What Fuzzy Match is
A matching tool for messy data. You upload a CSV or Excel file, pick the columns, and it finds records that refer to the same thing despite spelling differences, typos or formatting.
It is aimed at cleaning tasks rather than analytics.
What you can do with it
- Match names or addresses across tables
- Find duplicate records in a list
- Clean supplier or customer master data
- Compare text fields with semantic analysis
- Set similarity thresholds
- Export matched and unmatched results
Who it is for
- Operations and data analysis staff
- Sales operations teams
- Administration and finance teams
- Anyone reconciling two spreadsheets
What to watch out for
- Fuzzy matching can merge records that are genuinely different: review before merging
- Thresholds need tuning on your own data
- Uploaded files may contain personal data, so check handling and retention
- Master data merges should always have a human sign-off step
Pros & cons
✓ What we like
- Handles messy real-world text
- Works from CSV and Excel
- Semantic matching beyond exact strings
- Thresholds configurable
! What to watch out for
- Risk of merging distinct records
- Thresholds need tuning per dataset
FAQ
What does it solve?
Names, addresses and text fields failing to match because of spelling differences.
Can I merge on its results automatically?
High-risk matching should be manually reviewed before records are merged officially.
What should I prepare?
A CSV or Excel file, the fields to compare, matching rules and samples to check.
Last reviewed: 2026-09-15
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