> For the complete documentation index, see [llms.txt](https://docs.zingg.ai/latest/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.zingg.ai/latest/zingg-concepts/zingg-configuration/field-definition/match-types/fuzzy-match.md). # FUZZY Match The broadest match type handles typos, abbreviations, transpositions, and real-world data variation. The default starting point for most name and address fields. ### What `FUZZY` does `FUZZY` applies a string similarity comparison to two field values and produces a score reflecting how similar they are. It tolerates the full range of real-world variation: missing characters, transposed letters, common abbreviations, spelling differences, and format inconsistencies. It is Zingg's most permissive match type and the right starting point for any field where values can legitimately vary between records representing the same entity. ### What `FUZZY` matches and what it does not
Value AValue BMatch?
Jonathan SmithJon SmithYes - abbreviation and short-form variation
J. SmithJohn SmithYes - initial vs full name
JonathonJonathanYes - common transposition
JohnsonSmithNo - too different, no common characters
IBM CorpIBM CorporationYes - long shared prefix scores high
[null]John SmithDepends - add NULL_OR_BLANK to control null behaviour
### When to use `FUZZY`
First name, last name, full name fields `FUZZY` is the right match type for name fields in most datasets. Real-world name data contains spelling variations, abbreviations, culturally different name orders, and transliterations that `FUZZY` handles automatically. For datasets with known nickname patterns ("Jon" / "Jonathan", "Bill" / "William"),\ consider combining `FUZZY` with `MAPPING_(nicknames)` to catch cases that are too different for `FUZZY` alone.
Company names and organisation names "IBM", "I.B.M.", and "IBM Corporation" have high `FUZZY` similarity. Most common abbreviations and punctuation variants will score above the match threshold. For very different representations ("IBM" vs "International Business Machines"), combine `FUZZY` with `MAPPING_(company_names)` to map the canonical form explicitly.
Free-text address lines Full address lines benefit from `FUZZY` for the street name component. For structured addresses where the street number and street name are in the same field, consider splitting your approach: Use `ONLY_ALPHABETS_FUZZY` for the street name component (ignores numbers, applies fuzzy to letters) and `NUMERIC` for the number component. Or apply `FUZZY` to the full field and accept that number differences will influence the score.
### When not to use `FUZZY`
Trusted identifiers- SSN, passport, tax ID, national ID Never use `FUZZY` on fields that are reliable unique identifiers. `FUZZY` tolerance on an SSN field means "123-45-6789" and "123-45-6780" could score above the match threshold. That is a false positive you cannot afford in a compliance context. Use `EXACT` for trusted identifiers.
Date of birth, registration date, event date Dates should always use `EXACT`. A fuzzy comparison between "1985-06-15" and "1985-06-16" might score high enough to produce a match - but those are different people or different events. Use `EXACT` for all date fields.
Postal codes and ZIP codes Use `PINCODE` not `FUZZY` for postal codes. `PINCODE` is built to handle the specific format variants postal codes appear in (xxxxx vs xxxxx-xxxx) without introducing the tolerance that `FUZZY` adds, which could match "94102" and "94103" as similar.
When performance at scale is critical `FUZZY` is computationally heavier than `FUZZY_OPTIMISED`. For production runs on large datasets where you want the same matching quality with faster results, use `FUZZY_OPTIMISED` instead.
{% hint style="success" icon="right-long" %} **Related match types:** * `FUZZY_OPTIMISED` - same quality, better performance at scale * `ONLY_ALPHABETS_FUZZY` - strip numbers first, then apply fuzzy to letters only * `MAPPING_(FILENAME)` - handle completely different strings (nicknames, abbreviations) that `FUZZY` cannot bridge **Read more**: [Match Types](/latest/zingg-concepts/zingg-configuration/field-definition/match-types.md) | [Configure Zingg](/latest/running-zingg/configure-zingg.md) | [How Zingg Learns](/latest/zingg-concepts/how-zingg-learns.md) {% endhint %} --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://docs.zingg.ai/latest/zingg-concepts/zingg-configuration/field-definition/match-types/fuzzy-match.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.