Data Excellence¶
Create trustworthy, understandable, and scalable data products.
Principles¶
- Customer Value First — data exists to answer questions and drive decisions
- Simplicity Over Complexity — prefer understandable models over clever ones
- Ownership — every data product should have a named owner
- Build Quality In — data quality is validated, not assumed
Data Quality Dimensions¶
- Accuracy — does the data reflect reality?
- Completeness — are all expected records present?
- Consistency — is the data consistent across sources?
- Timeliness — is the data fresh enough to be useful?
- Validity — does the data conform to expected rules?
Data Testing¶
Every data pipeline should include: - Null checks on critical fields - Uniqueness checks on keys - Referential integrity checks - Freshness validation - Volume anomaly detection - Business rule validation
Data Modelling¶
Preferred characteristics: - Understandable by non-engineers - Reusable across use cases - Governed with clear ownership - Documented with business context, not just technical detail
Data Observability¶
Monitor: - Freshness — when was this data last updated? - Volume — are row counts within expected ranges? - Schema changes — have columns been added, removed, or changed? - Data drift — are distributions shifting unexpectedly?
Data Lineage¶
Track: - Sources — where does the data originate? - Transformations — how is it processed? - Consumers — who and what depends on it?