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Home > Data management / BI News > Seven master data management best practices |
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But master data management best practices are still emerging and it's not easy to get organizations to talk about their master data management experiences. Kalido Inc., a Burlington, Mass.-based MDM technology vendor, admits that it has a hard time getting customers to talk to the press. All this secrecy around successful master data management programs doesn't help companies looking for best practices, which is partly why Kalido sponsored a customer audit and master data management best practices study by San Mateo, Calif.-based analyst firm Ventana Research. Its researchers examined the best practices of five anonymous Kalido customers to reach their conclusions. The Ventana study, an experienced consultant, and a European telecom maker finally shed some light on the best (and worst) practices for master data management success. 1. Get business involved -- or in charge. "MDM has to be driven by business needs, otherwise it may turn out to be just another database that must be synchronized with all the other ones," said David Loshin, president of Knowledge Integrity Inc., a Silver Spring, Md.-based consultancy that provides an MDM strategy development service and has worked on enterprise-scale initiatives. Similarly, the Ventana study found that businesspeople, rather than IT, should drive the process. Support ranging from C-level executives to senior managers to business end users was critical for success, Ventana found. It's often hard to motivate an organization to get behind the dry prospect of master data management, but early enterprise-wide support is important in the long run, users said. If key corporate goals are tied to the project through a solid business case, it should be a straightforward task to demonstrate benefits and generate excitement. 2. Allow ample time for evaluation and planning. Plan at least three months for evaluation, talk to reference customers, and do a proof-of-value project with samples of real company data, Kalido users told Ventana researchers. Don't underestimate the time and expertise needed to develop foundational data models, users said.
"It's more complex than people realize -- and that requires starting early and using real data for planning," said David Waddington, a Ventana vice president and research director who worked on the study. IT's cooperation was an area of concern, as some companies have experienced delays in projects waiting for permission and access rights, Ventana found. 3. Have a big vision, but take small steps. Consider the ultimate goal, but limit the scope of the initial deployment, users told Ventana. Once master data management is working in one place, extend it step by step, they advised. Business processes, rather than technology, are often the mitigating factor, they said, so it's important to get end-user input early in the process.
"If you're just interested in getting consistent customer data, it's very important to do that against the bigger background of 'how am I going to manage all of my master data longer term?'" Waddington explained. "Then you don't end up in the situation [of] having to link together a whole lot of different solutions." 4. Consider potential performance problems. Performance is the 800-pound gorilla quietly lurking in the master data management discussion, Loshin cautioned. Different architectures can mean different performance penalties. For example, if a company uses the master hub style of master data management, record creation flows through a single point, which can become a bottleneck. Also, with many applications relying on master data management, the workflow, system priorities and order of operations become critical issues to consider up front. How companies solve this potential performance problem varies, Loshin said, because it's inherently related to their unique architectures. 5. Institute data governance policies and processes. Allow time and money for people and process change management, and don't underestimate the size of the job, experts agreed. Swedish telecom equipment maker Ericsson learned that the politics of data governance can be quite difficult, according to Roderick Hall, senior project manager. Long before deploying SAP master data management, the Stockholm-based company instituted a master data group to manage critical data assets. It's a "shared services" group that provides services to both IT and business. The group started as part of the finance department, but the function changed with the realization that master data management was a company-wide concern, Hall said. Their job isn't always easy.
Although some departments, such as finance, saw the value of centralizing master data management, Hall said, other groups were reluctant to give up data ownership. "To get acceptance of the fact that people have got to give up the freedom to correct their own master data to some faceless group in Stockholm [where the master data group is located] has been a pretty hard battle," Hall said. 6. Carefully plan deployment. Master data management is still relatively new, so training of business and technical people is more important than ever, Ventana found. Using untrained or semi-trained systems integrators and outsourcing attempts caused major problems and project delays for master data management users, Waddington said. 7. Consider the transition plan. Then, there's the prospect of rolling out a program that has an impact on many critical processes and systems -- no trivial concern. Loshin recommended that companies should plan an master data management transition strategy that allows for static and dynamic data synchronization. "Trying to adjust the underlying infrastructure without affecting day-to-day operations can be as challenging as fixing potholes in the highway without disrupting traffic," Loshin said. This article originally appeared on SearchDataManagement.com.
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