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

Field Match Analysis and Identity Match Analysis

Field Match Analysis and Identity Match Analysis

You can configure a Match transformation to perform field match analysis or identity match analysis.
In field match analysis, the Match transformation analyzes the source data that enters the transformation. You can perform field match analysis on any type of data. In identity match analysis, the Match transformation generates an index of alternative data values from the input data and analyzes the index data. Configure the Match transformation for identity match analysis when the input ports contain identity data. An identity is a group of data values that identifies a person or an organization.
A data set can represent a single identity in different ways. For example, the following data values represent the name John Smith:
  • John Smith
  • Smith, John
  • jsmith@email.com
  • SMITHJMR
The Match transformation reads the identity data in a record and calculates the possible alternative versions of the identity. The transformation creates an index that includes the current versions and alternative versions of the identities. The Match transformation analyzes the index values and not the values in the input records.

Identity Population Files

Identity match operations read reference data files called populations. The population files define the potential variations in the identity data. The files do not install with Informatica applications. You buy and download the population data files from Informatica.
Install the files to a location that the Content Management Service can access. Use Informatica Administrator to set the location on the Content Management Service.

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