For Heads-of · Practitioner
Data Quality
The degree to which data is accurate, complete, consistent, and representative of the phenomena it purports to measure.
- data quality
- data governance
- accuracy
- validation
Dimensions
Accuracy (correct values), completeness (no missing data), consistency (uniform formats and definitions), timeliness (current), and representativeness (covers relevant populations).
Impact
Poor data quality directly degrades model performance. Models trained on low-quality data produce unreliable outputs, perpetuate biases, and fail in production.
Governance Practice
Continuous data quality monitoring and remediation is essential. Organizations should define quality standards, audit data regularly, and have processes to detect and correct quality issues.