Fuzzy matching finds text values that are similar without being identical. It is useful when typos, spelling variations, abbreviations, or inconsistent data entry prevent an Exact match.

Examples include:

  • Katherine Johnson and Katherin Johnson
  • Northwind Trading and Northwind Tradng
  • 12 Market Street and 12 Market St

How fuzzy matching works

A distance algorithm compares two strings and returns a similarity score. Levenshtein counts the edits needed to transform one value into another. Jaro-Winkler gives extra weight to matching prefixes and is often useful for short names.

A threshold decides which scores become candidate matches. Lowering the threshold finds more candidates but also increases false positives.

📌 Practical rule

Start with a strict threshold. Lower it only when you are prepared to review broader candidate groups.

Fuzzy matching is not normalization

Smart matching cleans formatting differences before comparison. Fuzzy matching measures how close the remaining strings are.

Use Smart matching for predictable differences such as URL protocols, phone formatting, email aliases, punctuation, or word order. Use fuzzy matching when the value itself may contain a typo.

Use fuzzy matching safely for deduplication

Names alone are weak identifiers. Confirm fuzzy name matches with an email, phone number, domain, address, or external ID before merging records.

Datablist exposes Levenshtein and Jaro-Winkler through the Distance algorithm in its Duplicates Finder. These advanced algorithms are available on paid plans.

Compare the matching options in the data matching guide, or follow the complete list deduplication workflow.