TDWI | Training & Research | Business Intelligence, Analytics, Big Data, Data Warehousing

How Data Governance Fosters the Evolution of Data Quality and MDM Initiatives

Webinar Abstract

There are good reasons why data governance (DG) has become a critical success factor for a number of life-cycle stages within data quality (DQ) and master data management (MDM) initiatives.

Both DQ and MDM force changes in IT systems and their use; a mature DG program includes processes that facilitate change. DQ and MDM sustainability is more likely when they demonstrate a return for the business; a DG board’s cross-functional mix of people can reveal how DQ and MDM can demonstrate a return by aligning to business goals. DQ and MDM teams are under pressure to coordinate with a number of other data disciplines; a DG program that focuses on data standards (not just compliance) is an excellent medium for coordinating with teams for DQ, MDM, data integration, business intelligence, data warehousing, and so on. As DQ and MDM initiatives grow, they reach further across an enterprise; DG boards enjoy a strong executive mandate, which results in a meaningful influence on the entire enterprise, such that DQ and MDM standards are taken seriously and given broad adoption.

In this Webinar, you’ll learn:

• What data governance is and how certain variations of it are very advantageous to data disciplines, such as data quality and master data management
• Why DQ and MDM initiatives should let DG supply people-oriented process and collaboration, so they have more time and bandwidth for technical implementations
• Which DQ and MDM life-cycle stages, design tasks, architectural choices, and data standards should be governed and fostered by a DG program

Philip Russom, Ph.D.

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