What is fuzzy matching?
Real data is messy. The same company is "Acme Inc", "Acme, Incorporated", and "acme"; the same person is Bob and Robert. Exact matching treats these as different.
Fuzzy matching measures similarity rather than demanding equality, which is what makes deduplication work on real-world data.
Short answer
Fuzzy matching is finding records that are similar but not identical, so duplicates with typos, nicknames, abbreviations, or formatting differences are caught. Instead of requiring exact equality, it scores how alike two values are. Fuzzy matching is what catches the messy real-world duplicates that exact matching misses, like "Bob Smith" versus "Robert Smith".
Key takeaways
- Matches similar-but-not-identical values by scoring similarity.
- Handles typos, nicknames, abbreviations, and formatting differences.
- Catches duplicates that exact matching misses.
- Usually tuned with a confidence threshold to balance false matches against misses.
Why it matters
Exact matching only catches perfect copies, which are the minority of real duplicates. Fuzzy matching catches the rest - the typos, nicknames, and format differences that cause most double outreach and split history. It is what makes dedupe effective on actual data.
How Ardovo handles it
Ardovo uses fuzzy matching alongside exact rules and scores every candidate by confidence, so it catches messy duplicates without wrongly merging distinct records. Rook auto-merges high-confidence matches and flags borderline ones for review.
Frequently asked questions
How is fuzzy matching different from exact matching?
Exact matching requires values to be identical; fuzzy matching scores how similar they are and matches above a threshold. Exact catches perfect copies, fuzzy catches the typos, nicknames, and formatting differences that make up most real duplicates.
What is a match confidence threshold?
The similarity score above which two records are treated as a match. A high threshold means fewer false matches but more missed duplicates; a lower one catches more but risks wrong merges. Tuning it balances precision against recall for your data.
Can fuzzy matching create wrong merges?
If the threshold is too loose, yes - it can flag distinct records as duplicates. That is why good systems score confidence and auto-merge only high-confidence matches while routing borderline candidates to a human for review.