More organizations are pursuing MDM (master data management). Forrester Research says its client inquiries about MDM have increased 90 percent in the past two years, and by early December of 2010, Gartner had estimated that worldwide MDM software revenue would reach $1.5 billion by year end, a 14 percent increase from 2009.
That's a lot of cash and yet Gartner predicts 66 percent of organizations will find it difficult to demonstrate the business value of MDM.
Could it be that many organizations need MDM but haven't prepared for it?
In this slideshow, Loraine Lawson highlights the steps many MDM practitioners and analysts say you should take before embarking on a master data management initiative.
Click through for six steps to MDM success, as outlined by Loraine Lawson.
There's a degree of skepticism about MDM right now, according to Daniel Teachey, the senior director of marketing at DataFlux. In "Master Data Management Isn't for Everyone: How to Evaluate Readiness,” Teachey writes that a number of IT veterans rolled their eyes at the mention of MDM during a recent trade show. And, to be fair, not everyone needs MDM he says. He writes:
If your data management challenges are more finite, or if you have a smaller number of applications within the organization, MDM might be overkill for your situation. There are other ways to achieve a 'single view' outside of MDM. Creating a reference data 'lookup' or migrating other data to an existing CRM or ERP system can achieve many of the goals of MDM — without the cost.
Be forewarned, however. A panel of data and MDM experts agreed recently that they have never seen it work when companies use a source system as the master copy of their data.
The key words here are “integrity” and “confidence,” according to Forrester analyst Rob Karel, who says many organizations actually need to become more effective at data integration before they consider MDM. In a recent Tech Target article, Karel specifically identifies the ability to move data in scheduled batches and near real-time transactions, while maintaining the data's integrity, as a "critical enabler for a complex MDM architecture.”
David Loshin, president of Knowledge Integrity, Inc., says that includes documenting the business terminology used across your applications. He writes:
What emerges quickly during any data integration exercise, though, is that there is enough variance in 'implied semantics' to prevent a true consolidation. Recognizing that this variance will increase the time for an acceptable master data consolidation suggests that prior to the deployment of the MDM program, it is worthwhile to collect and document the fundamental business terminology that is commonly used in order to document the numerous understandings, research authoritative sources for actual definitions and harmonize those definitions to determine alignments (or perhaps misalignments), with actual use.
Master data management and data quality often go hand-in-hand. In fact, many undertake MDM because of data quality issues, so this can seem like a bit of a chicken-and-egg issue. But data quality has to come first, especially when it comes to planning the cost of your MDM initiative.
You would think organizations would intuitively know that since so many organizations tend to have a low opinion of their data quality, as shown by numerous surveys and studies. For instance, only 12 percent rated their data quality as good or better in a recent survey from The Information Difference.
Andy Hayler, president and CEO of The Information Difference, says many organizations fail at MDM because "they have failed to take data quality seriously as an integral part of their master data project, nor budget properly for it." In one survey, he points out, data quality consumed 30 percent of master data initiatives, but organizations had only funded it at less than 10 percent of the cost.
It is actually a bigger problem than you might at first think. The Information Difference survey found that while most companies see data quality as critical for MDM, a third confessed to having no data quality program. Most — 70 percent — don't even try to measure the cost of poor data quality.
MDM is about maintaining a correct version of your master data. While data quality corrects inaccuracies in the data, data governance allows you to maintain it by addressing the underlying issues that created the data quality problems in the first place.
Therefore, experts say data governance is a foundational component of any MDM initiative.
Data governance requires the business side to define the roles, responsibilities, policies and processes for how data is handled across an organization, Forrester Research analyst Rob Karel told Tech Target.
Karel has long and often evangelized about data governance as a foundational component of MDM success. In 2008, Loraine Lawson asked Karel what organizations needed to consider before they invested in MDM technology.
"Adoption of a data governance strategy to identify business ownership and stewardship roles and responsibilities should always be the first step,” he said. “MDM initiatives without effective governance are much more likely to deliver below expectations.”
Many may believe that IT/business alignment is so 2010. But in this case, experts offer some very specific recommendations for what that means and what you need to do.
For example, Forrester Research analyst Rob Karel firmly believes the key to alignment is to focus on the business processes, not just with MDM but with any data governance, data quality initiative. In a 2008 interview, Karel explained why that matters and why you should do it before you invest in an MDM solution:
The next step would be to identify which business processes are in the most critical need of master data, and to understand how and why the information in its current state is not meeting end user needs. MDM is a business capability, not just a technology space, and an organization’s MDM strategy must include improvements to the processes and systems that capture and update the raw data, not just the centralized solution that processes the data later. Understanding where changes may need to be made throughout the information supply chain is key to designing an MDM strategy.
How do you identify where the most critical need is? Try a consultant's trick: Listen for key phrases that indicate a need for MDM such as “we really don't know how many customers we have." That suggestion came from Jill Dyché, vice president of DataFlux, Baseline Division, at a recent seminar attended and written about by Rich Murnane for the Data Roundtable.
One common mistake is to see an executive sponsor as just someone who's going to fund your project, writes Rick Sherman, who runs the IT consulting firm Athena IT Solutions. While no one would underestimate the importance of that, Sherman says business sponsorship should also extend to ensuring you receive the daily support MDM and related programs, such as data governance, require from the business:
Business executives, from corporate and line-of-business groups, need to be involved in establishing the mindset that data is a corporate asset and needs to be managed just as carefully as any other valuable asset that the enterprise owns. Executives need to commit resources and time from people at all levels of the enterprise to define and manage data.
In part, this means your business sponsor should ensure the work is not rushed or handed to already overburdened employees, he writes. The executive sponsor should also ensure responsibility for these tasks is institutionalized by making it a measurable part of everyone's performance evaluations, according to Sherman.
When seeking an executive sponsor, you might also consider the advice Gartner analyst John Radcliffe gave about data governance: Find mid-level management business people who are respected and influential, rather than recruit an upper-level executive who might not have the time to devote to the MDM initiative.
