Data Quality Frameworks
Poor data quality costs organizations billions. A systematic framework ensures data is fit for purpose across the enterprise.
Data Quality Dimensions
- Completeness: No missing required values
- Accuracy: Data reflects reality
- Consistency: Same data, same meaning everywhere
- Timeliness: Data available when needed
- Validity: Conforms to business rules
- Uniqueness: No unwanted duplicates
Implementation
- Define quality rules per data domain
- Automate quality checks in pipelines
- Track quality metrics over time
- Assign data stewards for ownership
- Remediate issues at the source
Quality is not a one-time fix—it requires ongoing measurement and improvement.