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  • Informatica Data Quality 10.4.0
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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. 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

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Process Flow for Identity Match Analysis

Process Flow for Identity Match Analysis

The following process flow summarizes the steps that you take to configure a Match transformation for identity match analysis. You can define a process that uses the Match transformation alone or that uses the Match transformation and other transformations.
Before you connect the Match transformation to upstream data objects, verify that the records contain unique sequence identifier values. You can use a Key Generator transformation to create the values. When you perform identity match analysis, you can optionally organize the input data into groups.
Perform the following steps in the Match transformation:
  1. Specify identity analysis as the match type, and specify the number of data sources.
    If you configure the transformation to analyze two data sets, select a master data set.
    Use the
    Match Type
     view to set the type and the number of data sources.
  2. Identify the location to store the index data. The transformation can write the index data to temporary files or save the index data to database tables.
    Use the
    Match Type
     view to specify the index data store.
  3. Define a match analysis strategy. Select a population and a comparison algorithm, and assign a pair of columns to the algorithm.
    The population indicates the column pairs to select.
    Use the
    Strategies
     view to define the strategy.
  4. Specify the method that the transformation uses to generate the match analysis results.
  5. Set the match threshold value. The match threshold is the minimum score that can identify two records as duplicates of one another.
    Use the
    Match Output
     view to select the output method and the match threshold.
    You can set the match threshold in a Match transformation or a Weighted Average transformation. Use the Weighted Average transformation if you create a match mapplet.
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