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    Data Labeling

    AI fundamentals & modelsData identificationAnnotation
    Editorially reviewed on: 1 source
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    The short answer · 20 words

    In short

    Data annotation provides raw data according to established rules with classes, areas, relationships or other characteristics for training and evaluation.

    Flow-alerta.cab (2026): Data Labeling. https://flow-alerta.cab/en/glossar/data-labeling. Editorially reviewed on: 10 October 2026.

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    Practical

    The quality of annotation often limits the quality of monitored ML models. Clear guides, training, samples, review and, when appropriate tasks, the agreement between annotators make results more resilient.

    Support tickets are marked by cause and urgency; a model learns from this to propose new tickets, the final decision remains verifiable.

    Companies define taxonomy, examples, borderline cases, data protection and quality measures. SMEs can start with a small, representative and double-checked data set.

    At a glance

    Core
    In data annotation, people or systems mark relevant properties in text, images, audio or table data. Labels can be created manually, semi-automatically or automatically. They represent an agreed interpretation and are not automatically objective ground truth.
    Use
    The quality of annotation often limits the quality of monitored ML models. Clear guides, training, samples, review and, when appropriate tasks, the agreement between annotators make results more resilient.
    Example
    Support tickets are marked by cause and urgency; a model learns from this to propose new tickets, the final decision remains verifiable.
    Limits
    Unclear classes, changing rules, contextless examples, lack of review and sensitive data without protection. High conformity does not automatically prove that the label is technically correct or free of distortion.

    In context

    Human-in-the-Loop

    Human-in-the-loop means that people decide or examine in important places.

    Active Learning

    Active learning is a machine learning approach in which the model selects for which data points it needs additional labels or feedback.

    Questions about this term

    Who is annotating?

    Professional employees, service providers or automated procedures with control.

    How do you measure quality?

    Sampling, review and appropriate conformity measures.

    Are labels truth?

    They are rule-bound reviews and may contain errors.

    All details and background

    In data annotation, people or systems mark relevant properties in text, images, audio or table data. Labels can be created manually, semi-automatically or automatically. They represent an agreed interpretation and are not automatically objective ground truth.

    ELI5 – Explained simply

    You stick comprehensible labels on examples so that a learning system knows what to look for and what to distinguish.

    Why it matters in practice

    The quality of annotation often limits the quality of monitored ML models. Clear guides, training, samples, review and, when appropriate tasks, the agreement between annotators make results more resilient.

    Example

    Support tickets are marked by cause and urgency; a model learns from this to propose new tickets, the final decision remains verifiable.

    Pitfalls and limits

    • Unclear classes, changing rules, contextless examples, lack of review and sensitive data without protection. High conformity does not automatically prove that the label is technically correct or free of distortion.

    Origin & history

    Annotation is closely associated with supervised learning. Larger image, voice and text data sets resulted in specialized platforms as well as human-in-the-loop and active learning methods.

    Use in mid-sized companies

    • Companies define taxonomy, examples, borderline cases, data protection and quality measures. SMEs can start with a small, representative and double-checked data set.

    Related terms

    Matching Flow-alerta service

    AI opportunity analysis & roadmapAI data preparation and quality assurance: design annotation scheme, guidelines, review process and data protection-friendly workflow.

    Matching tool / use case

    View use casesLabel Studio or Vertex AI records; use case: classify documents and mark relevant text passages for an extraction model.

    Sources

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

    Editorially reviewed on:

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