By Todd Schraml
Quality can be a hard thing to define. What is good and what is bad may not be easily identified and quantified. When a data mart accurately reflects data exactly as found in the source, should that be considered a quality result? If the source data is bad, is the data mart of high quality or not? If the data mart differs from the source, when is the difference an improvement of quality and when is said difference evidence of diminished quality? While it may seem self-evident that correcting the source of load data would be the "right" thing to do, in practice that direction is not necessarily self-evident. The reasons supporting this nonintuitive approach are varied. Sometimes changes to the source impact other processes that must not change, or the changes will expose problems that may provoke undesired political fallout, or it may simply be that making the proper adjustments to the source application would prove too costly to the organization. For all these reasons and more, in the world of business intelligence, the dependent data often is expected to be of higher quality than the source data. In order for that improvement to occur, data placed within the dependent mart or data warehouse must be altered from the source. Sometimes these alterations become codified within the process migrating data from the source. Other times changes are made via one-time ad hoc updates. Either way, this alteration leads to a situation in which the dependent data will no longer equate one-for-one to the source data. Superficial comparisons of this altered content will highlight the disparity that what exists for analytics is not the same as what exists for the operational system ... full article.