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TDWI Blog: Data 360

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Master Data Management: Rules for the Next Generation

Blog by Philip Russom
Research Director for Data Management, TDWI

I’m currently researching a TDWI Best Practices Report that will redefine master data management (MDM) by describing what its next generation should look like. As part of the research, I’ve been interviewing users on the phone about their MDM programs.

The news so far is a mix of good and bad. I hate saying it, but half of the organizations I’ve talked with are mired in early lifecycle stages of their MDM programs, unable to get over certain humps and mature into the next generation. On the flip side, the other half is well into the next generation; so I know it can be done.

Allow me to list desirable capabilities of MDM’s next generation, and briefly say why these need to replace similar early phase capabilities. The following list (with a great deal more detail) will probably appear in my Next Generation MDM report that TDWI will publish April 2, 2012. After all, the list defines MDM’s next generation. And my goal is to establish a set of rules (or requirements) that can guide users into the next generation.

Multi-domain MDM. Many MDM solutions address only the customer data domain, and they need to move on to other domains, like products, financials, and locations. Single-data-domain MDM is a barrier to having common, consensus-based entity definitions and standard reference data that would allow you to correlate information across multiple domains. (See my blog The State of Multi-Data-Domain MDM.)

Multi-department, multi-application MDM. MDM for a single application (typically ERP, CRM or BI) is a safe and effective start. But the point of MDM is to share common definitions across multiple, diverse applications and the departments that depend on them. It’s important to overcome organizational boundaries if MDM is move from being a local fix to an enterprise infrastructure.

Bidirectional MDM. "Roach Motel MDM," as I call it, is when you extract reference data and study in a database from which it never emerges (as with many BI/DW systems). One-way MDM is bad whenever you need to improve reference data in a central place, then publish it out to a wide variety of operational applications. (See my article Roach Motel MDM.)

Real-time MDM. The strongest trend in data management today (and BI/DW, too) is toward real-time operation as a complement to batch. Real-time is critical to identity resolution and the immediate application of recent changes to reference data.

Consolidating multiple, competing MDM solutions. How can you have a single view of the customer, if you have multiple customer-domain MDM solutions? How can you correlate reference data across domains, if the domains are treated in separate MDM solutions? For many organizations, next-gen MDM begins with a consolidation of multiple MDM solutions.

Beyond enterprise data. Despite the obsession with customer data that most MDM solutions suffer, almost none of them today incorporate data about customers from Web sites or social media. If you’re truly serious about MDM as an enabler for CRM, next-gen MDM (and CRM, too) must reach into every customer channel.

Richer modeling. Reference data in the customer domain works fine with flat modeling, involving a simple (but very wide) record per customer. However, other domains make little sense without a richer, hierarchical model, as with a chart of accounts in finance of a bill of material in manufacturing. Metrics and KPIs – so common in BI, today – rarely have proper master data in multidimensional models. (See my article MDM for Performance Management.)

Coordination with other disciplines. To achieve next-gen goals, many organizations need to stop practicing MDM in a vacuum. Instead of MDM as merely a technical fix, it also needs to be aligned with business goals for data. And MDM should be coordinated with related data management disciplines, especially data integration and data quality. A solid data governance program can be an effective medium for such coordination. (See my blog MDM Can Learn from Data Quality.)

MDM Workflow. Development and collaborative efforts in MDM today are mostly ad hoc actions with little or no process. For MDM program to scale up and grow, it needs workflow functionality that automates the proposal, review, and approval process for newly created or improved reference and master data. Also, a few MDM programs need the kind of workflow enabled by tools for business process management. Vendor tools and dedicated applications for MDM are starting to support such workflows.

So, what do you think? Do you know of other generational changes that MDM is facing? Let me know.


Please take the TDWI MDM Survey for my upcoming report about Next-Generation MDM.

David Loshin and I will moderate the TDWI Solution Summit on Master Data, Quality, and Governance, coming up March 4-6, 2012 in Savannah, Georgia. You should attend!

Posted by Philip Russom, Ph.D. on November 17, 2011


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