Data Validation: Data Quality's Doorman

Assumptions and Issues Abound

Often data quality seems to be an assumption. The business assumes that IT has controls in place, IT assumes that data supplied is in order and everyone assumes they'd know about it if these assumptions weren't correct.

Fact is, most often, these assumptions are false and there are data quality issues. The costs, both tangible and intangible, associated to data quality issues are extremely high so you'd think people would assume less and attack data quality root causes more often. But for some reason data quality remains something people assume is either inherent or addressed.

Maybe this lack of diligence is because data quality root causes are so varied in nature and often tough to trace. I know of many instances where the source produced issues and there was nothing the data consumer could do to drive down, much less stop, the constant production of erroneous data.

If this situation sounds familiar and you are looking for relief, there is an important step that can give you back control of your data, stop the proliferation of issues, and define root causes for these issues.

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