Loading content
A methodical approach to systematically test data quality, transformations and data pipelines using automated tests and validations.
Data testing is a methodical approach to systematically verify data quality and data pipelines using automated tests, validations, and contract checks. It detects inconsistencies, regressions, and integration errors early in the development cycle. The method covers test design, execution, monitoring, and governance to ensure reliable data products.
Share of tested metrics/transformations relative to the total scope.
Number of detected data defects per data volume or pipeline run.
Average time from defect occurrence to detection.
A retail team introduced data testing to secure price calculations and aggregations during deployments.
Two teams established contract checks to prevent breaking changes in shared data flows.
PoC implemented automated quality rules and significantly reduced data-related incidents.
Identify stakeholders and define quality goals
Prioritize critical metrics and test cases
Introduce test infrastructure and tools (e.g., Great Expectations)
Integrate tests into CI/CD and run on PRs
Set up monitoring and alerts for production data
Establish regular review and maintenance processes