 
        
          Automated Quality Control
Successive data quality control layers continuously improve data quality while informing suppliers.
• Rules validate data at the source by blocking errors and inconsistencies while alerting suppliers.
• Validation workflows let retailer users manually review atomic changes to selected critical fields while letting non-critical changes pass through.
• Collaborative edits enable retailers to patch errors that would have gone undetected while proposing corrections to suppliers.
All the above serves as a training set for artificial intelligence (AI)-powered suggestions that help suppliers improve data quality.
 

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