Say Goodbye to Messy Joins: Fuzzy Matching in Power BI Explained

Fuzzy matching is a technique used to compare and match data that is similar but not exactly the same. It helps identify records that are likely referring to the same entity, even if there are minor differences such as typos, formatting variations, or extra spaces.

It's useful when dealing with:

• Typos (e.g., "Jhon" vs "John")
• Abbreviations (e.g., "Corp." vs "Corporation")
• Extra/missing spaces
• Case mismatches

This technique is essential in data cleansing, record linkage, and data integration tasks where data comes from multiple inconsistent sources.

Origins in Computer Science: Fuzzy matching has roots in approximate string matching, developed for search engines, AI, and bioinformatics.

Algorithms: Classic algorithms include:

•   Levenshtein Distance (edit distance)
•   Jaro-Winkler Distance
•   Soundex (phonetic matching)
•   TF-IDF with cosine similarity (used in document comparisons)

These methods helped search engines, credit card fraud detection, and CRM deduplication from the 1970s onward.

In Business Intelligence: The need to merge messy datasets (e.g., customer names, addresses) brought fuzzy matching into tools like SQL Server Integration Services (SSIS), Excel, and laterPower BI.

Logical Concept of Fuzzy Matching

Fuzzy matching works by:

• Comparing strings using asimilarity algorithm.
• Scoring how similar two values are (e.g., 0.0 to 1.0).
• Returning matches above a certain threshold.

Example:

"Acme Inc" vs "ACME Incorporated" might score0.85 similarity, and if your threshold is 0.8, it's considered a match.

Where and When to Use Fuzzy Matching

Use Cases:

• Customer Deduplication ("Jon Smith", "John Smith")
• Address Matching ( "123 Main St", "123 Main Street")
• Company Merging ("ABC Ltd", "ABC Limited")
• Data Consolidation (Joining datasets from different systems with inconsistent naming)