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TDWI Blog: Data 360

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The Top Ten Priorities for Next Generation MDM

Blog by Philip Russom
Research Director for Data Management, TDWI

[NOTE -- I recently completed a TDWI Best Practices Report titled Next Generation Master Data Management. The goal is to help user organizations understand MDM lifecycle stages so they can better plan and manage them. TDWI will publish the 40-page report in a PDF file on April 2, 2012, and anyone will be able to download it from In the meantime, I’ll provide some “sneak peeks” by blogging excerpts from the report. Here’s the fourth excerpt, which is the ending of the report.]

The Top Ten Priorities for Next Generation MDM
The news in this report is a mix of good and bad. Half of the organizations interviewed and surveyed are mired in the 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, which proves it can be done.

To help more organizations safely navigate into next generation master data management, let’s list its top ten priorities, with a few comments why these need to replace similar early phase capabilities. Think of these priorities as recommendations, requirements, or rules that can guide user organizations into the next generation.

1. Multi-data-domain MDM. Many organizations apply MDM to the customer data domain alone, and they need to move on to other domains, like products, financials, and locations. Single-data-domain MDM is a barrier to correlating information across multiple domains.

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

3. Bidirectional MDM. Roach Motel MDM is when you extract reference data and aggregate it in a master database from which it never emerges (as with many BI and CRM systems). Unidirectional MDM is fine for profiling reference data. But bidirectional MDM is required to improve or author reference data in a central place, then publish it out to various applications.

4. 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 verification, identity resolution, and the immediate distribution of new or updated reference data.

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

6. Coordination with other disciplines. To achieve next-generation goals, many organizations need to stop practicing MDM in a vacuum. Instead of MDM as merely a technical fix, it should also align with business goals for data. And MDM should be coordinated with related data management disciplines, especially DI and DQ. A program for data governance or stewardship can provide an effective collaborative process for such coordination.

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

8. 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-generation MDM (and CRM, too) must reach into every customer channel. In a related area, users need to start planning their strategy for MDM with big data and advanced analytics.

9. Workflow and process management. Too often, development and collaborative efforts in MDM are mostly ad hoc actions with little or no process. For an 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 data. Vendor tools and dedicated applications for MDM now support workflows within the scope of their tools. For a broader scope, some users integrate MDM with business process management tools.

10. MDM solutions built atop vendor tools and platforms. Admittedly, many user organizations find that home-grown and hand-coded MDM solutions provide adequate business value and technical robustness. However, these are usually simple, siloed departmental solutions. User organizations should look into vendor tools and platforms for MDM and other data management disciplines when they need broader data sharing and more advanced functionality, such as real-time operation, two-way sync, identity resolution, event processing, service orientation, and process workflows or other collaborative functions.


Although the above version of the top ten list is excerpted from the upcoming TDWI report on MDM, an earlier version of this list was developed in the TDWI blog “Rules for the Next Generation of MDM.“

Be sure to visit on April 2 or later, to download your own free copy of the complete TDWI report on Next Generation Master Data Management.

Please attend the TDWI Webinar where I present the findings of my TDWI report Next Generation MDM, on April 10, 2012 Noon ET. Register online for the Webinar.

Posted by Philip Russom, Ph.D. on March 16, 2012


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