Not all duplicates look the same. Some are byte-for-byte identical — the same row written twice. Others are near-duplicates: the same real-world entity represented slightly differently due to typos, abbreviations, or inconsistent data entry. Choosing the wrong deduplication method means you either miss real duplicates or accidentally merge records that should stay separate.
What Is Exact-Match Deduplication?
Exact-match deduplication removes rows where every selected column has an identical value. It's binary: either two rows match exactly or they don't. This is what Excel's Remove Duplicates and most CSV tools do by default.
Exact match works well when:
- Your data comes from a system that enforces consistent formatting (a database export, not a manually maintained spreadsheet)
- You're deduplicating on a unique identifier like an email address, transaction ID, or customer number
- The duplicates are genuine system-level duplicates, not data-entry variations
Sohovi automatically finds every duplicate in your dataset — including near-matches — and shows you exactly which rows are affected.
Example use case: An e-commerce platform exports customer records. The same customer submitted two orders with the exact same email — exact-match deduplication on the email column cleanly removes the second record.
What Is Fuzzy (Partial-Match) Deduplication?
Fuzzy matching finds records that are similar but not identical, using string similarity algorithms (like Jaro-Winkler or Levenshtein distance) to score how close two values are. Records above a similarity threshold are flagged as potential duplicates.
Fuzzy match is necessary when:
- Data was entered by humans, who make typos and abbreviate inconsistently
- You're matching on names (people or companies), where "Jon Smith", "John Smith", and "J. Smith" may all be the same person
- You're consolidating vendor records where "Acme Inc.", "Acme Incorporated", and "ACME" are the same supplier
Example use case: An accounts payable team exports vendor records. "Microsoft Corp", "Microsoft Corporation", and "Microsoft" all appear. Exact match keeps all three. Fuzzy match on the company name column identifies them as near-duplicates and flags them for review.
The Review Step Matters More With Fuzzy Matching
Exact-match deduplication is deterministic — you can run it and trust the result. Fuzzy matching produces candidate pairs that need human review before merging, because a high similarity score doesn't guarantee the records represent the same entity. "Smith & Jones" and "Smith & James" might score 85% similar but be completely different companies.
Build in a review step: export the flagged pairs, review a sample, set your confidence threshold conservatively, and only auto-merge records above 95%+ similarity.
For day-to-day CSV deduplication with exact matching, Sohovi's free Duplicate Row Remover handles both full-row and column-subset deduplication in your browser.
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