Table of Contents


  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. Match Transformation
  30. Match Transformations in Field Analysis
  31. Match Transformations in Identity Analysis
  32. Normalizer Transformation
  33. Merge Transformation
  34. Parser Transformation
  35. Python Transformation
  36. Rank Transformation
  37. Read Transformation
  38. Relational to Hierarchical Transformation
  39. REST Web Service Consumer Transformation
  40. Router Transformation
  41. Sequence Generator Transformation
  42. Sorter Transformation
  43. SQL Transformation
  44. Standardizer Transformation
  45. Union Transformation
  46. Update Strategy Transformation
  47. Web Service Consumer Transformation
  48. Parsing Web Service SOAP Messages
  49. Generating Web Service SOAP Messages
  50. Weighted Average Transformation
  51. Window Transformation
  52. Write Transformation
  53. Appendix A: Transformation Delimiters

Developer Transformation Guide

Developer Transformation Guide

Null Match Scores

Null Match Scores

A match algorithm applies a predefined match score to a pair of values when one or both values are null. You can edit the match score that a field match algorithm applies to null values.

Null Match Scores and Field Match Algorithms

When you configure a field match algorithm, verify the match score values that the algorithm applies to null data. A field match algorithm applies a default score of 0.5 when it compares two values and one or both values are null. A score of 0.5 indicates a low level of similarity between data values.
Consider the following rules and guidelines when you verify the null match scores:
  • When the algorithm analyzes columns that contain primary keys or other critical data, do not edit the default scores. In this case, a null value represents a data error, and the default scores are appropriate for the data.
  • When the algorithm analyzes columns that can optionally contain data, update the null match score values to the same value as the match threshold. You cancel the effect of the null values on the match analysis when you set the null match scores to the match threshold value.

Null Match Scores and Identity Match Algorithms

An identity match algorithm applies a match score of 0 when it compares two values and one or both values are null. Identity match analysis assigns a record with a null match score to a unique record cluster and records a cluster size value of 1. You cannot edit the score that an identity match algorithm applies to null data.


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