Explains the steps of building a metadata strategy
Initiate planning, interview stakeholders, assess the existing metadata sources and architecture, develop the future architecture, and plan a phased implementation.
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 | 45% | Not shown |
| gpt-6-luna | Low cost | 44% | Not shown |
| claude-haiku-5-5 | Low cost | 45% | 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: Initiate planning, interview stakeholders, assess the existing metadata sources and architecture, develop the future architecture, and plan a phased implementation.
How it is assessed: Answers a CDMP-style multiple-choice question on the metadata strategy steps
All knobits of Metadata management.
Source: DAMA-DMBOK: Data Management Body of Knowledge, 2nd edition (revised), chapter 12: Metadata Management, element MV-9