---
esco_uri: http://data.europa.eu/esco/skill/54c9c155-9293-4329-8ed2-2e76e8b519d4
esco_label: "define data quality criteria"
relation: optional
onet_soc: 15-2051.00
source: model-knowledge
confidence: high
qa_count: 0
generator: gemma3:27b (prompt-designed and spot-checked by Claude)
generated: 2026-07-10
---

# define data quality criteria — Data Scientist

As a Data Scientist, 'defining data quality criteria' isn’t about abstract ideals; it's *directly* tied to whether your models deliver useful business insights. You won't just list 'accuracy', you'll specify *what level* of accuracy is acceptable for a fraud detection model (e.g., >95% precision) versus a customer segmentation exercise (maybe 80% is sufficient). Daily tasks involve collaborating with stakeholders to translate business needs into measurable data quality rules – like defining valid ranges for age, acceptable formats for dates, or completeness thresholds for key demographic fields. This often means documenting these criteria in data dictionaries and profiling reports.

## Weitere Anreicherung

Stage-2 source for future practitioner grounding: arXiv cs.LG/stat.ML (ML preprints).
