Data Pipeline Validation
What This Requires
Validate data pipelines feeding AI systems for: data quality (completeness, accuracy), bias (demographic representation), lineage (source tracking), and compliance (consent, retention). Automate validation checks in CI/CD.
Why It Matters
Garbage in, garbage out. Poor data quality causes model drift, biased predictions, and compliance violations. Automated validation catches issues before they reach production.
How To Implement
Data Quality Checks
Implement automated tests: schema validation (expected columns present), null rate thresholds (e.g., <5% missing values), range checks (values within expected bounds), uniqueness checks (no duplicate records).
Bias Detection
For datasets used in sensitive decisions (hiring, lending), measure demographic distribution. Flag imbalances (e.g., <10% representation of protected class) for manual review.
Lineage Tracking
Instrument pipelines to log data source, transformation steps, and timestamps. Store lineage metadata in data catalog (Alation, Collibra).
Compliance Validation
Check that data sources have consent/license for AI use. Enforce retention policies (auto-delete data older than N days if required).
Evidence & Audit
- Data validation test suite with automated checks
- Pipeline run logs showing validation pass/fail status
- Bias analysis reports for sensitive datasets
- Data lineage documentation or catalog entries
- Compliance validation records (consent, retention)