Data Validation

Data Validation


RightData's rule-based data validation engine provides users and easy to use interface to create validation scenarios, which allows defining one or more validation rules against target data set, capture exceptions and ability to analyze and report.

Here are few examples of Validation Types

  • Duplicates - Find Duplicates in a Dataset (With or without Key Columns)
  • Reference Data - Missing reference data for dimensions in transactional data
  • Range Check - Checks that a value falls within the specified range
  • Length Check - Checks the data isn't too short or too long
  • Presence Check - Checks that data has been entered into a field
  • Pattern Matching Check - Check that data in the field matches a specific pattern
  • Complex Validation - Multi-step procedure-based business rules validation