Improve information trustworthiness
Continuously improve data quality
Provide visibility into quality of data
Reduce risks for propagating bad data
Find data anomalies and remediate data issues
Easily understand data and its relationships
Create data mapping rules for data integration
Define data cleaning and validation rules
Show ROI for data quality initiatives
Quantify impact of poor data on business
Speed up data integration projects with better DQ
information
Validate governance controls are effective on data
quality (for example: investment in MDM)