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        /  Skill   /  Data Governance Activities needed by MDM

      Data Governance Activities needed by MDM

      Many organizations seem to be confused about how to implement a master data management (MDM) program. The MDM program is complex given that it truly is an enterprise-level program especially if you are considering customer data. Not that product data is not a great challenge, however, customer data has greater touch points culturally, organizationally, externally, and in application environments. While as an industry, we have been implementing MDM programs for over 2 decades, if you are just starting one in your company it can be a daunting and risky effort.

      We often see this use case driven by the technology organization as this use case is a technology problem. Hidden in this use case is often a large problem with reporting and analytics quality and consistency.

      So why should we consider the above MDM implementation cases as data governance use cases? Why MUST one do a bit of data governance in every MDM project? Let’s look at what resources, processes, data, and metadata are needed to make the MDM project successful. And, of that, what should be provided by your data governance processes and resources?

      Simply an MDM implementation requires the following data governance activities:

      • Agreement on the business and technical resources that will be accountable for the mastered data. It is necessary to have one resource (person, business unit, or committee) to be accountable for the data in the MDM “golden record”. This is a data governance activity and the results should be maintained in the business glossary.

      • Agreement on the data that will be mastered and the associated metadata (data length, type, etc.). The decision may not be a simple one as all business unit functions and usages of the mastered data must be considered. This is often a technology decision and may impact existing application systems. This too should be maintained in your business glossary.

      • Agreement on the business rules and policies that will be enforced for the management of the “golden record data.” The business rules that govern the creation, management, archive, and disposition of the customer data may vary by the legacy application and business unit functions, as well as regulations that govern and individual business unit. These rules and policies need to address all of the needs of all business units. These rules should be maintained in the business glossary.

      • Consensus on the data quality rules for the golden record data. This one can be a challenge given that all application and business unit usages must be factored into the decision. Defining data quality and the thresholds are a data governance activity. Agreement on the profiling rules and sources for determining the quality level is also a data governance activity. Data quality metrics should be maintained in the business glossary, so all data consumers are aware of the level of quality prior to their usage.

      • Agreement on the resources that will function as data stewards to maintain the mastered data and validate merge/purge records to maintain the best data in the golden record. Yes, these are the data governance resources in both the technology and business teams. You may need stewards from many business units to manage customer MDM data. The roles and responsibilities, as well as policies from data governance, should be used by the governance resources.

      • Consensus on the processes and standards that will be used to manage data issues for the MDM data. Management of data issues is a functionality that the data governance team should provide to the MDM program.

      • Agreement on the measures and metrics that will be used. These may be metrics in the progress of the MDM implementation, metrics for data quality, metrics for data issues, or metrics for the governance of the MDM data. Again, this is a critical deliverable from data governance and the metrics should be maintained in the business glossary.

      • Lastly, the MDM implementation should leverage the data governance ability to communicate, educate and promote the critical data projects that impact everyone in the organization that uses data. And, today who does not use data!

      There should not be a question of doing an MDM project or doing data governance. It is not a question of which but a question of when do we start the MDM project so we can further the adoption of data governance. It is not two efforts but just one that has significant benefits and value to the organization. Yes, MDM is more than data governance and data governance is more than MDM. But the MDM program should be considered as an implementation of both practices. And, as always, stay calm and allow your data governance program to prosper.

      Source: The Data Administration Newsletter

      • June 4, 2019
        Stephen Jensen

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        • June 4, 2019
          Robert Sanchez

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