Explains the goals and principles of metadata management
Goals: record the organisation's knowledge of its data, collect and integrate metadata from many sources, give a standard way to reach it, and keep it of known quality, current and secure. Principles: organisational commitment, a strategy, an enterprise view, socialisation, access, quality, audit and improvement.
Can AI do this?
People still lead Few or none of the tested AI models showed they master this. 0 of 4 models in the current benchmark master it.
| Model | Tier | Probability of mastery | Status |
|---|---|---|---|
| gemini-4-argon | Flagship | 45% | Not shown |
| gemini-3.5-flash-lite | Low cost | 46% | Not shown |
| gpt-6-luna | Low cost | 44% | Not shown |
| claude-haiku-5-5 | Low cost | 46% | Not shown |
Mastered means a probability of at least 90% that the model has this knobit, estimated from its graded answers. “Not shown” means the answers do not settle it either way. How the models are tested.
What it looks like in practice
Expected result: Goals: record the organisation's knowledge of its data, collect and integrate metadata from many sources, give a standard way to reach it, and keep it of known quality, current and secure. Principles: organisational commitment, a strategy, an enterprise view, socialisation, access, quality, audit and improvement.
How it is assessed: Answers a CDMP-style multiple-choice question on the goals of metadata management
All knobits of Metadata management.
Source: DAMA-DMBOK: Data Management Body of Knowledge, 2nd edition (revised), chapter 12: Metadata Management, element MV-2