What to gate
- Schema/profile validation (fail fast).
- Terminology normalization and code system checks.
- Unit normalization and time zone handling.
- Duplicate detection and idempotency rules.
Insights
AI-ready data quality workflows
AI initiatives fail quietly when data quality is inconsistent. The fix is not “more dashboards,” but quality gates that make errors obvious, actionable, and hard to ignore.
Define “AI-ready” for your use case
Data quality is contextual. Start by defining the minimum bar your downstream systems actually need.
- Completeness: required fields present (and not defaulted to meaningless values).
- Conformance: shape matches schema/profile expectations (HL7/FHIR/warehouse).
- Plausibility: values are clinically and operationally reasonable (units, ranges, timestamps).
- Consistency: codes and identities are stable across sources and time.
- Traceability: you can explain where a value came from and what transformed it.
Install quality gates in the pipeline
The most effective checks run before data is published downstream, not after it breaks dashboards or models.
How to operationalize
- Make failures actionable: clear reasons and owners.
- Track an error backlog and burn it down intentionally.
- Write runbooks: what to check, how to reproduce, how to fix.
- Use fixtures and regression tests so fixes do not regress.
Want validation built into your pipeline?
We can help define quality gates, implement validation checks, and leave behind workflows your team can run without heroics.