Have you ever encountered a situation where you have a list of customers and you need to identify which ones are the same, but there are duplicates due to misspellings or typos? In this article, we will explore how to effectively use T-SQL to accomplish fuzzy matching and grouping in SQL Server.

Let’s assume you have a list of prospective customers with customer IDs and first and last names. Our objective is to group or match the unique customer IDs and create an output list that links similar customers and standardizes the first names. For example, if we have customers with different variations of the name “Tom”, we want to normalize it as “Thomas”.

To achieve this, we will create a T-SQL stored procedure that utilizes the Jaro-Winkler algorithm for fuzzy matching. The Jaro-Winkler distance is a measure of similarity between two strings, specifically designed for short strings like person names. The higher the Jaro-Winkler distance, the more similar the strings are.

Here is an example of the T-SQL code for the Jaro-Winkler algorithm:

CREATE FUNCTION [dbo].[fn_calculateJaroWinkler](@str1 VARCHAR(MAX), @str2 VARCHAR(MAX)) RETURNS FLOAT AS 
BEGIN 
    -- Implementation of the Jaro-Winkler algorithm
    -- ...
    -- Return the Jaro-Winkler distance
    RETURN @jaro_winkler_distance 
END

Once we have the Jaro-Winkler algorithm implemented, we can use it to compare each row in the input dataset with a lookup or reference table. By filtering the rows with scores over a certain threshold (e.g., 95%), we can identify the similar first names and group them by assigning a name ID.

Here is an example of the T-SQL query that utilizes the Jaro-Winkler algorithm for fuzzy matching and grouping:

SELECT NameLookup.name_group_id, NameInput.Cust_Id, NameInput.Name_Input, NameLookup.first_name, NameLookup.first_name_normalized
FROM NameInput
CROSS JOIN NameLookup
WHERE dbo.fn_calculateJaroWinkler(NameInput.Name_Input, NameLookup.first_name) > 0.95
ORDER BY NameLookup.name_group_id

By running this query, we can obtain the grouped and normalized customer records based on fuzzy matching. The name_group_id serves as the match key or identifier for record linkage.

It’s important to note that this solution is not suitable for very high volumes, but it provides a workable T-SQL solution for matching and grouping required for deduplication. Further steps, such as merge/purge to create a single customer record, can be explored in future articles.

In conclusion, using T-SQL for fuzzy matching and grouping in SQL Server can be a powerful technique for identifying similar records and standardizing data. By implementing the Jaro-Winkler algorithm, we can achieve accurate results and improve data quality. Give it a try and see how it can benefit your data management processes!

Thank you for reading!

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