The last article in this series looked at six authors’ definitions of two related dimensions of data quality (Timeliness and Accessibility). In this article, we’ll look at one of the most foundational dimensions, Completeness.

At a high level, Completeness is intuitive. The key to measuring Completeness (or anything in this world, for that matter) is to identify the data’s characteristics and then compare those known attributes at a later time to test whether they have changed, in this case whether they have changed from NULL to NOT NULL or vice versa.

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