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  • Informatica Data Quality 10.4.1
  • 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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Bad Record Exception Quality Issues

Bad Record Exception Quality Issues

Quality issues are text strings that describe the type of data quality problem that caused a low record score. The Bad Record Exception transformation receives quality issues associated with each source row that contains a low record score. You can configure different types of transformations that determine quality issues and record scores.
For example, you might create a Decision transformation that examines the phone number. The Decision transformation generates the record score and the quality issues for phone number.
The following decision strategy identifies phone numbers of incorrect length in a Decision transformation: 
IF LENGTH(Phone_Number) > 10 THEN
  Score:=50
  Phone_Quality_Issue:='Phone num too long'
ELSEIF  LENGTH(Phone_Number) < 10 THEN
  Score:=50
  Phone_Quality_Issue:=' Phone num too short’
ELSE
  Score:=90
ENDIF
When you configure the Exception transformation, you must associate Phone_Quality_Issue with the Phone_Number port. The ports are from different input groups.
The Exception transformation reads the scores generated by the Decision transformation and assigns records with a score of "50" to the bad records group of output ports. It writes the Phone_Quality_Issue to the Issues group of output ports.
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