Ensure data correctly represents real-world values and entities
Verify all required data is present without missing values
Maintain uniform data across different systems and over time
Ensure data is available when needed and up-to-date
Confirm data conforms to defined formats and business rules
Prevent duplicate records and ensure data integrity
Automated analysis of data structure, content, and relationships
Configurable rules engine for data validation and verification
Real-time monitoring of data quality metrics and KPIs
Automated techniques to detect and correct data quality issues
Integration with Great Expectations for data validation
Data quality testing framework using dbt test suite
Centralized management of critical business data entities
Policies, procedures, and accountability for data quality