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Application of Fuzzy Matching Techniques Using SAS Software Kirk Paul Lafler, Semicolon Analytics, Spring Valley, CA Stephen Sloan, Data Science Senior Principal, Accenture, Cream Ridge, NJ ABSTRACT
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01
Understand the concept of fuzzy matching techniques.
02
Gather the data that needs to be matched using fuzzy techniques.
03
Choose the appropriate fuzzy matching algorithm.
04
Preprocess the data by cleaning and standardizing it.
05
Implement the chosen fuzzy matching algorithm on the data.
06
Evaluate the results and adjust parameters if necessary.
07
Document the process and results for future reference.

Who needs application-of-fuzzy-matching-techniques-using?

01
Data analysts looking to clean and merge datasets.
02
Businesses aiming to improve data quality and accuracy.
03
Researchers working on record linkage and deduplication tasks.
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Developers building search engines or recommendation systems.
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Application of fuzzy matching techniques is the process of using algorithms to identify similar data entries that may not be exact matches, often used in data processing and cleaning.
Individuals or organizations involved in data analysis, data management, or technologies that require the reconciliation of disparate datasets may be required to file.
To fill out the application, one must provide details such as the datasets to be matched, the methodology used, and the expected outcome of applying fuzzy matching techniques.
The purpose is to improve data accuracy and integrity by identifying and merging similar records that may contain variations or errors.
Information such as the source datasets, matching criteria, algorithms employed, and results of the matching process must be reported.
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