The 6 Dimensions of Data Quality — Completeness
Missing data can look perfectly normal. How completeness failures distort analysis and the decisions that follow.
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Data Quality Is Six Different Ways to Make a Bad Decision
Six dimensions of data quality in business terms: completeness, validity, uniqueness, consistency, timeliness, and accuracy.
The 6 Dimensions of Data Quality — Validity: A Valid Email Address Can Still Be Terrible Data.
Data validity goes beyond accepted formats. Define business rules, controlled values, and responses to violations before trusting a field for analysis.
Read the essayThe 6 Dimensions of Data Quality — Uniqueness: One Customer, Four Rows, Zero Trust.
Duplicate events can manufacture growth. Define the counting unit, audit candidate keys, and investigate the mechanism before removing repeated rows.
Read the essayThe 6 Dimensions of Data Quality — Consistency: When Two Dashboards Disagree, Neither Team Is Crazy.
Conflicting dashboards often hide different metric definitions. Align grain, filters, timezones, and ownership with a practical metric contract.
Read the essayThe 6 Dimensions of Data Quality — Timeliness: Yesterday's Data Is Not Yesterday's Truth.
Fresh data can still be incomplete. Define latency, reporting windows, and freshness expectations before treating provisional metrics as final.
Read the essayThe 6 Dimensions of Data Quality — Accuracy: The Data Is Clean. It's Also Wrong.
Data accuracy needs a real-world reference. Reconcile metrics with payments, receipts, and domain knowledge before trusting a tidy dashboard.
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