Data governance defines who owns data, how it is controlled, and what rules apply to it. Data quality ensures that data is accurate, complete, and consistent. Together, they ensure data can be trusted and used responsibly across the organization.

A policy document states intent. Governance and quality services turn that intent into working practice: assigned stewards, automated quality checks, enforced access controls, and evidence that is always ready, not a document that sits unused.

Monitoring typically covers accuracy, completeness, consistency, and timeliness, with automated rules that flag issues for remediation before they affect downstream decisions.

GEM maps governance controls to applicable regulations, such as the General Data Protection Regulation (GDPR) and, where relevant, HIPAA, so compliance requirements are built into the framework rather than addressed after the fact.

Access is controlled through role-based permissions, encryption, and monitoring, aligned to information security practices such as ISO/IEC 27001.

Yes. Governance extends to AI and ML training data and feature pipelines, so AI initiatives are built on data that is trustworthy and properly controlled from the start.

GEM defines KPIs during the Assess phase, such as data quality scores, time to resolve data issues, and audit readiness, and measures results against a pre-agreed business case.

Cost and timeline depend on the number of data domains and the current state of governance maturity. GEM provides a scoped plan and estimated effort before implementation begins.

High-impact decisions, such as changing access policies or reclassifying sensitive data, pass through human approval gates. Routine monitoring and alerting can run automatically within agreed rules.