Data governance and AI governance are often treated as the same project. They are not. Data governance manages the data; AI governance manages the models. Neither, on its own, governs the decision the model influences, which is where Digital Information Governance comes in.
| Dimension | How they differ |
|---|---|
| Scope | Data governance: structured data quality, lineage, access. AI governance: model bias, drift, explainability. |
| Asset governed | Data governance: the data. AI governance: the model. DIG: the decision. |
| Gap each leaves | Both can be in place while an AI-influenced decision is still undefendable. |
| Where DIG fits | DIG sits above both, governing whether the decision they enable can be defended. |
Frequently asked questions
Is AI governance part of data governance?
They overlap but are distinct. Data governance manages data; AI governance manages models. Digital Information Governance manages the decisions models influence, the layer above both.
Do I need all three?
In regulated settings, yes. Data and model governance are necessary but not sufficient. DIG closes the decision-level gap they leave.
Put DIG to work in your organization.Governance Readiness Assessment →Book a keynote on DIG →
References
- NIST AI Risk Management Framework (AI RMF 1.0): Govern, Map, Measure, Manage. National Institute of Standards and Technology, 2023. View source ↗
- Information governance: the records and data lifecycle discipline (storage, retention, disposition), distinct from AI decision governance. ARMA International, Generally Accepted Recordkeeping Principles; AIIM. View source ↗
- EU AI Act, Regulation (EU) 2024/1689 (Official Journal of the European Union); ISO/IEC 42001:2023; Texas Responsible AI Governance Act (TRAIGA). View source ↗
- USPTO Trademark Reg. No. 99559923, Digital Information Governance / DIG, owner Matthew Bertram. View source ↗