Average Quality
Across 0 mappings
Passed
≥95% quality score
Warnings
80-95% quality score
Failed
<80% quality score
Filter Mappings
Quality Dimensions
Six key dimensions of data quality (BCBS 239 compliant)
Completeness
Percentage of non-null values
Accuracy
Data matches expected format and constraints
Consistency
Data is consistent across related fields
Timeliness
Data is up-to-date and current
Validity
Values are within acceptable ranges
Uniqueness
No duplicate records where uniqueness expected
Quality Score Trends
Track quality improvements over time
Chart visualization showing quality trends over 30d
Line chart with quality scores for each dimension
Quality Framework
How we measure data quality
Real-Time Validation
Quality checks run during transformation execution to catch issues immediately
Multi-Dimensional Scoring
Six independent dimensions provide comprehensive quality assessment
Trend Analysis
Track quality changes over time to identify improvements or degradation
BCBS 239 Compliance
Quality framework meets regulatory requirements for data quality monitoring
Overall quality score is a weighted average of all six dimensions. Each dimension is scored from 0 to 1, with thresholds: ≥0.95 = Pass, 0.80-0.95 = Warning, <0.80 = Fail. Weights are configurable based on your data governance policies.
Quality Best Practices
Recommendations for maintaining high data quality