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  • Informatica Data Quality 10.5
  • All Products

Table of Contents

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  1. Preface
  2. Introduction to Transformations
  3. Transformation Ports
  4. Transformation Caches
  5. Address Validator Transformation
  6. Aggregator Transformation
  7. Association Transformation
  8. Bad Record Exception Transformation
  9. Case Converter Transformation
  10. Classifier Transformation
  11. Comparison Transformation
  12. Consolidation Transformation
  13. Data Masking Transformation
  14. Data Processor Transformation
  15. Decision Transformation
  16. Duplicate Record Exception Transformation
  17. Expression Transformation
  18. Filter Transformation
  19. Hierarchical to Relational Transformation
  20. Java Transformation
  21. Java Transformation API Reference
  22. Java Expressions
  23. Joiner Transformation
  24. Key Generator Transformation
  25. Labeler Transformation
  26. Lookup Transformation
  27. Lookup Caches
  28. Dynamic Lookup Cache
  29. Macro Transformation
  30. Match Transformation
  31. Match Transformations in Field Analysis
  32. Match Transformations in Identity Analysis
  33. Normalizer Transformation
  34. Merge Transformation
  35. Parser Transformation
  36. Python Transformation
  37. Rank Transformation
  38. Read Transformation
  39. Relational to Hierarchical Transformation
  40. REST Web Service Consumer Transformation
  41. Router Transformation
  42. Sequence Generator Transformation
  43. Sorter Transformation
  44. SQL Transformation
  45. Standardizer Transformation
  46. Union Transformation
  47. Update Strategy Transformation
  48. Web Service Consumer Transformation
  49. Parsing Web Service SOAP Messages
  50. Generating Web Service SOAP Messages
  51. Weighted Average Transformation
  52. Window Transformation
  53. Write Transformation
  54. Appendix A: Transformation Delimiters

Developer Transformation Guide

Developer Transformation Guide

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Duplicate Record Exception Process Flow

Duplicate Record Exception Process Flow

The Duplicate Record Exception transformation analyzes the output of other data quality transformations and creates tables that contain records with different levels of data quality.
You can configure data quality transformations in a single mapping, or you can create mappings for different stages in the process.
You can use the Analyst tool to review and update the duplicate records that require manual review.
Use the Developer tool to perform the following tasks:
  1. Create a mapping that generates score values for data quality issues.
  2. Use a Match transformation in cluster mode to generate score values for duplicate record exceptions.
  3. Configure the Duplicate Record Exception transformation to read the Match transformation output. Configure the transformation to write records to database tables based on match score values in the records.
  4. Configure target data objects for automatic consolidation records.
  5. Click the
    Generate duplicate record table
     option to create the duplicate record table and add it to the mapping canvas.
  6. Add the mapping to a workflow.
  7. Configure a Human task to assign manual review of possible duplicate records to users. Users can review and update the records in Analyst tool.
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