Match Tuning Best Practices - Data Profiling, Analysis, Design, and Implementation

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Enhance your match tuning processes with best practices in data profiling, analysis, design, and implementation. Understand the life cycle of match rules, optimize attribute profiling, and fine-tune your matching algorithms for better results.

  • Match Tuning
  • Data Profiling
  • Analysis
  • Design
  • Implementation
  • Best Practices

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  1. Match Tuning Best Practices July 21, 2021

  2. Match Rule Life Cycle Data Profiling and Analysis Testing and Tuning Design and Implementation Match Tuning 2

  3. Data Analysis and Profiling - Profiling For each attribute from each source: Cardinality Completeness (Empty Strings, 0s, and nulls) Uniqueness Common Values (12/31/9999, 123-456-7890) Noise Words (Inc, Org, LLC) Standardization (Email, Phone, and Address) 3

  4. Data Analysis and Profiling - Analysis Identifying Attributes Differentiating Attributes Categorizing Attributes Low Uniqueness Differentiates data within a nested attribute Examples Address.AddressType Educiation.University High Uniqueness Varying Compleness High Completeness Examples Examples Gender SSN City Email Phone 4

  5. Design and Implementation - Design Configure rules in suspect rule type during design Set useOvOnly = true by default Be cautious when using negative rules Put match attributes names in the rule label Consider your Comparator and Tokenizer choices 5

  6. Design and Implementation - Tokens ignoreInToken when In a not operand with ExactOrNull or ExactAndAllNull when the thresholdChars is used Low cardinality Attributes With multiple similar match rules One high identifying attribute per rule Keep Token Count < 300

  7. Testing and Tuning - Static Match Rule Analyser GREEN - This indicates that the rule follows syntax and the analyzer has not found any major issue with the configuration. YELLOW - This indicates that the rule is identified either or has some similarities with an existing rule BLUE - This indicates that some of the operators used in the match rules could be either wrong or inefficient 7

  8. Testing and Tuning - Dynamic Match Rule Analyser 8

  9. Testing and Tuning - Troubleshooting Match Explanation API 9

  10. Questions?

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