des-data-quality

To define data quality rules across Bronze, Silver, and Gold layers, you must establish a specification covering dimensions, rule inventory, dataset-to-rule mapping, severity, and failure handling prior to implementation. This structured approach ensures clear quality boundaries for each layer.

A data quality specification is a structured document defining validation expectations, thresholds, and ownership before implementation. It is needed to prevent teams from shipping pipelines with unclear quality expectations by forcing explicit rule definitions and release gate candidates upfront.

Setting up quality gates and freshness SLAs requires designing a risk-based quality rule framework with explicit thresholds, severity levels, and evidence packs. This framework defines release and CI/CD gate candidates, ensuring pipelines halt safely on uncertainty before handing off to orchestration.

No, enforcing data contracts and validation rules requires Phase 13 transformation context and handoff artifacts as prerequisites. Without this prior transformation design, you cannot accurately draft the Phase 14 data quality specification needed for contracted P1 outputs.

The best way to handle workflow handoff for data quality orchestration is to generate explicit Phase 14 artifacts, including support plans, evidence packs, done gates, and Phase 14 to 15 handoff documents. This ensures downstream orchestration can observe quality without guessing.

You should not use predefined validation rules when Phase 13 transformation design context is missing or when outputs lack contracted P1 definitions. Applying rules without explicit dataset-to-rule mapping and severity boundaries risks shipping pipelines with misaligned quality expectations.