Data quality issues propagate downstream, corrupting analytics and degrading ML model performance. By the time problems surface in dashboards or predictions, significant damage has occurred. Automated data quality monitoring catches issues at the source, enabling rapid remediation.
Quality Dimensions
Data quality spans multiple dimensions requiring different checks. Completeness monitors for missing values and records. Accuracy validates values against expected ranges and patterns. Freshness ensures data arrives on schedule. Consistency checks relationships between tables and fields.
- Implement schema validation catching structural changes before they break pipelines
- Monitor value distributions detecting anomalies in numeric and categorical fields
- Track record counts identifying unexpected volume changes
- Validate foreign key relationships ensuring referential integrity
- Set freshness SLAs alerting when data arrives late
Tool Ecosystem
Great Expectations provides Python-native data validation with extensive expectation libraries. dbt tests enable quality checks within transformation workflows. Monte Carlo and similar platforms offer automated monitoring without extensive configuration. Choose tools matching your stack and team expertise.