Master Data Management: Why Do We Need It?

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I recently met someone tasked with achieving a global view of customers to improve strategic planning and positioning for a global manufacturing organization. Unfortunately, due to disparate business processes systems, a great deal of redundant data has built up in the company’s production systems, causing higher maintenance costs, revenue loss from outdated/missing prices, customer dissatisfaction from inaccurate pricing/billing, and compliance issues from incorrect tax/license/documentation.

This is a common issue that requires relatively simple data clean up. The bigger problem is how to improve the business processes so the cleanup process does not have to be repeated. What’s needed is a single source of truth, which we refer to as Master Data Management (MDM).

MDM solutions address the core data issues that plague large enterprises by helping customers create and maintain consistent, accurate lists of master data. The solutions combine process, governance, technology, and architecture capabilities to deliver business-enabled technology solutions that transform a complex, redundant, inconsistent data landscape into one that’s agile, reliable, and cost-effective.

MDM benefits include:

  • Improved strategic decisionmaking based on reliable, consistent data
  • Streamlined data operations and improved data architecture
  • Moving to a data-as-a-service model
  • More accurate internal and external reporting with reduced compliance risk
  • Improved end-user experience (a 360-degree view of the end user to better serve their needs)
  • High-value, reusable data services for internal and external use
  • A greater degree of business agility to adapt to changing markets and new requirements
  • Lower total cost of ownership of existing and new data and IT investments

It is important to note that true MDM includes both creating and maintaining master data. Investing time, money, and effort in creating a clean, consistent set of master data is a wasted effort unless the solution includes tools and processes to keep that data clean and consistent as it’s updated and expanded. That’s why the MDM journey involves discipline, clearly defined processes, business drivers, accountability, and governance of master data by data stewards. Indeed, data governance is the key factor for a successful journey.

MDM helps companies manage core information about their business—customers, suppliers, products, agents, accounts, and the relationships between these items. It also helps enterprises gain control over business information by helping them create and  maintain a complete, accurate view of their master data, sometimes called a gold copy.

MDM also helps companies extract maximum value from their master data by centralizing multiple data domains and providing a comprehensive set of prebuilt business services that support a full range of MDM functionality.

MDM is most effective when applied to all the master data in an organization. However, in many cases, the risk and expense of an enterprise-wide effort can be difficult to justify. It’s often easier to start with a few key sources of master data and expand the effort once success has been demonstrated and lessons have been learned.

If you do start small, be sure to conduct a thorough analysis of all the master data you might eventually want to include. This will prevent you from making design decisions or tool choices that will force you to start over when you try to incorporate a new data source. For example, if your initial customer master implementation only includes the 20,000 customers your direct sales force deals with, you want to avoid any decision that will preclude adding your other 10,000,000 customers later on.

I’ll delve deeper into MDM in upcoming posts, including discussion of use cases.

Post Date: 4/27/2016

Prakash Mishra - NTT DATA Prakash Mishra

About the author

Prakash Mishra leads NTT DATA’s Data Architecture and Management Practice. A solutions-driven, results-oriented, self-motivated leader, Prakash has a proven record of extensive data architecture leadership in a complex environment. Prakash has been involved in developing and leading the implementation of traditional and innovative big data strategies and solutions, data modernization and master data management solutions for small to large organizations. He holds a master’s degree in computer science, with two decades of experience specialized in enterprise data architecture and management.