"The bitterness of poor quality," runs an often-cited anonymous quotation, "remains long after the sweetness of meeting the schedule has been forgotten." Although quality discussions usually arise primarily in project or product manufacturing environments, it is actually poor quality data that can be particularly troublesome for an organization because its bitterness can affect the "taste" of everything else.

Understanding data quality issues is often a matter of finding a middle ground between those who define quality so broadly as to be unhelpful and those who define it too narrowly. Consider, for example, a data warehouse team whose quality concerns are sometimes limited to whether a data warehouse accurately reflects the contents of source systems. That's not going to satisfy most users (especially if the source systems themselves have quality issues). However, users often cast the quality net too broadly, expecting data to be there to support anything they want and to provide answers to questions they conceive of only dimly. Is there a middle ground here?

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