s critical data. This data can include information about customers, products, suppliers, financial data, and compliance data. Master data management, in particular, can be applied to data governance . Unfortunately, interest in master data management has remained consistent in comparison to data governance, which has been steadily increasing since 2016.

Master data management capabilities are essential for businesses that want to make informed decisions based on accurate and complete data. In this article, we explore the seven use cases for master data management and their benefits.

1. Data governance

Data governance is the process of managing data accuracy, availability, usability, integrity, and security. It can be an essential component of effective master data management because it can ensure that data is of high quality and properly secured.

Since 2004, interest in data governance has been increasing, with high search volume in almost every state in the U.S. Data governance is the ability to improve data quality, which is critical to effectively managing master data. With accurate, complete, and consistent master data records, organizations can create a single, trusted source of data systems. It can be used across the organization to support various business processes, such as:

• Sales: Accurate customer data can assist sales teams in identifying potential customers and tailoring their approach. MDM can provide a single, trusted master data record of customers that can be used to inform sales decisions.
• Marketing: Product data that is complete and accurate can assist marketing teams in developing targeted marketing campaigns that resonate with their target audience and drive engagement and sales. MDM can provide a centralized repository for product data to support marketing initiatives.
• Finance: Consistent financial data can assist finance teams in tracking revenue and expenses, developing budgets, and making sound financial decisions. MDM can provide a centralized, trusted source of financial data for use in making financial decisions.

Data security

Another benefit of master data management is that it can improve data security , reducing the risk of data breaches and protecting the organization’s reputation. By creating accurate master data, MDM can help reduce the risk of data breaches and protect the organization’s reputation. 

2. Customer data management

In 2020, the global interest in master data management and customer data management peaked. Customer data management (CDM) is a subset of MDM that focuses on the management of customer-related data within a company. The process of collecting, organizing, and maintaining customer data is referred to as customer data management. 

Effective customer data management enables businesses to keep accurate and complete customer information on hand, which can then be used to improve customer service and gain insights into customer behavior . 

As a result, the CRM benefits include:

• Improved customer service through personalized interactions based on accurate and complete customer data.
• Gaining insights into customer behavior by analyzing their data, which can be used to develop new products and services and improve customer loyalty.

3. Financial data management

Financial data management is a critical component of master data management that focuses on managing financial-related data within an organization. Financial data management refers to the process of collecting, organizing, and maintaining financial data. Financial data can include information related to transactional data, revenue, expenses, assets, liabilities, and other financial metrics that are essential for decision-making and financial reporting.

Financial data management strategy can be critical for ensuring that an organization’s financial data is accurate, complete, and consistent across all systems and applications. This is significant for several reasons. To begin, precise financial data is required for financial reporting , regulatory compliance , and tax reporting . Second, financial data is an important input for business decision-making processes like budgeting, forecasting, and strategic planning.

One of the primary advantages of incorporating financial data management into an MDM strategy is that it allows organizations to gain a comprehensive view of their financial operations. Organizations can gain valuable insights into their financial performance and identify areas for improvement by integrating financial data with other master data domains. Organizations, for example, can use financial data to determine which products or services are the most profitable, which regions generate the most revenue, and which cost centers drive expenses.

4. Supplier data management

One of the lesser-known aspects of MDM is supplier data management. In the last five years, supplier data management has seen consistent interest from India, the United States, and Canada. The process of collecting, organizing, and maintaining supplier data is referred to as supplier data management. This information may include details about:  

• Supplier contracts
• Performance
• Compliance 

Reduced supplier risks

Effective supplier data management is critical for companies that want to ensure that their suppliers meet quality and compliance standards. Knowing what is happening in your supply chain can prevent snowball effects . 

One of the benefits of supplier data management is the ability to reduce supplier risks . By maintaining accurate and complete supplier data, businesses can identify potential supplier risks and take proactive measures to mitigate them. This can help businesses reduce the risk of supply chain disruptions and improve the overall quality of their products and services.

Supplier performance

Another benefit of supplier data management is the ability to improve supplier performance . By analyzing supplier data, businesses can identify areas for improvement and work with their suppliers to improve performance . This can help businesses build stronger relationships with their suppliers and improve the overall quality of their products and services.

5. Compliance data management

Since the 2020s, there has been an increase in interest in compliance management . The process of collecting, organizing, and maintaining compliance data is referred to as compliance data management. This information may include regulatory requirements , internal policies and procedures , and audits . 

Reducing compliance risks is one of the advantages of compliance data management. Businesses can recognize possible compliance issues and take preventative action to mitigate them by storing accurate and complete compliance data and reduce the risk of storing incorrect data. By doing this, businesses can lessen the chance of non-compliance fines and reputational harm .

The capacity to enhance compliance reporting is a further advantage of compliance data management. Businesses can produce more accurate compliance reports that can be utilized to make decisions by preserving accurate and full compliance data. By doing this, organizations can ensure that they are fulfilling their duties and enhance their compliance performance.

6. Product information management

Product data management (PDM) is an important component of master data management that focuses on managing product-related data within an organization. Product data management refers to the process of collecting, organizing, and maintaining product data. Product data can include information such as product descriptions , specifications, prices, inventory levels , and other relevant information that is essential for managing an organization’s product catalog and sales operations .

Good product data management ensures that an organization’s product information is accurate, complete, and consistent across all systems and applications. MDM includes product data as a master data domain, together with customer, financial, and supplier data. This means that product data is vital for precise and consistent operations management.

One benefit of product data management is that it can improve product quality. Product quality can be improved through product data management. By preserving precise and full product data, organizations can identify and correct product problems, improve product features, and increase product quality.

Another benefit of product data management is product development. Product development can be streamlined with product information management. Businesses can save product development time and expense by retaining accurate and complete product data. Businesses may launch new items faster and more efficiently with this.

7. Reference data management

The management of reference data within an organization is the focus of reference data management (RDM), a critical component of master data management. Data that is used to categorize, classify, or otherwise define other data elements within an organization’s systems and applications are referred to as reference data. Product codes, industry codes, geographic codes, and other standard or regulatory codes are examples of reference data. In the last five years, reference data management has been frequently searched on Google.

Reference data management benefits

Reference data management benefits include:

• Improved data quality: One of the benefits of reference data management is the ability to improve data quality . By maintaining accurate and complete reference data, businesses can ensure that data is properly classified and can be used effectively. This can help businesses make informed decisions based on high-quality data.
• Improved data integration: Another benefit of reference data management is the ability to improve data integration . By maintaining accurate and complete reference data, businesses can ensure that data can be integrated across different systems and platforms. This can help businesses streamline their processes and reduce the risk of data errors.

If you have more questions about master data management, please contact us at:

This article was drafted by former AIMultiple industry analyst Yılmaz Doğukan Özlü.

External Links

• 1. Google Trends

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month. Cem's work has been cited by leading global publications including Business Insider , Forbes, Washington Post , global firms like Deloitte , HPE, NGOs like World Economic Forum and supranational organizations like European Commission . You can see more reputable companies and media that referenced AIMultiple. Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization. He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider . Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

To stay up-to-date on B2B tech & accelerate your enterprise:

Next to Read

Top 10 data as a service companies in 2024, data annotation in 2024: why it matters & top 8 best practices, data as a service (daas): what, why, how, use cases & tools in '24.

Your email address will not be published. All fields are required.

Related research

Data Observability: A New Frontier for Data Reliability in 2024

Data Quality Assurance: Importance & Best Practices in 2024

• Germany (Deutsch)
• Japan (日本語)

Redesigning master data management framework to save up to $98 million in procurement spend

How one pharma giant conquered master data management to mitigate risks and find savings

Who we worked with

A British multinational pharmaceutical company

What the company needed

To improve spend visibility and eliminate inefficient purchasing practices by managing master data better. To assess supplier risk more effectively.

How we helped

We created a detailed master data management business case road map and brought in the right technologies to do the job. This also involved developing a vendor data model and recommending that the firm designate people for data governance and stewardship. As well, we helped manage and refine indirect material categories.

What the company got

• Simplified and standardized data and procurement processes
• Reduced vendor risk and superior compliance
• Access to the best technology for master data management

Research shows that enterprises know robust master data management (MDM) can help them meet key business challenges. Yet there's a disconnect. MDM is one of the least mature procurement functions, 1 even though bad data affects all kinds of operations and decisions. When it's held in disparate systems, it doesn't offer a single version of the truth—and because there's a lot of manual effort involved, it's costly to maintain. Without a structured approach, firms miss opportunities for timely analysis, stronger growth, and accelerated mergers and acquisitions.

This multinational pharma was feeling those ill effects. It turned to us because it wanted to improve spend visibility, eliminate inefficient purchasing practices and have more effective supplier risk assessments. And it knew it needed to address its master data. When we came on board, it had categorized just 27% of its direct and indirect spend and it had low-quality vendor data, putting it in a weak negotiating position when it came to purchasing. In this master data management case study we discuss about helping the company design a master data management business case road map that would lead it to best-in-class MDM processes, governance, and technology.

Break down silos. Organize data. Bring in third-party oversight to manage vendor risks.

Many functions played a role in this firm's MDM: IT, procurement, finance, and the data management team were among them. But they were all working in their own silos, and that had to change if the company wanted to make better purchases by getting greater visibility into procurement. The enterprise also wanted to improve vendor risk management and regulatory compliance by introducing third-party oversight, but its vendor data model wasn't in any shape to accommodate that.

Take a copy for yourself

Our solution

Take full account of existing systems—and turn them into a comprehensive whole

Working with the pharma, we capitalized on our experience managing vendor and indirect material master data and taxonomy design. We began by analyzing the existing:

• Data quality
• Data governance
• Target operating model
• Vendor data model
• Indirect material and taxonomy
• The vendor process and KPIs

The assessment revealed:

• Incomplete, often inaccurate and frequently duplicated vendor data
• Data-quality metrics that didn't coordinate with business needs
• Missing or incomplete data fields that affected supplier risk management
• Over 100 systems that held vendor data, with no integration between them
• No one had stewardship of MDM
• At 45-55 days, the vendor-onboarding process was too long
• The spend management tool categorized only 10% of indirect materials. Globally, the company made 93% of indirect material purchases through free-text entries rather than catalogs. That represented 65% of the organization's total spend.
• Multiple taxonomies and mismatching commodity codes. No one used commodity codes in 35% of global purchase orders.

We responded to these issues by recommending a comprehensive master data management framework—something that can seem daunting if you think of the process as limitless. So we were careful to align the road map to areas that would generate the greatest business impact.

Developing a powerful vendor data model

To create a robust vendor data model that met the company's needs and that positioned it for third-party oversight, we identified:

• New attributes to improve supplier risk assessment, including site-level details and parent-child hierarchies
• Attributes for better vendor matching, as well as tax codes, tax identification numbers, and banking details
• Redundant or unused attributes for removal
• Principles for determining whether attributes should reside in transactional or centralized master data systems
• A road map of short- and long-term attribute changes.

Reshaping data quality and governance

We scrutinized the supplier data for completeness, accuracy, and uniqueness to benchmark the quality of the firm's vendor master data systems. Then we analyzed related business rules to measure the effectiveness of vendor master matching.

Finally, IT redesigned data governance and stewardship, giving people defined roles and responsibilities. It also proposed key metrics for the data quality dashboard.

Devising categories that work

Since 93% of indirect material sourcing came through free-form text, the risks of incorrect categorization and inflated inventory were high. As well, the existing system categorized only 10% of indirect materials. So we proposed the following:

• Options for either maintaining materials information in a detailed taxonomy with many commodity codes, or managing it in a master
• The pros and cons of developing a granular taxonomy or maintaining a material master
• Guidance on when a category should reside in a material master or a catalog
• Opportunities for creating categories in catalogs to reduce free-form text purchases
• High-level taxonomy analysis and best practices for taxonomy revision

Applying the best technology for the job

We also proposed technologies and guiding principles to support the initiatives, and shared how similar companies deal with master data.

Visit our life sciences page

Breaking silos, delivering results—and a bright procurement future.

Our recommendations identified ways for the pharma to generate benefits of $70–98 million. The vice president of strategy and performance praised our ability to work with teams from many functions to deliver insights and a strong master data management framework. We continue to support the company as the new target operating model and stewardship structure come on line. With effective master data management, the company can now expect world-class procurement.

1. Transforming procurement operations with advanced operating models

Elevating enterprise master data management

• Call for Change
• When Tech Meets Human Ingenuity
• A Valuable Difference
• Meet the Team
• Related Capabilities

Call for change

Accurate master data—an organization’s single source of basic business data used across multiple applications and processes—is a key element in driving accurate analytics and business decisions. To achieve accurate master data, organizations must develop a capability to manage unprecedented volumes of data in an integrated and agile way with rigorous maintenance and governance processes to maintain quality.

For Accenture, the need for a robust master data management (MDM) capability was driven by three main factors. The first was the need for agility to support Accenture’s ever-changing business. The second was the need for improved data quality to address new external requirements such as Sarbanes-Oxley requirements. The third was to resolve data inconsistencies that were the result of point-specific needs within silos without appropriate governance and controls. Both business and IT leaders recognized the need to create an integrated business and technology MDM capability to eliminate integration issues and the long lead time to manage master data changes and address quality issues.

Accenture’s MDM highlights:

Master data objects in scope

Acquisitions supported in the last 3 years

Integrations with global systems

Records updated per year

FTE integrated global capability

When tech meets human ingenuity

Accenture’s  global IT organization  collaborated with the business to create a cross-functional master data capability that sits within Accenture’s Business Integration organization. Together, the team delivers production and end-user support for Accenture’s key platforms, including single global instances of  SAP® , Workday and  Salesforce . The team manages sales and pricing, finance and HR data. In addition, it centralizes and standardizes processes and controls with strong support from IT and business leadership. This support includes active data governance representing each stakeholder group.

A key development of the MDM capability was the implementation of a single, integrated data model spanning business processes and applications ensuring one version of the truth. IT resources were charged with designing, implementing and maintaining this integrated data model, collaborating with Business Integration and other business teams to ensure appropriate data definitions, relationships and service levels were in place.

The MDM capability naturally scaled in cost-effective shared services centers and was done so using a follow-the-sun approach to support their respective business and system processes. The team has evolved to more than 70 specialized employees managing more than 10,000 transactions per month.

Having the organization, process and technology foundations in place enabled optimization. The MDM capability was able to outsource most of its functions to the Accenture Operations organization, enabling further scale, cost savings, career development opportunities, sharing of best practices and enhancement of Accenture’s go-to-market capability.

Many of Accenture’s workflow and request management tools are today using the  power of ServiceNow  for enterprise service management. Among these tools is the automation of the MDM request fulfillment process. It performs real-time validation, seamless integration and master data creation across Accenture’s ERP, leading to a faster turnaround with higher quality and less effort.

Master data management critical success factors:

Establish defined data roles and processes

Eliminate, simplify and automate

Centralize maintenance teams in shared services centers

Provide an intuitive user design

Be a trusted business partner

Develop an integrated application architecture

By implementing process rigor, automation and organization optimization, Accenture’s MDM team is able to provide the business with ever-greater agility and confidence in data and reporting. The team operates across three continents as a single, truly integrated global team with a solid focus on addressing customers’ business needs.

Governance, industrialization, automation and continuous improvement are key aspects of the team culture. This focus helps to deliver agile results to Accenture business customers cost-effectively and with the same number of people while Accenture’s business grows. Having honed the MDM capability, the team is able to rotate from the provision of basic transactional services to critical business advisory services

A valuable difference

Accenture progressively shaped MDM to become the prime provider of high-quality, timely master data today. This data delivers business value by integrating Accenture’s business analytics, security model and other core business processes that enable Accenture’s operations.

Operating from an integrated, centrally managed set of core master data allows Accenture with equal confidence to analyze results across Accenture’s markets and services while also being able to meet external reporting requirements.

Data quality as well as turnaround time for data delivery have improved by 5 percent per year. Operating costs have reduced by 5 to 7 percent every year even as Accenture’s business continues to grow. More than 300 global systems have integrations enabling consistent data. The lead time for the processing of master data has been significantly reduced and largely removed from the critical path of reorganization changes, integration of acquisitions into Accenture business applications, and other critical business events.

Moreover, while the model assures rigorous processes, it also has the flexibility to adjust to the ever-changing reality of the business and to take on board new applications or capabilities. As Accenture’s MDM capability continues to evolve, the team looks to mature business integration, improve the user experience through advanced automated and integrated technology solutions, and develop deeper enterprise analytics capabilities.

MDM outcomes:

Annual improvement in data quality

Average annual reduction in data delivery turnaround time

Reduction in operating costs

Meet the team

Steve Collins

Mark Burbage

Pablo juan rubí, related capabilities, how accenture does it, accenture + sap, finance at accenture.

Case Study: Master Data Management

A large and growing manufacturing company with a global footprint had experienced consistent organic growth coupled with numerous acquisitions. The company’s processes and systems used to create and manage master data had become inconsistent and disparate, leading to a growing challenge in managing data quality.

A large and growing manufacturing company with a global footprint had experienced consistent organic growth coupled with numerous acquisitions. The company’s processes and systems used to create and manage master data had become inconsistent and disparate, leading to a growing challenge in managing data quality.

Key Challenges

• The company’s needs around product master data were critical to maintain compliance with import/export regulatory requirements; to meet customer specifications for e-commerce data; and to optimize internal transaction processing, warehouse management and distributions. 
• A best-of-breed technology approach coupled with the integration of acquisitions resulted in numerous data conventions and a need to manually replicate data across multiple databases, which contributed to the degradation of data quality.

Our Solution

Our consulting team led a four-phase approach to assess the company’s master data management processes and systems. We developed and implemented a plan to address its challenges and to build a scalable solution to effectively manage and use product master data going forward.

• Discover  – Our team worked with client leadership to identify and interview subject matter experts across the business to understand the processes in which data was defined, maintained and used by each major business function. Current state processes were mapped and documented, along with the current application architecture and the flow of data across enterprise systems. Business impacts resulting from data quality issues were identified in each function, and data quality analysis was conducted to assess error rates and determine the greatest areas of impact and deficiencies in the data.
• Vision – Considering the company’s unique environment and its current challenges, we identified 16 value-creating opportunities to enhance data quality, streamline processes, and ensure compliance with regulatory and customer requirements. After considering the value and investment required to deliver on these opportunities, we worked with the company to prioritize these items and develop a future state vision for a master data management solution centered on an integrated data hub and automated workflow processes.
• Roadmap – With the future state vison defined, our team identified three distinct initiatives resulting from the prioritized value-creating opportunities and developed project charters for each to outline the scope and objectives. We defined an implementation roadmap around these initiatives to set clear milestones and guide the delivery of each solution component.
• Execution – Leveraging a process-automation platform recently procured by the company, our team worked closely with their staff to design and implement a workflow solution to standardize the process in which master data is defined, created and maintained. A centralized data hub was established and integrated with the workflow solution. This served as the single point of entry and source of truth for product master data, from which appropriate data could be syndicated via automated integrations to required enterprise systems.

The Results

Documenting the client’s enterprise architecture and data flows allowed the business to clearly see the process and data ownership across the organization, resulting in a common language for master data improvements. The resulting implementation of a new workflow solution, data hub and supporting integrations to enterprise systems generated tangible benefits to the business:

• Significant reduction in manual data entry efforts
• Single source of entry for product master data and synchronized data across systems
• Increased security and control over master data creation and maintenance across enterprise systems
• Process automation/workflow delivered to streamline and provide visibility into the master data authoring/maintenance processes
• Accelerated timeframe to configure new products and ensure readiness to sell/distribute
• Reduced fines resulting from data errors that previously violated import/export regulations and large customer agreements
• Enhanced trust in data and management reporting

Sign Up for Our Emails & Events

Receive insights from our specialists in a variety of areas and timely information on upcoming events directly to your inbox as they go live in our online Knowledge Center.

Enterprises are challenged with consolidating, and providing centralized access to data across organizational silos and workflows. They require a single source of truth for multi-domain data including customer, product and location data for successful AI, machine learning and business intelligence initiatives, and to facilitate data governance and regulatory compliance.

Master data management (MDM) meets this challenge by providing the ability to intelligently connect and match associated records to create accurate entities across multi-domain sources, then efficiently determines relationships between the data records. By delivering access to accurate views of master data and their relationships, MDM solutions enables faster insights, improved data quality and compliance readiness.

With access to complete and accurate picture of data about an entity and their relationships, MDM software delivers business and IT users with the right data to derive business intelligence and reduce business process inefficiencies, improve customer experience, proactively identify and mitigate risks and ultimately improve business decision-making.

Enable your teams to have quick access to the critical data they need across customers, products, organizations, households and more.

Get the right data to your users and reduce time-to-market with robust pre-built data models.

Reduce administrative burden and improve matching accuracy using built-in machine learning algorithms.

ML-powered self-service matching delivers trusted, unified views of master data across disparate, multi-domain sources.

Facilitate regulatory compliance by helping maintain accurate and consistent records of critical data.

Boost data quality by deduplicating data and improving accuracy of master data.

Provision, share and connect master data for deeper analysis and enhanced insights.

An easy-to-use experience intended for business users to encourage exploration at their own pace and to get the data they need, at the right time.

Proven to scale and perform with production deployments at many Fortune 500 companies as well as large clients across industries.

Leverage automation to match associated records across multi-domain data sources to create accurate, 360-degree view of entities.

A highly performant and accurate tool to understand the relationship between records.

Easily provide the reasoning for why entities were matched, and remediate issues through automated workflows.

Creates an accurate and up-to-date repository of service and product data.

Simplify access to data by leveraging IBM’s data integration and data governance capabilities as part of your data management strategy.

Self-serve and ML-powered data matching capabilities to help business users gain a trusted, unified view of customer and enterprise data across internal and external sources.

Manage enterprise-wide master data for single or multiple data domains, including customers, suppliers, products, accounts and more across all MDM implementation styles.

Provides product information management and collaborative master data management capabilities.

Created a single accurate record for each of the nine million individuals in their healthcare network using an IBM InfoSphere Master Data Management solution.

Learn how data leaders can build a 360-degree views of customers by breaking down data silos with a data fabric architecture.

Learn about centralizing MDM in the cloud with multiple cloud deployment options and subscription-based pricing.

Create a single repository of product and service information and use it throughout the enterprise.

Schedule a 30-minute, one-on-one call or talk with an expert at no cost. Our specialists have deep experience building master data management solutions for thousands of clients. 

Master Data Management Maturity Evaluation: A Case Study in Educational Institute

• Conference paper
• First Online: 01 October 2022
• Cite this conference paper

• Dupinder Kaur 7 &
• Dilbag Singh 7  

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 311))

410 Accesses

To deal with an organization’s essential data as a single coherent system, Master Data Management is essential. It links all the critical data as a unified version of truth known as “Master data”. It is responsible for data sharing, integration, analytics and decision making. The quality of business intelligence, analytics, and AI depends upon Master data management. A maturity model can be used to test the effectiveness of Master Data Management program in an organization. In the present research, a case organization has been considered to assess master data’s maturity level using Spruitz–Pietzka’s maturity model. The considered model consists of 13 focus areas and five key topics. Each focus area consists of packed capabilities used to determine the maturity of master data. The findings showed among 62 applicable capabilities, 44 (70.96%) are applied and 18 (29.03) are absent. Thus, on the basis of applied capabilities, overall maturity level is taken as 1. Hence, an organization can accomplish higher development by implementing missing capabilities.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

• Available as PDF
• Read on any device
• Instant download
• Own it forever
• Available as EPUB and PDF
• Compact, lightweight edition
• Dispatched in 3 to 5 business days
• Free shipping worldwide - see info
• Durable hardcover edition

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

https://www.tableau.com/learn/articles/what-is-data-management , last accessed on 04/09/2021

Singh, D., Kaur, D.: A master data management solution for building frameworks: a constructive way to pilot the implementation. In: 2nd International Conference on Data Analytics and Management (ICDAM). Springer, Berlin (2021)

Google Scholar  

https://help.sap.com/viewer/99218f2c48044ddc8f2ea30adc0e38a1/7.1.18/enUS/471c5928cd0412b8e10000000a1553f7.html , last accessed on 04/09/2021

Singh, D., Kaur, D.: Data profiling model for assessing the quality traits of master data management. Int. J. Recent Technol. Eng. 8 (6), 446–450 (2020)

Lepeniotis, P.: Master data management: its importance and reasons for failed implementations, Ph.D. thesis. Sheffield Hallam University (2020)

Aditya Rahman, A., Dharma, G., et al.: Master data management maturity assessment: a case study of a Pasar Rebo Public Hospital. In: 2019 International Conference on Advanced Computer Science and Information Systems (ICACSIS), pp. 497–504. IEEE, Bali, Indonesia (2019)

Pratama, F.G., Astana, S., Yudhoatmojo, S.B., Hidayanto, A.N.: Master data management maturity assessment: a case study of organization in Ministry of Education and Culture. In: International Conference on Computer, Control, Informatics and its Applications (IC3INA), pp. 1–6. IEEE, Tangerang, Indonesia (2018)

Qodarsih, N., Yudhoatmojo, S.B., Hidayanto, A.N.: Master data management maturity assessment—a case study in the supreme court of the Republic of Indonesia. In: 6th International Conference on Cyber and IT Service Management (CITSM), pp. 1–7 (2018)

Murti, Z., Andarrachmi, A., Hidayanto, A.N., Yudhoatmojo, S.B.: Master data management planning: (Case study of personnel information system at XYZ Institute). In: International Conference on Information Management and Technology (ICIMTech), pp. 160–165. IEEE, Jakarta (2018)

Vilminko, R., Pekkola, S.: Master data management and its organizational implementation: an ethnographical study within the public sector. J. Enterp. Inf. Manag. 30 (3), 454–475 (2017)

Article   Google Scholar  

Zuniga, D.V., et al.: Master data management maturity model for the microfinance sector in Peru. In: Proceedings of 2nd International Conference on Information System and Data Mining, pp. 49–53, USA (2018)

DataFlux Company: MDM components and the maturity model (2010). Retrieved September 3, 2021 from http://www.knowledgeintegrity.com/Assets/MDMComponentsMaturityModel.pdf

Spruit, M., Pietzka, K.: MD3M: the master data management maturity model. Comput. Human Behav. 51 , 1068–1076 (2014)

Download references

Author information

Authors and affiliations.

Department of Computer Science and Engineering, Chaudhary Devi Lal University, Sirsa, Haryana, India

Dupinder Kaur & Dilbag Singh

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Dupinder Kaur .

Editor information

Editors and affiliations.

Hertfordshire Business School, University of Hertfordshire, Hatfield, Hertfordshire, UK

Jyoti Choudrie

Vishwakarma Institute of Information Technology, Pune, Maharashtra, India

Parikshit Mahalle

University Putra Malaysia, Serdang, Malaysia

Thinagaran Perumal

Global Knowledge Research Foundation, Ahmedabad, India

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Cite this paper.

Kaur, D., Singh, D. (2023). Master Data Management Maturity Evaluation: A Case Study in Educational Institute. In: Choudrie, J., Mahalle, P., Perumal, T., Joshi, A. (eds) ICT with Intelligent Applications. Smart Innovation, Systems and Technologies, vol 311. Springer, Singapore. https://doi.org/10.1007/978-981-19-3571-8_22

Download citation

DOI : https://doi.org/10.1007/978-981-19-3571-8_22

Published : 01 October 2022

Publisher Name : Springer, Singapore

Print ISBN : 978-981-19-3570-1

Online ISBN : 978-981-19-3571-8

eBook Packages : Intelligent Technologies and Robotics Intelligent Technologies and Robotics (R0)

Share this paper

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research

Master Data Management: A Comprehensive Guide to Best Practices with Real-World Examples

Hello there! Master data management (MDM) is a critical strategy that leading companies rely on to improve trust in data and drive business value. However, MDM is also complex, with many organizational and technical challenges. In this comprehensive guide, I‘ll explain what master data management entails, why it offers such a high ROI, and proven best practices to make your MDM initiative a success.

What is Master Data Management and Why It Matters

Let‘s start with a quick definition – master data refers to the core business entities used across an organization like customers, products, employees, etc. Master data management implements centralized processes to collect, consolidate, and share this critical data across different IT systems and teams.

MDM arose from a common pain point – company data fragmented across disconnected systems, leading to poor data quality. According to a 2020 survey , 92% of companies rated data quality as extremely or very important, but only 29% were satisfied with their current performance.

The consequences of poor master data include:

• Inaccurate reporting and analytics
• Low customer satisfaction from mistakes
• Non-compliance with regulations
• Increased IT costs for fixes and manual workarounds

With data growing across more systems and channels, these problems will only worsen. MDM offers a solution by delivering:

• A single "source of truth" for master data
• Improved data accuracy and completeness
• Faster access to trusted data
• Digital transformation and analytics enablement

According to IDC, organizations with mature MDM practices achieved :

• 27% greater productivity
• 24% cost savings from consolidation of redundant applications
• More effective compliance with regulations like GDPR

Let‘s explore the key benefits driving these results.

Key Business Benefits of Master Data Management

MDM delivers a range of strategic, operational and analytical benefits including:

Greater trust and usage of data

• 99.6% data accuracy as per a Forbes Insights study of MDM adopters
• 28% more business users relying on data for decisions after MDM implementation [report]( https://www.informatica.com/content/dam/informatica-com/global/amer/us/collateral/brochure/master-data-management-for – 360-view_brochure_6657br02-01-en.pdf)

Increased productivity and lower costs

• $10 million in annual savings from consolidated customer data per Nucleus Research
• 25-35% reduction in time for customer onboarding through MDM as per Deloitte

Enhanced customer service and satisfaction

• 20% faster complaint resolution from consolidated customer history via MDM according to [451 Research]( https://www.informatica.com/content/dam/informatica-com/global/amer/us/collateral/brochure/master-data-management-for – 360-view_brochure_6657br02-01-en.pdf)
• 90% of companies surveyed by Forrester saw improved customer satisfaction after MDM implementation

Better analytics and reporting

• 25% improvement in business metric tracking from unified data per Nucleus Research
• 33% boost in actionable insights for decision-making according to Forrester study of analytics leaders

Clearly, MDM delivers tremendous ROI across business functions – when executed properly. Now let‘s go over some best practices.

Best Practices for Master Data Management Success

Implementing MDM can be challenging – gaining agreement on data governance, standardization, system integration and more. Here are 8 proven tips:

Get executive sponsorship

• 57% of organizations say lack of sponsorship is an MDM barrier per EATON data quality survey
• Assign a steering committee of key department heads to align on strategy

Focus on data governance

• Only 16% have fully defined MDM data governance roles per QI Global
• Appoint data domain stewards for each master data type

Prioritize data quality and integration

• Fix quality issues before consolidating data into a golden record
• Leverage ETL, APIs and microservices for smooth data integration

Start with a business-driven pilot

• Pick a 1-2 high priority domains and deliver quick wins
• Expand scope once the pilot ROI is demonstrated

Take an incremental implementation approach

• Introduce MDM gradually across business functions vs a big bang effort
• Retrofit legacy systems slowly to avoid disruption

Perform extensive change management

• Get buy-in from staff whose daily work will change after MDM rollout
• Provide training on new data stewardship processes

Leverage cloud scale and flexibility

• Managed cloud services like AWS, GCP offer rapid MDM deployment
• Cloud elasticity handles spikes in data volumes cost-efficiently

Measure ROI continuously

• Track KPIs for data quality, system adoption, cost savings
• Quantify benefits and share results to sustain momentum

While the path to MDM success is challenging, the payoff is transformational. Now let‘s look at some real-world examples.

Master Data Management in Action: Real-World Case Studies

Here are a few examples of organizations realizing major business benefits from MDM:

Company : Nestle, Fortune 500 food & beverage conglomerate

Challenge : Disjointed customer data across 600 legacy systems

MDM Solution : Consolidated data into a single customer record

Impact : 66% faster onboarding of new customers and vendors

Company : State of Wyoming

Challenge : No visibility into citizen data spread across 40+ agencies

MDM Solution : Built an ID management hub with 360-degree citizen view

Impact : Improved government service delivery to citizens

Company : Cigna, Global health services firm

Challenge : Difficulty auditing 5 million provider records with poor data transparency

MDM Solution : Centralized provider data repository with workflow automation

Impact : Reduced provider record audit time from weeks to hours

Company : Bosch, multinational engineering and technology firm

Challenge : Product data fragmentation across 200 regional SAP instances

MDM Solution : Implemented standardized global MDM system on SAP MDG

Impact : Increased efficiency in new product launches worldwide

These examples showcase MDM delivering cleaner data, improved regulatory compliance and accelerated speed across industries – but every company‘s needs are unique. Working with expert consultants can help craft the right MDM roadmap. Now let‘s go over some key considerations as you evaluate options.

How Do You Select the Right MDM Approach?

With an array of MDM solutions and approaches available, here is a framework to guide your decision making:

1. Define your MDM business drivers

• What problem areas or opportunities does MDM need to address?

2. Determine scope and priorities

• Which data domains offer the highest ROI based on your drivers?

3. Assess internal capabilities

• Does your team have the skills for MDM program management, data governance, stewardship?

4. Evaluate solution options

• On-premise, cloud or hybrid deployment? Point solutions vs unified platform?

5. Analyze costs, resources and timelines

• Budget, staffing needs and rollout roadmap

6. Select implementation partners

• Require proven expertise in your focus industries and use cases?

With a clear business-driven MDM strategy aligned to enterprise priorities, you‘re positioned for success. Now let‘s discuss some key considerations during implementation.

Critical Factors for Master Data Management Execution

Once you‘ve formulated an MDM gameplan and selected enabling technologies, focus on these factors for smooth execution:

Phased and iterative rollout – Big bang implementations tend to fail. Follow agile principles for incremental value delivery.

Ongoing data governance – Embed stewardship into day-to-day operations for sustainable data quality.

Hybrid architecture – Blend centralized and federated data across systems to balance control versus flexibility.

Cloud foundation – Cloud platforms like AWS enable faster MDM deployment and scalability.

Automation – Reduce manual efforts around data cleansing, integration and issue resolution.

MDM Center of Excellence – Dedicate a team to provide ongoing guidance, support and best practice dissemination.

Business-IT alignment – Joint KPIs and success criteria to ensure MDM delivers business impact, not just technical outcomes.

With attention on these aspects from planning through rollout and ongoing enhancement, you can maximize the ROI of your MDM initiative and become a data-driven organization.

Now that you have a solid understanding of MDM, potential benefits and how to approach implementation, let‘s recap some key takeaways:

Conclusion and Next Steps

• Master data management has become crucial for trustworthy analytics, compliant operations and extraordinary customer experiences.
• MDM solutions deliver a unified "single source of truth" for master data across the enterprise.
• Organizations implementing MDM realize accelerated efficiency, improved data quality and analytics, superior regulatory compliance and increased competitiveness.
• However, MDM success hinges on executive sponsorship, change management, data governance and the right solution match.
• With a business-driven MDM roadmap and rigorous focus on best practices, you can maximize value and ROI.

I hope this guide has provided you with a comprehensive introduction to master data management. As your organization evaluates MDM options, I‘m happy to offer any additional advice as you chart the path forward – just let me know! Wishing you tremendous success.

I am Paul Christiano, a fervent explorer at the intersection of artificial intelligence, machine learning, and their broader implications for society. Renowned as a leading figure in AI safety research, my passion lies in ensuring that the exponential powers of AI are harnessed for the greater good. Throughout my career, I've grappled with the challenges of aligning machine learning systems with human ethics and values. My work is driven by a belief that as AI becomes an even more integral part of our world, it's imperative to build systems that are transparent, trustworthy, and beneficial. I'm honored to be a part of the global effort to guide AI towards a future that prioritizes safety and the betterment of humanity.

Similar Posts

Large language models in healthcare: transforming patient outcomes through ai.

Large language models (LLMs), which leverage vast datasets to understand and generate human language, are emerging…

The Complete Guide to Mastering Web Testing in 2023

Testing your website or web application is no longer optional – it‘s essential for providing a…

The Complete Guide to Human-in-the-Loop Machine Learning in 2023 and Beyond

Human-in-the-loop (HITL) machine learning is one of the most important AI trends right now. By strategically…

Top 6 DataOps Tools: In-Depth Guide for Implementing DataOps

In today‘s data-driven world, effectively managing and optimizing data pipelines is essential yet challenging. This comprehensive…

CRM in Retail: The Complete Guide to Customer Relationship Management for Retail Success

The retail industry is growing rapidly, with global retail sales projected to reach nearly $28 trillion…

The Comprehensive Guide to Ensure Data Quality in AI/ML Projects

Data quality can make or break your AI/ML project. Low-quality data leads to low-quality models filled…

Data Topics

• Data Architecture
• Data Literacy
• Data Science
• Data Strategy
• Data Modeling
• Governance & Quality
• Data Education
• Enterprise Information Management
• Information Management Articles

Case Study: Enhanced Master Data Management Implemented at Office Depot

“The way customers are shopping has changed and continues to evolve,” said Sam Copeland, Vice President of Merchandising Operations for Office Depot, in a recent interview with DATAVERSITY®. To meet this new challenge, he said, “We need to have nimble systems, processes, and perfect data quality.” Since the implementation of Stibo Systems’ STEP solution for […]

Needs Assessment

“ Office Depot is a company that thrives by providing the best solutions for clients’ constantly changing workplaces,” Donders said, yet in 2009, they were struggling with inconsistent and time-consuming recording of product details, an inability to deliver requested product information to customers on a consistent basis, and a longer than desired time-to-market for new products.

With a desire to respond to increased customer demand for a more comprehensive, multi-channel shopping experience, the company realized that they were unprepared to efficiently manage data coming from internal sources as well as third-party suppliers. “The departments responsible for our publications and merchandising were spending too long categorizing products,” which delayed time-to-market, said Donders. “While we should have been focusing on selling products, sometimes it seemed like we were only processing data.”

The decision to improve customer experience included a desire to respond to evolving customer needs in the future as well as meeting current demands, said Copeland. “Traditional sales channels are blurring. Customers may start the shopping journey through web or mobile, but the transaction may happen online or at retail through a dedicated sales associate. The fulfillment of those products may happen in a direct-to-consumer model, in a store at the POS or via ship-from-store or with a third party,” he said.

In order to reach omnichannel customers, Office Depot needed consistent, trusted data, and systems and processes that were nimble enough to adjust quickly to changing demands. A Master Data Management (MDM) solution was identified as a priority, so that data input and categorization could be streamlined.

Finding a Solution

Office Depot needed a responsive solution that could standardize the data for a complex web of hundreds of suppliers offering millions of products. This level of complexity demands centralized master data, Copeland said.  “Stibo Systems’ STEP platform was selected as the best solution set to facilitate our future state needs. The data quality engine, ability to be nimble,” and a user interface that is easy for suppliers and internal associates to use was considered of critical importance.

STEP is a Master Data Management (MDM) platform that “integrates multiple disparate systems by streamlining the process of aggregating and consolidating information around an organization’s products, customers, suppliers, employees, assets, location and reference data from multiple sources and formats,” he said. “STEP connects information sources to derive actionable insights, and publishes it to backend systems as well as online and offline channels.” This is particularly important because today’s consumers increasingly expect a consistent experience whether shopping in a store or on a website, or browsing via mobile device, he said.

According to a May, 2017 press release from Stibo Systems , they are “the global leader in multidomain Master Data Management (MDM) solutions.” The company provides “cross-channel consistency by linking product and customer data, suppliers, and other organizational assets,” enabling businesses to “make more effective decisions, improve sales and build shareholder value.” A privately held subsidiary of the Stibo A/S group, which was originally founded in 1794, Stibo Systems’ corporate headquarters is located in Aarhus, Denmark.

In an article by Stibo Systems entitled “Success Story/OfficeDepot,” Donders said that the initial implementation went well enough that the team wanted to expand the scope to include more data. “With our old way of working, this would have given rise to a lot more work,” said Donders, “but now we’ve set up a structure and tools, we only need to set up the product data once.”

Office Depot is now using data brokers as a way of centralizing and standardizing product data. “There are pros and cons to this as the data becomes available to our competitors, so of course you lose some control,” he said, “but it allows us to deliver the right data to our customers. Moreover, the broker arranges the data, so we only need to concern ourselves with publishing it online. It’s also good for our suppliers as they only have to supply the data to a single party, not to dozens of resellers.”

Efficiency and accuracy have improved significantly, he said. “By implementing a content management system that’s fed with data from MDM, we’re able to develop our online catalogue thirty to forty percent faster.”

The online customer experience has improved as all data is now standardized across all channels. “Using master data has also improved our online search functions, meaning that our customers can now find products more easily with fewer searches being abandoned prematurely. As our websites are now fed with MDM, rather than manually, we’ve also managed to reduce the number of errors,” Donders said.

Best Practices

Copeland shared some of what Office Depot learned from the process. “We are very early on in our journey, but all team members involved have had a seat at the table and have been encouraged to be very vocal,” he said. “We continue to have candid conversations to ensure that we are designing the best solution.”

He also noted that an ongoing evaluation of the systems that provide, manage, and govern their data is a key part of success going forward. “Implementing a new technology is only one piece of the puzzle. A significant portion of the work from this project is undergoing a robust process redesign effort.

We are looking hard at the processes, roles and responsibilities to ensure that we are bringing best-in-class influenced processes, which the MDM technology will enable,” he said. “This also means looking at the organization and identifying gaps from best-in-class. For example, we recognize that we need more robust Data Governance to ensure that we aren’t making short-term decisions with negative long-term impacts.”

Moving Forward

“Given the early stage of the project, next steps include further socialization of the project internally and externally with our suppliers, refinement of the plan and continued execution on the foundational aspects of the project,” said Copeland.

Leave a Reply Cancel reply

You must be logged in to post a comment.

• Awards and Recognition
• Board of Directors
• Client Stories
• Corporate Social Responsibility
• Protecting Our Planet
• Enabling People to Outperform
• Caring for Our Communities
• Ethics, Integrity and Compliance
• Delivering Value to Our Clients
• From the CEO
• Global Partner Ecosystem
• South Africa
• United States
• The Philippines
• United Arab Emirates
• United Kingdom
• Management Team
• Press Releases
• WNS in the News
• Our Proposition
• Vision and Mission
• Generative AI
• ESG Analytics
• Augmented Data Management and Data Governance
• BI & Visualization
• Big Data Engineering
• Data Governance Intelligent Cloud
• Data Sciences
• Embedded Analytics
• Fraud And Risk Analytics
• HR Data Analytics
• Customer Loyalty Analytics
• Marketing Analytics Services
• Insurance Analytics
• Retail Media
• Centers of Excellence
• Customer Experience Consulting
• HIFFI – Home Insurance in a Jiffy
• Digitally Simulated Training Environment
• WNS Open Talent
• Decision Support
• Financial Planning and Analysis (FP&A)
• WNS Duplicate Invoice Detector
• WNS Forecaster and Predictor
• WNS Journal Entry Analytics
• WNS Risk and Audit Analytics
• WNS Working Capital Analytics
• Outperforming CFO Framework (OCF)
• Procure-to-Pay (P2P)
• Quote-to-Sustain (QtS)
• WNS TRAC eClose
• Anti-Money Laundering and Know Your Customer
• Financial Crime
• Fraud and Risk Analytics
• Fraud Management Services
• Regulatory Compliance and Risk Reporting
• Third-Party Risk Management
• Compensation
• HR Analytics
• Learning and Performance Management
• Payroll Administration
• Recruitment Services
• Workforce Management
• Legal Services
• Category Management
• Contracting Services
• Digital Solutions
• Procure-To-Pay Services
• Sourcing Services
• Supplier Management
• Supply Chain Management
• Robotics & Digital Automation TRAC
• Supplier Portal
• Technology Services
• WNS IP Labs
• Insurance TRAC
• RPA & Intelligent Automation TRAC
• Security TRAC
• Travel TRAC
• Analytics Incubation Center
• Big Data Analytics
• Data Sciences Group
• Process Maturity Model
• Value Innovation Program
• WNS Transition
• Workforce And Productivity Management
• Industry Segments
• Capital Markets Operations
• Research and Analysis Services
• Trade Finance
• Financial Crime and Compliance (FCC)
• FinTech Services
• Mortgage Operations
• Retail Banking
• Banking Analytics
• Banking and Financial Services Consulting
• Credit Risk Management and Underwriting Services
• Customer Experience in BFS
• Finance & Accounting for Banks & Financial Institutions

Governance, Risk and Compliance

• Model Risk Management
• Customer Acquisition and Customer Experience Management Services
• Driving Insight-led Decisions
• Realizing Your Energy and Utility Digital Transformation Goals
• Carbon Accounting
• Digital Customer Experience Services
• Digital Meter-to-Cash
• Smart Collection and Debt Management Services
• Elevating Business Operations with Shared Services
• Customer Management Services
• Energy & Utilities Analytics
• Shared Services
• Customer Analytics
• Data Management
• Revenue Assurance
• Risk Analytics
• Video and Voice AI Solution
• Benefits Administration
• Clinical Operations
• Healthcare Claims
• Medical Bill Review
• Member Engagement Services
• Provider Lifecycle Management
• Revenue Cycle Management
• Telehealth Services
• Content and Information Publishers
• Executive Search Services
• Hi-Tech Firms
• ID and Background Verification Firms
• Legal Service Firms
• Risk and Compliance Service Providers
• Design Services
• Hi-Tech & Professional Services Analytics
• Product Data Operations
• Revenue Operations
• Pension Risk Transfer
• Lloyd’s of London Market
• Claims Prevention
• Straight-through Processing (STP)
• Insurance Finance & Accounting
• WNS Claims 360
• Voice AI Solution
• Finance and Accounting
• Procurement
• Research and Analytics
• Pharmacovigilance
• Competitive Intelligence
• Forecasting
• Business Intelligence
• Marketing & Pricing Analytics
• Sales Analytics
• Commercial Assessment
• Customer Experience
• Finance & Accounting
• Supply Chain Management (SCM)
• CPG & Retail Analytics
• Integrated Sales Order Management
• Forwarders and Brokers
• Safety and Compliance
• WNS Air Cargo Optimized Solutions Suite (ACOSS)
• WNS Document Verification Solution
• WNS Volume Surge Solution
• Hospitality Companies
• Online Travel Agencies
• Travel Management Companies
• Travel and Leisure Analytics
• WNS Ancillary Revenue Optimizer Suite
• WNS Post-booking Servicing Solution
• Verifare Plus
• Private Equity
• WNS Co-creation Labs

Perspectives

Master Data Management Transforms Customer Experience for a Leading Bank

01 november 2022, a wns perspective.

A leading US bank wanted a unified customer view across multiple lines of business to deliver personalized experiences and capitalize on upselling / cross-selling opportunities.

The bank required a ‘golden record’ for its customer data to reduce risk incidents and strengthen compliance.

WNS Triange – our data, analytics and AI practice – co-created an analytics-led master data management solution to integrate customer data across disparate systems and create a unified repository to power actionable insights.

This is our story of co-creating an analytics-led Master Data Management (MDM) solution to drive a 360-degree customer view for a leading US bank.

As we know…

Banks tend to house customer data across multiple systems. However, most of these systems work in silos, preventing banks from gaining a unified customer view. This has several repercussions – from creating various profiles of the same customer to targeting unintended recipients with communication and promotional campaigns to missing out on upselling and cross-selling opportunities. Banks also run the risk of incurring penalties and facing irreversible reputational damage. An integrated data management solution becomes inevitable in a scenario like this.

The challenge for the client was…

To achieve a single aggregated view of customer data across various Lines of Business (LOB) and applications. The bank required a ‘golden record’ of high-quality data points to serve customers in a more personalized, friction-free way while also improving data governance.

The bank wanted a ‘single source of truth’ for its customer data, leading to accurate insights, reduced data redundancy, improved upselling / cross-selling and enhanced collaboration between business units. It also wanted to avoid communication errors, e.g., sending payment reminders to the wrong recipients and upsetting loyal customers. The module was expected to support compliance-related reporting as well.

As the consulting partner…

We leveraged Triange Consult , a key pillar of WNS Triange (our data, analytics and AI practice), to provide advisory services to the client. We helped the client identify the right technologies and define the end-to-end architecture for the MDM solution.

As the co-creation partner…

With Triange NxT , another critical pillar of WNS Triange, we combined best practices, and proprietary frameworks and accelerators to implement an analytics-led MDM solution. The solution integrated customer data residing across various disparate systems. It acted as a single repository, cleansing and providing a ‘golden record’ with relevant, latest and high-quality data points for each customer.

Key aspects of our MDM solution included:

Proprietary accelerators such as data profiling tools, data matching and merging algorithms, and knowledge graphs enabling quick delivery and better accuracy

Custom user interface for data stewards to manage system failures and conflicts

Robust data governance practice with an added layer of security to ensure the highest data standards

Data security and entitlement framework to provide the right access to the right users

Metadata repository for quick reference of data elements and documentation

Data consumption layer with application programming interfaces for MDM consumers

Data-led insights from a single, authentic repository…

Enabled the bank to improve customer experience significantly. Authorized users across LOBs could drive targeted growth through personalization. Other benefits included:

95 percent increase in data quality scores by reducing duplicates and data anomalies

Higher returns on analytics investment

Robust data governance

Potential benefits included a 50 percent reduction in risk incidents and a 10 percent increase in upselling and cross-selling opportunities. Moreover, our flexible engagement model allowed the bank to fine-tune the solution on the fly.

About WNS Triange:

WNS Triange (formerly WNS Research and Analytics practice) powers business growth and innovation for 120+ global companies with data, analytics and Artificial Intelligence (AI). Driven by a specialized team of over 4000 analysts, data scientists and domain experts, WNS Triange helps translate data into actionable insights for impactful decision-making. Built on the pillars of consulting ( Triange Consult ), future-ready platforms ( Triange Nxt ), and domain and technology ( Triange CoE ), WNS Triange seamlessly blends strategy, industry-specific nuances, AI and Machine Learning (ML) operations, and intelligent cloud platforms.

Driving a futuristic edge are WNS Triange’s modular cloud-based platforms and solutions leveraging advanced AI and ML to provide end-to-end integration and processing of data to actionable insights. WNS Triange leverages the combined strength of WNS’ domain expertise, co-creation labs, strategic partnerships and outcome-based engagement models.

Join the conversation

Related To:

Similar perspectives.

Banking and Financial Services

Mastering the Art of Data Management to Transform Customer…

03 December 2021

4 Effective GRC Strategies for a Complex Business Landscape

05 April 2024

Generative Artificial Intelligence

5 Insights on How Enterprises Are Leveraging Gen AI in CXM

27 March 2024

• Master data management case study

Master data management

• Insights Landing page

Helping asset managers in acquiring, managing, and enriching the most useful information for accurate business attribution

The client needed support on their advisor contacts and relationships data management process.

Most of the advisor contacts were from large broker dealers or wire houses having multiple offices and advisors spread across different locations in the US and globally.

The source data came from multiple external publishers that needed to be consolidated through the MDM process and eventually consumed by the business end-users in CRM for tracking and analysis purposes.

Following are few problems the client was facing:

• Data management process
• Support migration process to new CRM system
• Support CRM user requests on any data issue or streamlining requirement

Read more...

FOCUSED ON YOUR SUCCESS

Case Studies Getting Buy-In Customer Experience

Architect BI/Analytics

MICROSOFT ENTERPRISE

The Profisee Platform

Get an overview of Profisee's cloud-native modern MDM platform — and learn why we're the obvious choice for master data management. Learn More

See the benefits of modern MDM

Connect any data source

Quickly & seamlessly manage data

Enforce governance rules & activate metadata

Seamlessly define & manage complex relationships

Create trusted golden records for your business

Maintain and measure data quality

Automate tasks and approvals

See all platform features

Native integrations with Azure apps and services

SaaS, PaaS, hybrid, on-prem

No-surprise, flexible pricing

Azure, AWS, GCP or any cloud

Leverage our expert team

Make it easy, make it accurate, make it scale

Our vision for the future of MDM

PROFISEE PARTNERS

Partners Home Cloud Partners Find a Partner

2023 DATA HERO SUMMIT

Watch the full Data Hero Summit on-demand to see leading data experts share their experiences and heroic stories. WATCH ON-DEMAND

Analyst reports, guides and other tools

The latest on data management & more

Join MDM experts live or on-demand

Relevant news from across the industry

How industry leaders use trusted data

Best-in-class training & support

Hear from data leaders first-hand

A chance to step into the discussions as they happen

• Case Studies

Learn More About MDM

Harvard Business Review Report: Data Readiness for the AI Revolution

Join our expert panel covering the 2024 HBR Pulse Survey Report: Data Readiness for the AI Revolution including a live Q&A session.

A Practical Blueprint for AI Enablement with MDM & Microsoft Fabric

Featured Resources

Master Data Management Software Evaluation Template

Top 30+ Master Data Management Tools [Updated 2023]

What is Master Data Management | Definition, Tools, Solutions [Updated 2023]

Harvard Business Review | The Path to Trustworthy Data

YMCA of the USA: Improving Staff Management Through MDM

2024 State of Master Data Management

CDO Matters LIVE | Data Products

Mass General Brigham: Streamlining the Patient Experience

CDO Matters LIVE | The State of MDM in 2024

The Playbook for Modern Data Leadership

Apogee: Driving Efficiency with Master Data Management

How to Drive AI Enablement with Profisee MDM in Microsoft Fabric ? [In 4 Minutes]

Playbook for Modern Data Leadership | CDO Matters Live

From Data to Decisions: The Synergy of MDM, Fabric and AI

The Essential Role of Governance & MDM In Microsoft Fabric

Azure Marketplace

Adaptive Stewardship

Microsoft Fabric and the Future of Data Management | CDO Matters Live

Business Impact Assessment

Hear from the Heroes! Profisee Customers at the 2023 Data Hero Summit

Data Hero Summit Keynote: The Data Hero In You | Determining Truth Before Meaning

Microsoft Ignite 2023

Why Profisee is the Best MDM for Azure + Microsoft Fabric Demo

Microsoft Tribal Nations

Why is Profisee MDM the Obvious Choice for Microsoft Customers? [In 3 Minutes]

Ways to get started, resources for your journey, speak to an mdm expert, explore the profisee platform, see profisee in action, schedule a custom demo.

Get a tailored demonstration of the industry’s only Adaptive MDM Platform and get answers to your questions.

SCHEDULE DEMO

Profisee Master Data Management (MDM) is how enterprises can finally solve the data quality issues that have been holding back so many strategic initiatives.

Corporate Office

Register Now for the
Data Hero Summit

Privacy Policy    |    Web Accessibility    |    Sitemap

Copyright © 2024 Profisee. All Rights Reserved.

LET'S DO THIS!

Complete the form below to request your spot at Profisee’s happy hour and dinner at Il Mulino in the Swan Hotel on Tuesday, March 21 at 6:30pm .

NEW EPISODE

Top Data Trends of 2023

with Wendy Turner-Williams, Chief Data & AI Officer

Join Profisee at our upcoming event:

REGISTER BELOW

Data Analytics Case Study Guide 2024

by Sam McKay, CFA | Data Analytics

Data analytics case studies reveal how businesses harness data for informed decisions and growth.

For aspiring data professionals, mastering the case study process will enhance your skills and increase your career prospects.

So, how do you approach a case study?

Use these steps to process a data analytics case study:

Understand the Problem: Grasp the core problem or question addressed in the case study.

Collect Relevant Data: Gather data from diverse sources, ensuring accuracy and completeness.

Apply Analytical Techniques: Use appropriate methods aligned with the problem statement.

Visualize Insights: Utilize visual aids to showcase patterns and key findings.

Derive Actionable Insights: Focus on deriving meaningful actions from the analysis.

This article will give you detailed steps to navigate a case study effectively and understand how it works in real-world situations.

By the end of the article, you will be better equipped to approach a data analytics case study, strengthening your analytical prowess and practical application skills.

Let’s dive in!

Table of Contents

What is a Data Analytics Case Study?

A data analytics case study is a real or hypothetical scenario where analytics techniques are applied to solve a specific problem or explore a particular question.

It’s a practical approach that uses data analytics methods, assisting in deciphering data for meaningful insights. This structured method helps individuals or organizations make sense of data effectively.

Additionally, it’s a way to learn by doing, where there’s no single right or wrong answer in how you analyze the data.

So, what are the components of a case study?

Key Components of a Data Analytics Case Study

A data analytics case study comprises essential elements that structure the analytical journey:

Problem Context: A case study begins with a defined problem or question. It provides the context for the data analysis , setting the stage for exploration and investigation.

Data Collection and Sources: It involves gathering relevant data from various sources , ensuring data accuracy, completeness, and relevance to the problem at hand.

Analysis Techniques: Case studies employ different analytical methods, such as statistical analysis, machine learning algorithms, or visualization tools, to derive meaningful conclusions from the collected data.

Insights and Recommendations: The ultimate goal is to extract actionable insights from the analyzed data, offering recommendations or solutions that address the initial problem or question.

Now that you have a better understanding of what a data analytics case study is, let’s talk about why we need and use them.

Why Case Studies are Integral to Data Analytics

Case studies serve as invaluable tools in the realm of data analytics, offering multifaceted benefits that bolster an analyst’s proficiency and impact:

Real-Life Insights and Skill Enhancement: Examining case studies provides practical, real-life examples that expand knowledge and refine skills. These examples offer insights into diverse scenarios, aiding in a data analyst’s growth and expertise development.

Validation and Refinement of Analyses: Case studies demonstrate the effectiveness of data-driven decisions across industries, providing validation for analytical approaches. They showcase how organizations benefit from data analytics. Also, this helps in refining one’s own methodologies

Showcasing Data Impact on Business Outcomes: These studies show how data analytics directly affects business results, like increasing revenue, reducing costs, or delivering other measurable advantages. Understanding these impacts helps articulate the value of data analytics to stakeholders and decision-makers.

Learning from Successes and Failures: By exploring a case study, analysts glean insights from others’ successes and failures, acquiring new strategies and best practices. This learning experience facilitates professional growth and the adoption of innovative approaches within their own data analytics work.

Including case studies in a data analyst’s toolkit helps gain more knowledge, improve skills, and understand how data analytics affects different industries.

Using these real-life examples boosts confidence and success, guiding analysts to make better and more impactful decisions in their organizations.

But not all case studies are the same.

Let’s talk about the different types.

Types of Data Analytics Case Studies

Data analytics encompasses various approaches tailored to different analytical goals:

Exploratory Case Study: These involve delving into new datasets to uncover hidden patterns and relationships, often without a predefined hypothesis. They aim to gain insights and generate hypotheses for further investigation.

Predictive Case Study: These utilize historical data to forecast future trends, behaviors, or outcomes. By applying predictive models, they help anticipate potential scenarios or developments.

Diagnostic Case Study: This type focuses on understanding the root causes or reasons behind specific events or trends observed in the data. It digs deep into the data to provide explanations for occurrences.

Prescriptive Case Study: This case study goes beyond analytics; it provides actionable recommendations or strategies derived from the analyzed data. They guide decision-making processes by suggesting optimal courses of action based on insights gained.

Each type has a specific role in using data to find important insights, helping in decision-making, and solving problems in various situations.

Regardless of the type of case study you encounter, here are some steps to help you process them.

Roadmap to Handling a Data Analysis Case Study

Embarking on a data analytics case study requires a systematic approach, step-by-step, to derive valuable insights effectively.

Here are the steps to help you through the process:

Step 1: Understanding the Case Study Context: Immerse yourself in the intricacies of the case study. Delve into the industry context, understanding its nuances, challenges, and opportunities.

Identify the central problem or question the study aims to address. Clarify the objectives and expected outcomes, ensuring a clear understanding before diving into data analytics.

Step 2: Data Collection and Validation: Gather data from diverse sources relevant to the case study. Prioritize accuracy, completeness, and reliability during data collection. Conduct thorough validation processes to rectify inconsistencies, ensuring high-quality and trustworthy data for subsequent analysis.

Step 3: Problem Definition and Scope: Define the problem statement precisely. Articulate the objectives and limitations that shape the scope of your analysis. Identify influential variables and constraints, providing a focused framework to guide your exploration.

Step 4: Exploratory Data Analysis (EDA): Leverage exploratory techniques to gain initial insights. Visualize data distributions, patterns, and correlations, fostering a deeper understanding of the dataset. These explorations serve as a foundation for more nuanced analysis.

Step 5: Data Preprocessing and Transformation: Cleanse and preprocess the data to eliminate noise, handle missing values, and ensure consistency. Transform data formats or scales as required, preparing the dataset for further analysis.

Step 6: Data Modeling and Method Selection: Select analytical models aligning with the case study’s problem, employing statistical techniques, machine learning algorithms, or tailored predictive models.

In this phase, it’s important to develop data modeling skills. This helps create visuals of complex systems using organized data, which helps solve business problems more effectively.

Understand key data modeling concepts, utilize essential tools like SQL for database interaction, and practice building models from real-world scenarios.

Furthermore, strengthen data cleaning skills for accurate datasets, and stay updated with industry trends to ensure relevance.

Step 7: Model Evaluation and Refinement: Evaluate the performance of applied models rigorously. Iterate and refine models to enhance accuracy and reliability, ensuring alignment with the objectives and expected outcomes.

Step 8: Deriving Insights and Recommendations: Extract actionable insights from the analyzed data. Develop well-structured recommendations or solutions based on the insights uncovered, addressing the core problem or question effectively.

Step 9: Communicating Results Effectively: Present findings, insights, and recommendations clearly and concisely. Utilize visualizations and storytelling techniques to convey complex information compellingly, ensuring comprehension by stakeholders.

Step 10: Reflection and Iteration: Reflect on the entire analysis process and outcomes. Identify potential improvements and lessons learned. Embrace an iterative approach, refining methodologies for continuous enhancement and future analyses.

This step-by-step roadmap provides a structured framework for thorough and effective handling of a data analytics case study.

Now, after handling data analytics comes a crucial step; presenting the case study.

Presenting Your Data Analytics Case Study

Presenting a data analytics case study is a vital part of the process. When presenting your case study, clarity and organization are paramount.

To achieve this, follow these key steps:

Structuring Your Case Study: Start by outlining relevant and accurate main points. Ensure these points align with the problem addressed and the methodologies used in your analysis.

Crafting a Narrative with Data: Start with a brief overview of the issue, then explain your method and steps, covering data collection, cleaning, stats, and advanced modeling.

Visual Representation for Clarity: Utilize various visual aids—tables, graphs, and charts—to illustrate patterns, trends, and insights. Ensure these visuals are easy to comprehend and seamlessly support your narrative.

Highlighting Key Information: Use bullet points to emphasize essential information, maintaining clarity and allowing the audience to grasp key takeaways effortlessly. Bold key terms or phrases to draw attention and reinforce important points.

Addressing Audience Queries: Anticipate and be ready to answer audience questions regarding methods, assumptions, and results. Demonstrating a profound understanding of your analysis instills confidence in your work.

Integrity and Confidence in Delivery: Maintain a neutral tone and avoid exaggerated claims about findings. Present your case study with integrity, clarity, and confidence to ensure the audience appreciates and comprehends the significance of your work.

By organizing your presentation well, telling a clear story through your analysis, and using visuals wisely, you can effectively share your data analytics case study.

This method helps people understand better, stay engaged, and draw valuable conclusions from your work.

We hope by now, you are feeling very confident processing a case study. But with any process, there are challenges you may encounter.

Key Challenges in Data Analytics Case Studies

A data analytics case study can present various hurdles that necessitate strategic approaches for successful navigation:

Challenge 1: Data Quality and Consistency

Challenge: Inconsistent or poor-quality data can impede analysis, leading to erroneous insights and flawed conclusions.

Solution: Implement rigorous data validation processes, ensuring accuracy, completeness, and reliability. Employ data cleansing techniques to rectify inconsistencies and enhance overall data quality.

Challenge 2: Complexity and Scale of Data

Challenge: Managing vast volumes of data with diverse formats and complexities poses analytical challenges.

Solution: Utilize scalable data processing frameworks and tools capable of handling diverse data types. Implement efficient data storage and retrieval systems to manage large-scale datasets effectively.

Challenge 3: Interpretation and Contextual Understanding

Challenge: Interpreting data without contextual understanding or domain expertise can lead to misinterpretations.

Solution: Collaborate with domain experts to contextualize data and derive relevant insights. Invest in understanding the nuances of the industry or domain under analysis to ensure accurate interpretations.

Challenge 4: Privacy and Ethical Concerns

Challenge: Balancing data access for analysis while respecting privacy and ethical boundaries poses a challenge.

Solution: Implement robust data governance frameworks that prioritize data privacy and ethical considerations. Ensure compliance with regulatory standards and ethical guidelines throughout the analysis process.

Challenge 5: Resource Limitations and Time Constraints

Challenge: Limited resources and time constraints hinder comprehensive analysis and exhaustive data exploration.

Solution: Prioritize key objectives and allocate resources efficiently. Employ agile methodologies to iteratively analyze and derive insights, focusing on the most impactful aspects within the given timeframe.

Recognizing these challenges is key; it helps data analysts adopt proactive strategies to mitigate obstacles. This enhances the effectiveness and reliability of insights derived from a data analytics case study.

Now, let’s talk about the best software tools you should use when working with case studies.

Top 5 Software Tools for Case Studies

In the realm of case studies within data analytics, leveraging the right software tools is essential.

Here are some top-notch options:

Tableau : Renowned for its data visualization prowess, Tableau transforms raw data into interactive, visually compelling representations, ideal for presenting insights within a case study.

Python and R Libraries: These flexible programming languages provide many tools for handling data, doing statistics, and working with machine learning, meeting various needs in case studies.

Microsoft Excel : A staple tool for data analytics, Excel provides a user-friendly interface for basic analytics, making it useful for initial data exploration in a case study.

SQL Databases : Structured Query Language (SQL) databases assist in managing and querying large datasets, essential for organizing case study data effectively.

Statistical Software (e.g., SPSS , SAS ): Specialized statistical software enables in-depth statistical analysis, aiding in deriving precise insights from case study data.

Choosing the best mix of these tools, tailored to each case study’s needs, greatly boosts analytical abilities and results in data analytics.

Final Thoughts

Case studies in data analytics are helpful guides. They give real-world insights, improve skills, and show how data-driven decisions work.

Using case studies helps analysts learn, be creative, and make essential decisions confidently in their data work.

Check out our latest clip below to further your learning!

Frequently Asked Questions

What are the key steps to analyzing a data analytics case study.

When analyzing a case study, you should follow these steps:

Clarify the problem : Ensure you thoroughly understand the problem statement and the scope of the analysis.

Make assumptions : Define your assumptions to establish a feasible framework for analyzing the case.

Gather context : Acquire relevant information and context to support your analysis.

Analyze the data : Perform calculations, create visualizations, and conduct statistical analysis on the data.

Provide insights : Draw conclusions and develop actionable insights based on your analysis.

How can you effectively interpret results during a data scientist case study job interview?

During your next data science interview, interpret case study results succinctly and clearly. Utilize visual aids and numerical data to bolster your explanations, ensuring comprehension.

Frame the results in an audience-friendly manner, emphasizing relevance. Concentrate on deriving insights and actionable steps from the outcomes.

How do you showcase your data analyst skills in a project?

To demonstrate your skills effectively, consider these essential steps. Begin by selecting a problem that allows you to exhibit your capacity to handle real-world challenges through analysis.

Methodically document each phase, encompassing data cleaning, visualization, statistical analysis, and the interpretation of findings.

Utilize descriptive analysis techniques and effectively communicate your insights using clear visual aids and straightforward language. Ensure your project code is well-structured, with detailed comments and documentation, showcasing your proficiency in handling data in an organized manner.

Lastly, emphasize your expertise in SQL queries, programming languages, and various analytics tools throughout the project. These steps collectively highlight your competence and proficiency as a skilled data analyst, demonstrating your capabilities within the project.

Can you provide an example of a successful data analytics project using key metrics?

A prime illustration is utilizing analytics in healthcare to forecast hospital readmissions. Analysts leverage electronic health records, patient demographics, and clinical data to identify high-risk individuals.

Implementing preventive measures based on these key metrics helps curtail readmission rates, enhancing patient outcomes and cutting healthcare expenses.

This demonstrates how data analytics, driven by metrics, effectively tackles real-world challenges, yielding impactful solutions.

Why would a company invest in data analytics?

Companies invest in data analytics to gain valuable insights, enabling informed decision-making and strategic planning. This investment helps optimize operations, understand customer behavior, and stay competitive in their industry.

Ultimately, leveraging data analytics empowers companies to make smarter, data-driven choices, leading to enhanced efficiency, innovation, and growth.

Related Posts

4 Types of Data Analytics: Explained

Data Analytics

In a world full of data, data analytics is the heart and soul of an operation. It's what transforms raw...

Data Analytics Outsourcing: Pros and Cons Explained

In today's data-driven world, businesses are constantly swimming in a sea of information, seeking the...

What Does a Data Analyst Do on a Daily Basis?

In the digital age, data plays a significant role in helping organizations make informed decisions and...

Baylor International Studies

Case management, immigration law, and a willingness to learn: an interview with charlie kruljac.

by Tatum Koster (’24)

Kruljac started his time working during undergrad and quickly discovered a passion for teaching. He began a job tutoring student athletes, which as a result connected him more with his peers. Wanting to become more involved with his peers, he became a global engagement ambassador at Baylor University. Kruljac enjoyed welcoming new international students into Baylor and helping them immerse in the culture. After graduation, Kruljac worked with Teach for America from 2019-2021, where he taught at underprivileged schools. As well as work experience throughout his undergraduate years, he gained volunteer experience with the International Institute of St. Louis, where Kruljac is still involved today. Kruljac has taught courses during his time there, including a “bridge to college class”. Kruljac still pursues this passion of teaching today.

After Kruljac’s years of various job and volunteer experiences, he discovered an interest in a career that requires working with families. He is now a case manager at Cofman & Bolourchi, LLC. In Kruljac’s current work, he works closely with attorneys in the field of immigration law. He prepares the filings for the attorneys and contacts the families they are working with to verify any information. When asked what major challenges he deals with on a daily basis, Kruljac said the workload is very busy and there is a rushed feeling. In addition, another challenge Kruljac faces is the “difficulty of working with a complicated and dysfunctional immigration system across cultural and linguistic lines of difference”. While working as a case manager, Kruljac is applying to law schools, including Baylor Law. He hopes to pursue a career in immigration law. Kruljac has years of experience to prepare him for this role.

When asked to give advice to current students, Kruljac expressed the importance of discovering what you’re interested in and passionate about and making a willingness to learn the subject. Kruljac remembers his time at Baylor through courses completed such as Comparative Politics, a semester-long study abroad program in Mendoza, and his time in the Baylor Interdisciplinary core.

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

2024 - Supercharging social protection systems with anticipatory cash: Case study on Fiji’s Anticipatory Action Framework

This paper explains how, through close collaboration with the Fijian Government and other relevant stakeholders, WFP has developed a first-of-its-kind system where 100 percent of WFP’s anticipatory cash assistance will be channelled through the country’s existing social protection infrastructure. By building on the country’s disaster risk management capacities and leveraging the Government’s existing social protection infrastructure, the initiative promotes local ownership—firmly placing the Fijian Government and people in the driver’s seat of proactive disaster mitigation.

Christoph Baade and Osborne Sibande, 2024, Supercharging social protection systems with anticipatory cash: Case study on Fiji’s Anticipatory Action Framework

Publications

Integrating anticipatory action and social protection.

2023 Anticipatory Action Activations

Scaling up anticipatory actions for food security: Anticipatory Action Year in Focus 2022

Advanced search