Start With Fitness for Use
Define data quality in terms of a specific business use, not a generic cleanliness standard.
The move: stop asking whether the dataset is clean; ask whether it is fit for the decision. Name the use first Data quality dimensions only become useful once the use is explicit. Accuracy asks whether the value represents reality. Completeness asks whether required values are present. Timeliness asks whether the data is fresh enough. Consistency asks whether the same fact agrees across places. Those are not equal for every use. Pick the critical fields Most business processes have a small number of fields that carry the decision. For a renewal forecast, paid status and contract end date matter more than…
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