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Organizational Best Practices for MDM

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Excerpted from "Master Data Management: Consensus-Driven Data Definitions for Cross-Application Consistency," a report published in October 2006 by The Data Warehousing Institute (TDWI). To download the complete report, visit www.tdwi.org/research. 

Master Data Ownership and Control

In a survey run in 2006, TDWI asked master data management (MDM) implementers: "What organizational structure primarily owns or controls your MDM solution?" (See Figure 1.) Most respondents identified technology organizations, such as the data warehousing team (22 percent), enterprise data architecture group (20 percent) and IT department (16 percent). They ranked far lower organizations with direct business involvement, such as a line of business (12 percent), data governance committee (8 percent) and data stewardship program (7 percent).

What's wrong with this picture? First, most organizations need businesspeople to be involved in the creation of business entity definitions, if the definitions are to be valid and useful. Second, for master data to achieve its goal - consensus-driven definitions applied consistently - it must be shared ruthlessly, which in turn demands a central organizational structure with an executive mandate, such as a data governance committee or data stewardship program. These much-needed corrections to how master data is managed have deep ramifications for organizational structures and staffing.

Figure 1

Organizational Structures for MDM

MDM and other cross-functional data management practices. Master data management is similar to data warehousing and data stewardship, in that all three benefit from a cross-functional team. Organizations already practicing data warehousing or stewardship should borrow ideas from these teams, in terms of what works specifically within their organization. In fact, due to similarities across these three practices (plus information deliverables they share in common) some organizations consolidate them into a single cross-functional team. Even if teams are kept separate, they must collaborate closely because of the data and business goals they share. Either way, these and other increasingly collaborative data management practices are ripe for consolidation under the control of a data governance board or similar organizational structure.

D ata governance. A data governance committee or board is cross-functional, in that it's populated with a mix of technical data experts and business people whose management effectiveness depends on complete, clean and consistent data. It's also cross-functional in the sense that its technical people represent multiple data management practices, including data warehousing, data quality, master data management, metadata management, database administration, enterprise data architecture and so on. For all these people, governing data is a part-time responsibility that complements their "day jobs."

The data governance committee provides common ground where data stakeholders can collaborate about how to share and improve data. And it establishes change management processes for proposing, reviewing, and implementing changes to data, systems that manage it and business processes that handle it. In short, data governance unites IT and the business through people and processes to effect data improvements (see Figure 2).

Figure 2

Enterprise MDM needs data governance. One of the goals of data governance is to give data management practices broader reach and visibility, perhaps even enterprise scope. In that context, data governance isn't necessarily appropriate to application-specific forms of MDM, like operational MDM or analytic MDM. However, enterprise MDM is difficult to pull off without data governance's central organization representing all data stakeholders.

Organizational structures involving MDM. Data governance is not the only organizational structure that can help pull master data efforts together. Many data stewardship and enterprise data architecture programs have an enterprise breadth and executive mandate similar to data governance. And some teams that do MDM as part of a larger application are cross-functional in nature, like teams focused on data warehousing or ERP. Competency centers (also called centers of excellence) that focus on data integration and quality can also give scope to MDM efforts.

Staffing a Master Data Management Team Cross-Functional Staffing

There are two prominent requirements to consider before staffing MDM:

  • All master data management efforts should be cross-functional to some degree. That is because deciding what data means, how it should be defined and how users should use it is best done as a consensus-driven collaboration between technical and business staff. This is a critical success factor, and survey respondents ranked a "lack of cross-functional cooperation" as the leading inhibitor to MDM technical implementations.
  • The scope of cross-functional interaction varies according to the type of MDM practiced. For example, operational MDM in an ERP context requires a relatively narrow cross-functional team consisting of technical personnel who are expert in the ERP software and its data, along with line-of-business managers who understand the processes in which the ERP is used. In other words, application-specific MDM serves one system and its end-users within one business unit, and so needs a small team with loose policies and procedures for collaboration. At the other end of the spectrum, however, enterprise MDM serves multiple systems and the end-users of multiple business units, so the cross-functional team requires more people to represent more organizational units of IT and the business, plus well-developed policies and procedures that foster collaboration across multiple organizations.

Figure 3

Figure 4

Technical Staffing

The cross-functional nature of master data management is visible in the makeup of some of the technical teams that implement it:

  • Mixture of technical and non-technical team members. According to TDWI's survey, MDM is most often implemented by a data warehousing team (and most of these are cross-functional), followed by a "cross-functional team from business and IT" (49 percent and 45 percent, respectively, as shown in Figure 3). So, respondents clearly recognize that MDM requires a consensus-driven collaboration between IT and business.
  • Hybrid team members. Some implementers tend to be hybrids with both technical and business skills, namely business analysts (30 percent) and data stewards (24 percent).

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