Have you ever heard someone say that a statistic is valid, but inaccurate? Or perhaps they adamantly argue with the IT department that, although it isn’t in the list of valid values (and fails an error check), the value is accurate (factual). In this article, we’ll build on what we discussed in prior articles in this series regarding the dimensions of data quality and look more closely at Validity and Integrity. Below is a comparison of six data quality authors’ agreement with the Validity dimension.

As discussed in the first article, there is relative agreement on the Accuracy dimension, but there is some confusion around the Validity dimension, which is distinctly different. Although people often use the words valid or invalid when they are expressing whether data is factual or not, the words hold different implications when considered in data management/quality context.

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