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It is easy to agree that getting your data 'right' is a necessary pre-requisite for Master Data Management (MDM), but:

1. few companies actually make realistic plans for that aspect of their MDM initiative and
2. even fewer stop to think about "if I already had data that was fit for purpose across my business, what would my remaining MDM requirements really be?"

This paper discusses the advantages of data mastering and, in particular, its relevance to MDM. Indeed, we suggest that it should be considered a pre-requisite and precursor to implementing MDM: in some cases even as a lower-budget substitute to a full MDM program, where much of the value can be delivered for a fraction of the cost (the 80/20 rule is a reasonable estimate). An additional factor is that traditional data quality products were designed to cater for name and address cleansing and matching and do not necessarily have architectures that lend themselves to cleansing product data, so if your MDM objective relates to product data (often referred to as PIM—Product Information Management), a different approach may be required, which we will explore later.

However, before going on to discuss these points we will first need to clarify what we mean by data mastering and explain how it differs from traditional approaches to MDM and data quality.

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