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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. 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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Match Transformation Overview

Match Transformation Overview

The Match transformation is an active transformation that analyzes the levels of similarity between records. Use the Match transformation to find records that contain duplicate information in a data set or between two data sets.
The Match transformation analyzes the values on an input port and generates a set of numeric scores that represent the degrees of similarity between the values. You can select multiple ports to determine the overall levels of similarity between the input records. You specify a minimum score as a threshold value to identify the records that are likely to contain duplicate information.
You can use the Match transformation in the following data projects:
  • Customer Relationship Management. For example, a store designs a mail campaign and must check the customer database for duplicate customer records.
  • Mergers and acquisitions. For example, a bank buys another bank in the same region, and the two banks have customers in common.
  • Regulatory compliance initiatives. For example, a business operates under government or industry regulations that insist all data systems are free of duplicate records.
  • Financial risk management. For example, a bank may want to search for relationships between account holders.
  • Master data management. For example, a retail chain has a master database of customer records, and each retail store in the chain submits records to the master database on a regular basis.
  • Any project that must identify duplicate records in a data set.
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