Master Data Management: Companies Struggle to Find the Truth in Massive Data Flows

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.

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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.”

3. Managing the vast complexity of multiple data domains without proper techniques.

Cross-enterprise MDM—which Forrester defines as “transactional, bi-directional synchronization of multiple data domains across your information supply chain including all points of data capture, update and usage”—is extremely complicated, concede the analysts. Many MDM software packages are able to reduce this complexity by providing common data models, integration APIs, and Web-service-enabled features that help coordinate the “information supply chain,” as the analysts call it. (Also see Demystifying Master Data Management.)

“The technology, unfortunately, is the easy part,” according to Wang and Karel. “In fact, the data governance, prioritization, people and process aspects of implementing an MDM solution will likely derail the project before the technology fails.” In other words, change-management problems are more prevalent and dangerous than the IT hurdles.

4. Prioritizing funding and managing costs.

Not only is an MDM project complicated, but it is expensive. The software license costs for an average size implementation of an enterprise MDM solution range anywhere from $500,000 to $2 million, states the report. (If you’re just starting out, see 10 Mistakes to Avoid When Writing an RFP for Master Data Management.)

In addition, enterprises often find it costs $2 in professional and consulting services for every $1 in software licenses just to implement MDM technology, write Wang and Karel.

“When you consider the eventual costs of synchronizing the MDM solution throughout your entire data management infrastructure, some large organizations can approach a 5-to-1 professional service to software cost ratio,” the analysts write, “leading to a potential overall investment exceeding $10 million.”

5. Underestimating the level of executive sponsorship required for success.

For sure, it’s energizing for IT, business managers and users to hear senior executives proclaim that data is their company’s most critical asset and that an MDM transformation is key to the organization’s future, write the analysts.

“However, discouragement soon follows when these same executives fail to provide the necessary resources, funding and prioritization to mitigate the risks associated with bad data,” write Wang and Karel. “These scenarios often reflect an organization’s inability to incorporate a business case for MDM into overall corporate strategy.”

There’s No Shortage of ‘MDM Fever’

Enthusiasm for MDM programs is at an all-time high, even with the potential risks, costs, organizational change and stumbling blocks, according to Forrester data.

A late 2007 survey of 1,017 North American and European executives found that 44 percent of enterprises consider the deployment of a master data management initiative as either a priority or a critical priority in 2008. (Forrester predicts that the MDM market will reach $6.7 billion by 2010, with year-over-year growth averaging between nearly 60 percent.)

What’s interesting to note is that these numbers equaled the importance of other strategic IT initiatives such as the adoption of service-oriented architectures, and were ahead of the priorities such as the adoption of software-as-a-service and Web 2.0 technologies.

“The important takeaway from these potentially competing thoughts—increasing MDM priority and daunting inhibitors to MDM—is that there are many ways to skin this MDM cat; customer adoption trends reveal multiple approaches to evolving an MDM strategy,” write Wang and Karel in the report. “Trends among successful implementations point to building a case for change, investing in people and process, and prioritizing for data quality.”