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Table of Contents

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  1. Preface
  2. Working with Transformations
  3. Address Validator Transformation
  4. Aggregator Transformation
  5. Association Transformation
  6. Bad Record Exception Transformation
  7. Case Converter Transformation
  8. Classifier Transformation
  9. Cleanse transformation
  10. Comparison Transformation
  11. Custom Transformation
  12. Custom Transformation Functions
  13. Consolidation Transformation
  14. Data Masking Transformation
  15. Data Masking Examples
  16. Decision Transformation
  17. Duplicate Record Exception Transformation
  18. Dynamic Lookup Cache
  19. Expression Transformation
  20. External Procedure Transformation
  21. Filter Transformation
  22. HTTP Transformation
  23. Identity Resolution Transformation
  24. Java Transformation
  25. Java Transformation API Reference
  26. Java Expressions
  27. Java Transformation Example
  28. Joiner Transformation
  29. Key Generator Transformation
  30. Labeler Transformation
  31. Lookup Transformation
  32. Lookup Caches
  33. Match Transformation
  34. Match Transformations in Field Analysis
  35. Match Transformations in Identity Analysis
  36. Merge Transformation
  37. Normalizer Transformation
  38. Parser Transformation
  39. Rank Transformation
  40. Router Transformation
  41. Sequence Generator Transformation
  42. Sorter Transformation
  43. Source Qualifier Transformation
  44. SQL Transformation
  45. Using the SQL Transformation in a Mapping
  46. Stored Procedure Transformation
  47. Standardizer Transformation
  48. Transaction Control Transformation
  49. Union Transformation
  50. Unstructured Data Transformation
  51. Update Strategy Transformation
  52. Weighted Average Transformation
  53. XML Transformations

Transformation Guide

Transformation Guide

Process Flow for Field Match Analysis

Process Flow for Field Match Analysis

The following process flow summarizes the steps that you take to configure a Match transformation for field match analysis. You can define a process that uses the Match transformation alone or that uses the Match transformation and other transformations.
When you add a Match transformation to a mapping in field match analysis, add an upstream Key Generator transformation to the mapping.
To prepare the data for the Match transformation, perform the following steps:
  1. Organize the source data records into groups.
    Use a Key Generator transformation to assign a group key value to each record. The group assignments reduce the number of computations that the Match transformation must perform.
  2. Verify that the data source records contain unique sequence identifier values. You can use a Key Generator transformation to create the values.
Perform the following steps in the Match transformation:
  1. Specify field 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. Define a match analysis strategy. Select an algorithm, and assign a pair of columns to the algorithm.
    Use the
    Strategies
    view to define the strategy.
  3. Specify the method that the transformation uses to generate the match analysis results.
  4. 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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