Explains the goals and principles of data quality management
Making data fit for purpose by a managed approach: criticality first, management across the data lifecycle, prevention over correction, remediation at the root cause, governance, standards, objective measurement and transparency, quality embedded in business processes, and service levels.
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: Making data fit for purpose by a managed approach: criticality first, management across the data lifecycle, prevention over correction, remediation at the root cause, governance, standards, objective measurement and transparency, quality embedded in business processes, and service levels.
How it is assessed: Answers a CDMP-style multiple-choice question on the principles of data quality management
All knobits of Data quality.
Source: DAMA-DMBOK: Data Management Body of Knowledge, 2nd edition (revised), chapter 13: Data Quality, element QV-2