Data Governance
The discipline of managing data as a corporate asset: classification, quality, lineage, catalog, ownership, privacy, access, retention — the full picture.
What is Data Governance?
The set of rules, roles, and processes that turn data into a trusted, usable, and compliant corporate asset.
Data Classification
Labeling every dataset by its sensitivity so the right controls (access, encryption, retention) apply automatically.
Data Quality
The dimensions (accuracy, completeness, consistency, timeliness, validity, uniqueness) that make data trustworthy — and how to measure and fix them.
Data Lineage
The map of where data comes from and where it goes: upstream sources, transformations, downstream consumers. Essential for trust, impact analysis, and compliance.
Data Catalog & Metadata
The searchable yellow-pages of your data estate: what datasets exist, what they mean, who owns them, and where they come from.
Data Ownership & Stewardship (RACI)
Who's accountable for each dataset vs who does the day-to-day work. Clear roles kill the 'whose problem is this?' paralysis.
Privacy & Compliance (GDPR, CCPA, HIPAA)
The main data privacy regulations, what rights they grant to individuals, and what technical obligations they create for your systems.
Access Control — RBAC & ABAC
Who can access what data, under what conditions. RBAC (by role) is simple but rigid; ABAC (by attributes) is flexible but complex. Most orgs use both.
Retention & Data Lifecycle
When to keep data, when to archive it, when to delete it — driven by legal, business, and privacy requirements.
Implementing Data Governance (Frameworks & Maturity)
How to actually roll out governance in a real organization without boiling the ocean. Start small, show value, expand by domain.