Smart matching is a data matching method that cleans values before comparing them.
It sits between exact matching and fuzzy matching. It is stricter than fuzzy matching, but more flexible than raw exact matching.
Example
Raw exact matching can treat these values as different:
Example.comhttps://www.example.com/example.com?utm_source=ad
Smart matching can normalize the values first, then compare the cleaned result.
What smart matching can clean
Smart matching can handle differences such as:
- Uppercase and lowercase text
- Extra spaces
- Punctuation
- URL formatting
- Email formatting
- Company legal suffixes
- Basic text variations
📌 Short version
Smart matching is useful when values should match after cleanup, but you do not want loose fuzzy matches.
Smart matching vs fuzzy matching
Fuzzy matching uses similarity scores to find values that are close, even with typos.
Smart matching focuses on normalization. It removes formatting noise and compares the cleaned values.
Use smart matching for domains, URLs, emails, company names, and fields where formatting changes are common.
Use distance matching when you need threshold-based similarity with algorithms such as Levenshtein or Jaro-Winkler.
Use phonetic matching when names sound alike but are spelled differently.
Datablist workflow
Datablist supports smart matching in the Duplicates Remover. The duplicate worker uses specialized comparison logic for text, URLs, emails, and company names.
Use it after data normalization and before auto-merge.