Knowing where your data is is not enough. You need to know who owns it, what it is worth, and how it lives.
Data governance turns a scattered asset into a managed one: every data point has an owner, a definition, a measured quality and a documented lifecycle. It is the foundation on which compliance, security and AI rest.
Business dictionaries and glossaries — shared definitions, common vocabulary across business, IT and management
Catalogues and metadata — inventory of data, its origin and its uses
Ownership and stewardship — clear allocation of responsibilities: who decides, who maintains, who controls
Data quality — indicators, control rules, monitoring over time
Lifecycle and Master Data — from creation to archival, reference data management
How this pillar connects
Governance is the foundation the other pillars rest on. Without a data dictionary and clear ownership, the DPO record has no anchor, cybersecurity does not know what to protect first, and AI cannot trace the lineage of what it learns from. This is the pillar KDPO builds first — everything else stands on it.
What you get
- A business dictionary and shared glossary
- A data catalogue with origin and uses documented
- An ownership and stewardship matrix: who decides, maintains, controls
- Data quality indicators and a lifecycle framework
Track record
Setting up a data and AI governance strategy on a multi-division datahub: documenting processes and indicators, mapping uses, reviewing access rights, defining a data access service, and maintaining compliance over time.