Data Quality

Catalog-side data-quality signals — test results from Great Expectations / dbt / odd-collector-profiler / custom frameworks, the cross-catalog Quality Dashboard, and dataset SLA statuses.

The Data Quality section of ODD Platform is the home for the catalog's correctness signals — test results pushed in from quality frameworks, the cross-catalog quality dashboard, and the operator-set dataset SLA statuses that downstream BI reports consume.

ODD covers Data Quality fully as an aggregator. Quality checks are not performed inside ODD Platform — the platform integrates with leading tools in the field and surfaces their results in one operator-friendly view. See the Data Governance map for the position of Data Quality among the other governance pillars.

Open it from the top-level navigation Data Quality tab (the catalog-wide dashboard) or from any data entity's Test reports tab (per-entity test results and SLA status).

Subsections

• Test Results Import — how test results land in the catalog: push-client integrations with Great Expectations and dbt, statistical profiles via odd-collector-profiler, and a custom-framework escape hatch via POST /ingestion/entities.

• Quality Dashboard — the catalog-wide quality view at /data-quality — three breakdown rings (Table Health / Test Results / Monitored Tables), six anomaly-class metrics, and the per-side filter sets (tables vs tests).

• Dataset Quality Statuses (SLA) — Minor / Major / Critical statuses on test results, the dataset-level aggregate SLA colour, and two endpoints for BI import: /api/datasets/{data_entity_id}/sla_report (the JSON report) and /api/datasets/{data_entity_id}/sla (a pre-rendered PNG badge).

• Test Run History — the per-test runs timeline (/dataentities/{id}/history) — every individual run, the status_reason diagnostic each upstream framework writes, status filter, infinite-scroll pagination, and the cross-owner read posture on status_reason text.

Why this is a separate pillar

For how Data Quality relates to the other governance pillars (Data Discovery, Data Modelling, Master Data Management, Data Lineage, Data Glossary), see Main Concepts → Data Governance map → Pillar differentiation — the canonical home for the six-pillar framing. Quality is its own pillar because the correctness signal cuts across every catalogued dataset; this landing consolidates the three ways an operator interacts with it (ingest test results, view the catalog-wide dashboard, curate per-dataset SLA statuses for BI consumption).

Where to next

• If you are connecting a quality framework into the platform → Test Results Import.

• If you are auditing the catalog's overall quality posture → Quality Dashboard.

• If you are exposing dataset-level quality to BI reports → Dataset Quality Statuses (SLA).

• For the data-quality-engineer use case end-to-end → Visibility for Data Quality Engineer.

• For DQ-test-failed alerts and where they surface → Alerting.

• For the broader catalog vocabulary → Main Concepts.