MDM intersections, Part 1: Data quality


Master data management (MDM) is distinct from other data management disciplines due to its primary focus on giving the enterprise a single view of the master data that represents key business entities, such as parties, products, locations and assets. MDM achieves this by standardizing, matching and consolidating common data elements across traditional and big data sources. In turn, it's possible to develop and maintain a consistent definition and best representation of these business entities – and share their master data – across IT systems and business units.

In this two-part series, I'll examine the intersections between MDM and other data management disciplines, starting with data quality.

How MDM intersects with data quality

crystal ball against mountains repesents data quality and MDMData quality intersects with MDM in many ways. Data profiling is used to evaluate sources for master data entities, including the performance of a baseline assessment of potential data quality issues that must be addressed. Postal validation and address verification is essential for location master data. And since most master data originates in free-form text fields (e.g., customer name, product description), the composite data elements (e.g., given name, family name, unit of measure, packaging type) must be parsed and standardized.

However, the biggest intersection between MDM and data quality is with the matching and survivorship processes used to create the best master data record to represent the single view of the business entities at the heart of MDM. These data quality rules must be customizable and supported by an interface that enables business users to interactively review, approve and document how the single view is constructed. The interface also needs to provide both metadata lineage and data linkage back to the originating master data source systems.

How has data quality intersected with your MDM?

Please share your perspective and experience regarding the intersection of MDM and data quality by posting a comment below.

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About Author

Jim Harris

Blogger-in-Chief at Obsessive-Compulsive Data Quality (OCDQ)

Jim Harris is a recognized data quality thought leader with 25 years of enterprise data management industry experience. Jim is an independent consultant, speaker, and freelance writer. Jim is the Blogger-in-Chief at Obsessive-Compulsive Data Quality, an independent blog offering a vendor-neutral perspective on data quality and its related disciplines, including data governance, master data management, and business intelligence.

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