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What level of data accuracy standards do we need for master data?

Find out how to establish data accuracy standards and discover what level of accuracy is necessary for your organization, depending on your business and data requirements.

What level of validation and/or verification of consistency, correctness and completeness is sufficient for master data?
That's a savvy question, my friend. By asking it you're revealing that you already understand the following seldom-understood truth: That there are different levels of "acceptability" for data. (Want a job?)

We once had a financial services client that was committed to SixSigma. The client believed in the goal of "zero defects" for all of its corporate data. An admirable goal, yes, but also a very expensive one. In order to consistently hit a 99>9 percent data accuracy rate the client was forced to "over-process" the data. In addition to the requisite data quality automation, that processing involved extensive human time from business subject matter experts, data stewards, and data governance sponsors. And at the end of the day the business itself was using mere summary information most of the time, rendering the processing of granular records so much overkill.

In this case, perfect was the enemy of good. Yes, sometimes "good enough" is good enough. (Peter Drucker is spinning as I write this, but just the same.) The key is to understand your business requirements and then drill them down to data requirements. That will tell you conclusively what good enough really is.

This was last published in March 2009

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