There is a tendency in IT to use generic approaches when managing the different aspects of the infrastructure we have to deal with. This is at odds with the increasing specialization we see all around us. Whether we are dealing with data, software, hardware, projects or anything else, the probability is that we will be working with something much narrower in scope than was usual just a few years ago. One-size-fits-all approaches, or very general advice that amounts to little more than "do the right thing" is unlikely to yield the desired results in such situations. Data is now being recognized as an area that is not a homogenous domain, but rather as consisting of a set of distinct categories. Different categories of data have their own unique characteristics. If these characteristics are not properly understood, practitioners will only be able to use generic approaches that are unlikely to deliver the desired results and may even be doomed to failure from the start.

Master data and reference data are two major data categories that are often thought of as the same. The reality is that they are quite different, even though they have strong dependencies on each other. Failure to recognize these differences is risky, particularly given the current explosion of interest in master data. Projects that approach master data as just "data" and fail to address its unique needs are likely to encounter problems. This is particularly true if such projects experience scope creep that leads them into the very different realm of reference data management.

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