Data Governance First, Master Data Management Second

To reduce the risk and increase the success of your MDM project, establish a data governance program first.

To reduce the risk and increase the prospects for success when implementing a master data management (MDM) project, keep this tip in mind: Implement data governance (DG) first, master data management second.

This sequence has advantages because of the two formidable hurdles an MDM program of any size must leap over. On the one hand, MDM entails substantial collaborative overhead because people from many departments and disciplines must coordinate decisions and actions. On the other hand, MDM faces considerable organizational resistance because it requires people to change their processes and systems. The average data governance committee (or similar organizational structure) is built to handle these issues and others; the average MDM program isn’t. That’s good reason to put the two together.

Practicing MDM and DG together makes even more sense when you consider all that they have in common:

Cross-functional. Regardless of how you practice MDM, its goal is always the same: consistent, consensus-driven definitions of common business entities, like customers, products, and financials. Because the entities must be agreed to by many people and retrofitted to many IT systems, MDM is inherently a cross-business-unit and cross-IT-system program. Data governance helps tremendously in this regard, because DG is an organizational mechanism for the coordination and collaboration of people from numerous functions and disciplines, on both sides of business and IT.

Collaborative. On the technology level, there are many ways to represent and manage entity definitions, simply because there are many types of information systems. Although most organizations desire a single MDM infrastructure spanning all systems, the more common reality is to have multiple MDM solutions, each addressing a collection of similar systems. Given the number of IT systems involved, collaboration among multiple IT teams is commonly required of MDM, sometimes enabled by DG.

Data-intensive. Obviously, both MDM and DG focus on enterprise data, but MDM is a technical practice for managing data whereas DG is an organizational practice that defines policies and procedures for how enterprise data may (and may not) be used. DG may also define how data and its semantics should be improved or standardized. Greater enterprise consistency is attained when the data definitions of MDM are expressed as data usage policies via a DG program.

Data improving. MDM greatly improves data semantics, such as master, reference, and meta data, similar to how data quality and stewardships programs improve physical data. Given that improvement on this scale requires enterprise wide change, DG helps to prioritize, mandate, and manage change for the sake of data improvement.

Business transforming. MDM and DG are transformational, in that they provide improvement and change management mechanisms for the retrofitting of consistent data definitions to various IT systems. A good MDM program will go the extra mile and transform how a business thinks about key business entities and how that information is used. Both MDM and DG are often linked to other data management practices that transform data and its business, as in programs for business intelligence, data quality, customer relationship management, supply chain management, and so on.

In closing, the hefty doses of collaboration and change that MDM requires are best handled by a data governance committee or equivalent. Otherwise, the MDM program may not achieve an appropriate cross-IT-system and cross-business-unit scope. If you anticipate considerable collaborative overhead or organizational resistance, start by implementing data governance to deal with these issues, which in turn empowers MDM to focus on its primary goal: cross-system, consensus-driven definitions of common business entities, such as customers, products, and financials.

About the Author

Philip Russom, Ph.D., is senior director of TDWI Research for data management and is a well-known figure in data warehousing, integration, and quality, having published over 550 research reports, magazine articles, opinion columns, and speeches over a 20-year period. Before joining TDWI in 2005, Russom was an industry analyst covering data management at Forrester Research and Giga Information Group. He also ran his own business as an independent industry analyst and consultant, was a contributing editor with leading IT magazines, and a product manager at database vendors. His Ph.D. is from Yale. You can reach him by email (, on Twitter (, and on LinkedIn (

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