Data quality analyst: which parts can AI do?
A data quality analyst needs Data quality: 8 activities and 136 knobits, the smallest units of knowledge and skill worth learning and testing on their own.
- Mastered by the AI model
- Partly
- Where people lead
- Not tested
- Builds on (a prerequisite)
- Both mastered
- The activity’s knobits
Hover or tap a star to see the activity; what it builds on lights up.
How much of this job can AI do?
30knobits most AI models master
12that some models master
94where people still lead
0not tested yet
Of the 136 knobits the benchmarks cover, 22% are mastered by most of the 4 AI models tested. Mastering the knobits in a test is not the same as doing the job: someone still decides what to build, talks to the business, and answers for the result. But it shows where the work is shifting.
Activities, from most to least covered by AI
| Activity | Best AI model | Typical AI model (median) |
|---|---|---|
| Analyse the root causes | 94% | 88% |
| Manage data quality issues | 85% | 83% |
| Document data rules in SBVR | 89% | 82% |
| Derive data quality rules | 87% | 78% |
| Set data quality service levels and the scorecard | 81% | 76% |
| Profile and assess data quality | 77% | 67% |
| Select the critical data elements | 78% | 62% |
| Set the data quality strategy and framework | 63% | 48% |
For your career: where people still lead
The knobits the fewest AI models showed they master. Being good at these is what a person adds next to AI tools.
- Q-303 Recognises a constraint deferred without a check
- Q-702 Recognises a data quality service level threshold with no rationale
- Q-004 Recognises a data quality framework with no route to improvement
- Q-603 Recognises a data quality issue closed with no verification
- Q-403 Recognises a quality measurement labelled with a characteristic it does not test
- Q-315 Recognises a past event judged against present data
- Q-209 Recognises a gap in a partition of cases
- Q-304 Recognises a derived or copied column without a check
- Q-904 Explains the published frameworks of data quality dimensions
- Q-905 Explains the role of metadata in data quality
- Q-907 Explains the types of data quality business rules
- Q-910 Explains the categories of data quality tools
Where AI already helps the most
Knobits that almost every model tested masters: good candidates to hand to an AI assistant and review.
- Q-351 Explains data quality dimensions
- Q-353 Explains the population, measure and threshold of a check
- Q-451 Explains the ISO/IEC 25012 data quality characteristics
- Q-1901 Explains a pooled rate as the weighted mean of its population rates
- Q-1802 Explains the concept of the arithmetic mean
- Q-1804 Explains the concept of a percentage
- Q-1806 Explains the meaning of negation, conjunction and disjunction
- Q-651 Explains impact-based severity of data quality issues
Choosing an AI model for data quality analyst work
Best result for data quality analyst work: gemini-4-argon (89% test score). Best value: claude-haiku-5-5 (89%, within 5 points of the best at $0.006 per run instead of $0.021).
Per-activity results are on each activity page; all models are compared on the benchmarks page.
Test yourself
Skilirium's adaptive test measures you on the same knobits as the AI models. How a test works.