What are data quality tests, really?
View details →
Data Quality Tests
When someone says 'we validated the data,' what does that mean? Data quality tests turn validation from opinion into repeatable, auditable checks that anyone can verify.
- Data quality tests = automated checks that run every time data flows through your pipeline
- Completeness, consistency, outliers—each dimension has specific rules
- 99.7% accuracy means 3 errors per 1,000 records; you decide if that's acceptable
- Tests should fail loudly so bad data never reaches decisions
- The goal: defensible claims. If you can't prove it, you can't claim it.
Real-world example
The supplier sends you 75 product sheets with environmental claims
You need to validate recycled content percentages, carbon footprints, and material types. Manually checking would take weeks. One error could mean greenwashing accusations.
- Completeness tests: Does every SKU have required fields? Missing = fail.
- Consistency tests: Do units match (kg vs lb)? Do percentages sum correctly?
- Outlier tests: Is recycled content >100%? Is carbon negative without explanation?
- Cross-reference tests: Do material codes match your reference database?
- With 105+ tests, most issues get caught before the data reaches anyone.
Automated tests turn "we checked it" into "here are the 105 checks that ran."
Why "automated" matters
View details →
How to interpret accuracy numbers
View details →
Common mistakes
View details →
See it in action
Need a data quality framework for your pipeline?