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'm examining the intersections between MDM and other data management disciplines. Part 1 discussed data quality. Part 2 concludes the series with a focus on data governance.
How MDM intersects with data governance
Data governance provides a framework for the communication and collaboration of business, data and technical stakeholders. It establishes an enterprisewide understanding of the roles and responsibilities involved. It also specifies the accountability required to support business activities and to materialize the value of enterprise data as positive business impacts.
Data governance, therefore, must eventually intersect with all data management disciplines. MDM is no exception. Data governance provides the guiding principles and context-specific policies that frame the processes and procedures of MDM. An example of a guiding principle is “master data will be managed as a shared asset to maximize business value and reduce risk.” Policies provide context to the specific business uses of master data, such as the different ways billing and marketing define who a customer is, and who has the authority to access sensitive data (e.g., social security and credit card numbers) describing those customers.
Data governance connects the dots between these principles and policies and MDM processes and procedures. The intended result is to make sure principles are followed, policies are enforced, and any and all changes are well-communicated across the organization. The glue that makes sure data governance sticks is data stewardship. Data stewards are those essential individuals responsible for such tasks as interpreting data profiling reports, conducting meetings about data quality requirements, and providing two-way translations of the business and technology aspects of data governance policies. MDM’s data stewards are often aligned with a single master data domain (i.e., business entity type, such as product) or sub-domain (e.g., the customer role within party).
How has data governance intersected with your MDM?
Please share your perspective and experience regarding the intersection of MDM and data governance by posting a comment below.