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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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Master Data Analysis

Master Data Analysis

When you analyze two data sources in the Match transformation, you must identify a source as the master data set. The transformation compares data values from each record in the data set that you specify with the corresponding values in every record in the second data set.
In many organizations, a master data set constitutes a permanent, high-quality data store. Before you add records to a master data set, use the Match transformation to verify that the records do not add duplicate information to the master data.

Master Data Example

A bank maintains a master data set of customer account records. The bank updates the master data set every day with records that identify new customer accounts. The bank uses a duplicate analysis mapping to verify that the new records do not duplicate the customer information in the master data set. The master data set and the new account tables have a common structure, and the tables use the same type of database. Therefore, the bank can reuse the duplicate analysis mapping each time it needs to update the master data set.

Directionality in Master Data Set Analysis

The Match transformation compares records from one data set to another in a single direction. The transformation compares each record in the master data set to all of the records in the second data set. The transformation does not compare each record in the second data set to all of the records in the master data set. Therefore, the selection of the master data set can affect the results of the match analysis.
The following table shows two data sets that you can compare in identity match analysis:
Data Set 1
Data Set 2
Alex Bell
Alexander Bell
Alexander Graham Bell
Thomas Edison
Alva Edison
Nicola Tesla
Marie Curie
Irene Joliot Curie
Dorothy Crowfoot
Dorothy Hodgkin
If you select Data Set 1 as the master data set and you select the
Best Match
 output option, the output includes the following records:
  • Alex Bell, Alexander Bell
  • Alexander Graham Bell, Alexander Bell
If you select Data Set 2as the master data set and you select the
Best Match
 output option, the output includes the following records:
  • Alexander Bell, Alex Bell
When Data Set 2 is the master data set, the transformation cannot match Alexander Bell and Alexander Graham Bell, because Alexander Bell already matches Alex Bell in the output data.
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