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Making Fuzzy Merges More Precise using the COMPARE Function in SAS Kathryn Martin, Maternal, Child and Adolescent Health Program California Department of Public Health ABSTRACT Linking records from

How to fill out making fuzzy merges more

How to fill out making fuzzy merges more:

Understand the concept of fuzzy merges:

1. Familiarize yourself with the concept of fuzzy merges, which involves matching and merging similar or approximate data from different sources.
2. Learn about the different techniques and algorithms used in fuzzy matching, such as Levenshtein distance or phonetic matching.

Choose the right tool:

1. Research and select an appropriate software or programming library that supports fuzzy merges.
2. Consider factors such as compatibility with your data sources, scalability, and ease of use.

Preprocess and clean your data:

1. Before performing fuzzy merges, ensure that your data is clean and standardized.
2. Remove any unnecessary characters, spaces, or special characters from the data.
3. Consider normalizing the data to a consistent format, such as lowercase or removing diacritical marks.

Define matching criteria:

1. Determine the specific criteria or rules for matching data records.
2. Identify which fields or attributes should be considered during the fuzzy merging process.
3. Adjust thresholds or weights for each attribute based on the importance and reliability of the data.

Apply fuzzy matching techniques:

1. Implement the chosen fuzzy matching algorithm or technique using the selected tool.
2. Use the defined criteria to compare and match records from different data sources.
3. Explore different parameters and options provided by the tool to optimize the fuzzy merge process.

Review and validate results:

1. Carefully review the merged results to ensure accuracy and identify any potential errors or mismatches.
2. Conduct manual inspections or spot checks on a subset of the merged data to validate the quality of the fuzzy merge.

Who needs making fuzzy merges more:

Data analysts and scientists:

1. Professionals working with large datasets and diverse data sources often need to merge and integrate data efficiently.
2. Fuzzy merges help them find approximate matches and consolidate information from different sources.

Marketing and sales teams:

1. Companies with customer databases need fuzzy merges to eliminate duplicate entries, consolidate customer information, and improve the accuracy of customer records.
2. Fuzzy merges can also help identify patterns, relationships, and trends across multiple datasets for targeted marketing campaigns.

Research institutions and academics:

1. Researchers often work with diverse datasets from various sources, such as surveys, databases, or research papers.
2. Fuzzy merges allow them to combine and analyze data from different domains, improving the quality and comprehensiveness of their studies.

Note: The content provided is a general overview and should be adapted or expanded based on the specific context and requirements of making fuzzy merges and its intended audience.

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