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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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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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