Data Quality Is Fitness for Use
Assess data quality relative to the decision risk, not as an abstract pass or fail.
Do not ask whether the data is perfect. Ask what decision it can safely support. A data quality framework gives you a vocabulary for defects. Completeness covers missing fields. Uniqueness covers duplicates. Validity covers whether values fit the expected format or allowed set. Accuracy covers whether values match the real-world fact. Consistency covers whether systems define the same thing the same way. Timeliness covers whether the data is recent enough. Those dimensions are useful because each defect changes a decision differently. Missing industry codes may not matter for a company-level revenue read, but they matter for segment targeting. Duplicate opportunities…
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