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Data Governance & Quality

Explore data quality, data catalog, and compliance governance platforms for data management.

Choosing guide

2 results in Data Governance & Quality

Active Pro listings from this category appear first. Your sort applies within Pro and Free results.

Focused collections

Shortlists for Data Governance & Quality

Explore collections with tools relevant to this category, grouped by deployment, source model or workload.

Self-hosted IT tools →

2 matching tools from this category

IT tools with an API →

2 matching tools from this category

Open-source IT tools →

2 matching tools from this category

How to choose data governance & quality tools

Data-governance tools describe, classify and control data so teams can understand ownership, lineage, quality, access and policy across platforms.

Read the guide

Who uses these tools

  • Data platform and governance teams
  • Security, privacy and compliance owners
  • Analysts and engineers finding trusted datasets

Capabilities to compare

  • Catalog, glossary and ownership
  • Lineage, classification and quality signals
  • Access policy, workflow and audit evidence

Operating considerations

  • Connector depth and metadata freshness
  • Automated classification accuracy
  • Stewardship workflow and policy enforcement boundaries

Selection checklist

  1. Scan representative structured and unstructured sources with known sensitive data.
  2. Trace lineage through one production transformation and validate missing edges.
  3. Measure the work required to keep ownership, glossary and exceptions current.

What belongs in this category: A product belongs here when governance metadata, policy or stewardship is a primary capability rather than a reporting add-on.

Data Governance & Quality on iThub in figures

iThub lists 2 tools in Data Governance & Quality. 2 of 2 are open source, 2 of 2 can be self-hosted, 2 of 2 have a free, freemium or open-source pricing model and 2 of 2 offer an API.

Tools listed
2
Open source
2
Self-hostable
2
Free to start
2
With an API
2
Web app
2
Desktop app
1

Recently added: c15t, Comp AI

Typical use cases

  • Sensitive-data discovery
  • Dataset ownership and certification
  • Impact analysis before schema changes

Trade-offs to model

  • Automated discovery increases coverage but creates false positives.
  • Central catalogs improve finding while adding another metadata system to maintain.
  • Policy visibility is not enforcement unless integrated with the data platform.

Common mistakes

  • Launching a catalog without assigned stewards
  • Counting connectors instead of validating metadata depth
  • Publishing confidence scores without a correction workflow

Explore data and analytics →Compare Airbyte and Fivetran →Browse API-enabled tools →

Decision resources

Continue the technical evaluation

Use the resources that match your decision stage. Profile data and linked sources were refreshed through November 11, 2026.

Evaluation methodology · Corrections process