Glossary · IIoT, data and AI
Data governance
German: Data Governance
In information management, data governance is the framework of roles, responsibilities, policies, standards and processes that ensures data is managed as an asset: defined, owned, of known quality, protected and used in line with legal and business requirements.
- IIoT
In one sentence
Data governance defines who owns data, which rules apply and how quality, security and lawful use are ensured across its lifecycle.
Example
A manufacturer appoints data owners for material, equipment and customer master data and requires their approval for any change to field definitions.
How it applies
- Engineering: Industrial data comes from many sources: controllers, MES, ERP, PLM and quality systems. Governance defines the authoritative source for each data object and how it is named and identified.
- Operation: Data owners and stewards approve changes, resolve conflicts and monitor Data quality. Without them, dashboards and AI models drift apart from reality.
- Documentation: Documentation teams are both data consumers and data owners: product data, safety information and metadata in manuals should come from governed sources, not be retyped.
Data governance vs. data management
Data governance decides and oversees: who is responsible, what rules apply. Data management executes: storing, integrating, securing and delivering data according to those rules. Governance also underpins AI governance, since AI systems depend on the data they are trained and fed with.