Explains the parts of the ISO 8000 data quality standard
General principles and vocabulary, data quality management (the part 60 series, with part 61 the process reference model), and master data exchange: syntax, semantic encoding, conformance to requirements, provenance, accuracy and completeness of exchanged data (the part 100 series).
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 | 63% | Not shown |
| gemini-3.5-flash-lite | Low cost | 48% | Not shown |
| gpt-6-luna | Low cost | 48% | Not shown |
| claude-haiku-5-5 | Low cost | 48% | 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: General principles and vocabulary, data quality management (the part 60 series, with part 61 the process reference model), and master data exchange: syntax, semantic encoding, conformance to requirements, provenance, accuracy and completeness of exchanged data (the part 100 series).
How it is assessed: Answers a CDMP-style multiple-choice question on the ISO 8000 standard
All knobits of Data quality.
Source: DAMA-DMBOK: Data Management Body of Knowledge, 2nd edition (revised), chapter 13: Data Quality, element QV-7