Unifying master data management with existing data practices

By Philip Russom, Research Director for Data Management, TDWI

The TDWI Checklist Report, Seven Tips for Unified Master Data Management Integrated with Data Quality and Data Governance, examines the interaction of master data management (MDM) with other data management practices and provides best practices to help organizations implement successful unified data management (UDM) projects.

Master data management can be practiced many different ways, with various user conventions and a broad array of vendor-built technologies. The report focuses on a specific practice called unified MDM. In unified MDM:

article-russom
Philip Russom is the Research Director for Data Management at TDWI (The Data Warehousing Institute). He has been an industry analyst at Forrester Research, Giga Information Group, and Hurwitz Group, specializing in BI and data management issues.
  • MDM is part of a unified program for many data management disciplines. Unified data management (UDM) is a best practice for coordinating diverse data management disciplines. UDM enables MDM to leverage competency synergies with related disciplines, such as data quality, data integration and data governance.
  • MDM is one of many solutions built atop a unified vendor framework supporting many data management functions. By using a vendor’s unified toolset, developers can share development artifacts (for productivity and consistent standards), plus design solutions that incorporate diverse DM functions. The initial investment in a vendor’s unified platform reduces system integration and other costs over time because multiple MDM solutions are built on top of it. A unified platform also accelerates time-to-use for DM projects.
  • MDM is part of a series of easily managed projects. This phased approach avoids risky big-bang projects, and it enables an organization to incrementally grow into multiple MDM solutions that in aggregate amount to enterprise coverage for MDM.
  • MDM is controlled and guided by data governance and data stewardship. Master and reference data are like all data in that they are subject to the enterprise regulations of governance as well as detailed improvement via data stewardship. A modern, unified platform will provide software functions that automate governance and stewardship tasks.
  • MDM is continuously improved by multiple data quality functions. Master and reference data benefit strongly from quality measures for standardization, address verification, data enrichment, profiling, monitoring of quality metrics, and so on.
  • MDM is for business people who act as hands-on stewards, not just technical personnel. A growing number of stewards want and need tool functions designed for them, such as profiling, search, collaboration and remediation.
  • MDM is organized and optimized via a hub. Many high-value features of MDM are more broadly disseminated when enabled through a hub, namely collaboration among multiple stakeholders, one-stop governance and stewardship, entity resolution, and publish/subscribe methods.

    This TDWI Checklist Report examines these characteristics typical of business programs and technical solutions for unified MDM. The report begins with basic definitions for some of the data disciplines discussed in this report, such as master data management, data quality, data governance and data stewardship. It then provides seven tips for practicing unified data management.

    For example, the report begins by explaining that there are many good reasons for coordinating MDM, data quality, data governance and related disciplines. Such coordination is evolving into a common best practice among business and technology users. It goes by many names – among them enterprise data management and enterprise information management (EIM) – but TDWI prefers to call it unified data management. MDM has a prominent place in UDM – especially when coordinated with data quality and data governance – and MDM definitely benefits from the coordination.

    In fact, lack of coordination can lead to redundancies. The report points out two key requirements that UDM should satisfy and balance in order to be considered successful. Furthermore, we explore the importance of taking a phased approach to MDM projects and the role of data governance and data stewardship, the part data quality plays, and the necessity of user-friendly tools.

    For more details about these and other key points, download the full Checklist Report free at Seven Tips for Unified Master Data Management Integrated with Data Quality and Data Governance.

     

    business people collaborating about MDM

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