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TDWI Blog: Data 360

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MDM Lessons Learned

Master data management (MDM) enables organizations to maintain a single, clean, consistent set of reference data about common business entities (e.g. customers, products, accounts, employees, partners, etc.) that can be used by any individual or application that requires it. In many respects, MDM applies the same principles and techniques that apply to data warehousing—clean, accurate, authoritative data.

Not surprisingly, many data warehousing (DW) professionals have taken the lead in helping their organizations implement MDM solutions. Yet, even grizzled DW veterans pose fundamental questions about how to get started and succeed in this new arena. Here are answers to the seven most common questions:

1. What’s the best place to start with MDM? People want to know whether it’s best to start with customer, product, or account data, or whether the finance, service, or marketing department is most receptive to MDM. The actual starting place is determined by your organization and the amount of pain that different groups or departments feel due to lack of conformed master data. The only surefire advice is to start small and work incrementally to deliver an enterprise solution.

2. How do you fund MDM? Few people have succeeded in funding stand-alone MDM projects, especially if their company has recently funded data warehousing, data quality, and CRM initiatives. Executives invariably ask, “Weren’t those initiatives supposed to address this?” Replying that MDM makes those initiatives more efficient and effective just doesn’t cut it. The best strategy is to bake MDM projects into the infrastructure requirements for new strategic initiatives.

3. How do you architect an MDM solution? The right architecture depends on your existing infrastructure, what you’re trying to accomplish, and the scope and type of reference data you need to manage. A classic MDM hub is essentially a data reconciliation engine that can feed harmonized master data to a range of systems—including the data warehouse. MDM hubs come in all shapes and sizes: on one extreme, a hub serves as the only source of master data for all applications; on the other, it simply maintains keys to equivalent records in every application. Most MDM solutions fall somewhere in the middle.

4. What’s the role of the data warehouse in MDM? There is no reason you can’t designate a single application to serve as the master copy. For example, you could designate the data warehouse as the master for customer data or an Oracle Financials application as the master for the chart of accounts. These approaches are attractive because they reuse existing models, data, and infrastructure, but may not be suitable in all situations. For instance, you may want an MDM solution that supports dynamic bidirectional updates of master data in both the hub and operational applications. This requires a dynamic matching engine, a real-time data warehouse, and Web services interfaces to integrate both ends of the transaction.

5. What organizational pitfalls will I encounter? Managing the expectations of business and IT stakeholders is nothing less than a make-or-break proposition. “Change management can derail an MDM project,” says one chief technology officer at a major software manufacturer that implemented a global MDM project. “When you change the data that end users have become accustomed to receiving, it can cause significant angst. You have to anticipate this, implement a transition plan, and prepare the users.” In addition, don’t underestimate the need to educate IT professionals about the need for MDM and the new tools and techniques required to implement it.

6. What technical pitfalls will I encounter? First of all, MDM requires a panoply of tools and technologies, some of which may already exist in your organization. These include database management systems, data integration tools, data matching and quality tools, rules-based systems, reporting tools, scheduling, and workflow management. Buying a packaged solution alleviates the need to integrate these tools. But if you already have the tools that exist in a package, negotiate a steep discount. Early MDM adopters say the biggest challenges are underestimating the time and talent required to define and document MDM requirements, analyze source data, maintain high-performance Web services interfaces, and fine tune matching algorithms to avoid under- or over-matching.

7. How do I manage a successful MDM implementation? To succeed, MDM requires business managers to take responsibility for defining master data and maintaining its integrity. This involves assigning business executives to stewardship roles in which they drive consensus about data definitions and rules and oversee processes for changing, managing, auditing, and certifying master data. Good data governance may or may not involve steering committees and meetings, but it always involves establishing clear policies and processes and holds business people accountable for the results.

MDM is a major undertaking and there is much to learn to be successful. But hopefully the answers to these seven questions will get you moving in the right direction.

Posted by Wayne Eckerson on November 6, 2009


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