Data quality as a term can be difficult to pin down, though we all have a pretty good idea of what we mean when we use the phrase. Let's start with a simple premise: there is no absolute standard that constitutes quality data. Rather, the quality of data is simply a measure of its fitness for purpose and can vary widely, depending on the purpose at hand.

I have seen retail customers rigorously cleanse cash register logs of duplicate card swipes and transactions in order to derive good quality data for their data warehouse. Their analytics and reports required that these anomalies be purged or the inventory and sales data would not reconcile. However, when the analysts added fraud detection to their requirements, they found duplicate card swipes to be a very useful indicator; fraudsters might try several cards or try one card several times in an attempt to make a purchase. Good quality data for one purpose was useless for another.

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