The Six Dimensions of Data Quality
Data quality is multi-dimensional. Addressing only one dimension while ignoring others leads to analytics failures.
- Completeness: Are all required fields populated?
- Accuracy: Does the data reflect real-world truth?
- Consistency: Does the same entity have the same values across systems?
- Timeliness: Is the data fresh enough for the use case?
- Uniqueness: Are there duplicate records?
- Validity: Do values conform to defined formats and business rules?
Automation Tools
Use Great Expectations, dbt tests, or Soda Core to define and run automated quality checks as part of your pipeline execution.