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    Fuzzy Matching

    Data integration & searchUnsharp alignmentApproximate String Matching
    Editorially reviewed on: 2 sources
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    The short answer · 9 words

    In short

    Fuzzy Matching finds similar strings or records despite deviations.

    Flow-alerta.cab (2026): Fuzzy Matching. https://flow-alerta.cab/en/glossar/fuzzy-matching. Editorially reviewed on: 10 October 2026.

    How much detail would you like?

    Practical

    Useful for search and data cleanup, but error prone to short or frequent names.

    Hypothetical example: Supplier names from invoices are compared against master data candidates and confirmed manually.

    First, create candidate lists and check critical mergers using labeled examples.

    At a glance

    Core
    Unsharp matching compares strings using a similarity method. Power Query uses Jaccard similarity and an adjustable threshold in the Fuzzy Merge. A high score does not replace checking whether two records denote the same entity.
    Use
    Useful for search and data cleanup, but error prone to short or frequent names.
    Example
    Hypothetical example: Supplier names from invoices are compared against master data candidates and confirmed manually.
    Limits
    Take score as probability; ignore language and normalization; automatically merge; fail to measure false positives.

    In context

    Entity Resolution

    Entity Resolution detects which records describe the same real entity.

    Entity Resolution

    Entity Resolution detects which records describe the same real entity.

    Questions about this term

    Is Fuzzy Matching the Same as Entity Resolution?

    No, it can be a building block of it.

    How do you choose the threshold?

    Check right and wrong hits on labeled cases.

    All details and background

    Unsharp matching compares strings using a similarity method. Power Query uses Jaccard similarity and an adjustable threshold in the Fuzzy Merge. A high score does not replace checking whether two records denote the same entity.

    ELI5 – Explained simply

    It recognizes similar spellings, even if letters are missing or interchanged.

    Why it matters in practice

    Useful for search and data cleanup, but error prone to short or frequent names.

    Example

    Hypothetical example: Supplier names from invoices are compared against master data candidates and confirmed manually.

    Pitfalls and limits

    • Take score as probability; ignore language and normalization; automatically merge; fail to measure false positives.

    Origin & history

    Unsharp text matching is now documented in Power Query, among other things. The previous sources do not prove any concrete origin dating.

    Use in mid-sized companies

    • First, create candidate lists and check critical mergers using labeled examples.

    Related terms

    Matching Flow-alerta service

    AI opportunity analysis & roadmapAI consulting: Examine tasks, data and prerequisites of a possible AI use case.

    Matching tool / use case

    View use casesPower Query Fuzzy Merge; Similarity Threshold and Transformation Table.

    Sources

    Quellenprüfung: 10.10.2026. Definition und dokumentierte Einordnung abgeglichen; Beispiele beschreiben mögliche Anwendungen.

    Editorially reviewed on:

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