Master Data Management: Companies Struggle to Find the Truth in Massive Data Flows
Enterprises are struggling to get to "one version of the truth" with their master data. A new Forrester Research report explains the overwhelming adoption barriers and why companies are failing.
Wed, May 28, 2008
CIO — In the pursuit to achieve "one version of the truth" from their growing volumes of corporate and customer data, enterprises are struggling to implement master data management (MDM) initiatives today.
MDM is one way to achieve data truth, but it's not easy. At a high level, MDM is a set of processes and technologies that help enterprises better manage their data flow, its integrity and synchronization. At the core is a governance mechanism by which data policies and definitions can be enforced on an enterprise scale. (For an inside look at an MDM success story, see How Master Data Management Unified Financial Reporting at Nationwide Insurance.)
The reasons for organization's difficulties are many, including people, process, governance and cost complexities, according to a May 2008 Forrester Research report, "Trends 2008: Master Data Management," by Ray Wang and Rob Karel. The analysts based their findings on nearly 150 MDM inquiries and interviews with end users and vendors.
From the interviews with Forrester clients, the analysts claim that executive-speak about the importance of their organizations' data and how it must be nurtured, analyzed and protected is at an all-time high.
"Unfortunately," Wang and Karel write, "MDM requires much more than rhetoric to survive its adoption barriers."
Here are the five most common problems and mistakes cited by MDM early adopters and those who have had successful projects.
1. Approaching MDM as purely a technology initiative.
While IT departments and staffers will drive and sponsor many MDM initiatives, it is the business stakeholders who should ultimately define the value of the MDM efforts that can improve their business processes. They must give more than just minimal participation and sponsorship, Wang and Karel write. (See Master Data Management: Truth Behind the Hype for a look at how Wachovia handled this critical topic.)
For example, data architects often benefit from a cross-enterprise perspective, "allowing them to recognize the business impacts of a data-quality problem often not even visible to the business stakeholders themselves," the analysts write. "Hence it's natural for IT to evangelize early MDM efforts." Risks increase, however, when IT takes ownership of not just the enabling technology solution but the business data definitions and rules that, in fact, must come from their business customers.
2. Assuming dirty data is just an IT problem.
Poor data quality is, obviously, a critical business barrier. "No longer relegated to the IT teams as a technical exercise, business units require accurate and up-to-date information to make key decisions," Wang and Karel write. "Without accurate information on product inventories, customer locations and relationships, enterprises lack the ability to act on key initiatives such as serving customers efficiently, managing compliance and risk, and optimizing install base value."