Skip to content
    Flow-alerta.cab

    Data Enrichment

    Data integration & searchData processingAttribute enrichment
    Editorially reviewed on: 1 source
    Your reading view

    The short answer · 12 words

    In short

    Data enrichment complements existing data sets with linked, external or derived information.

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

    How much detail would you like?

    Practical

    Enrichment can improve segmentation, search, analysis and automation if allocation, timeliness and rights of use are right. It is not the same as data cleansing or ML data augmentation.

    Item master data shall be supplemented by a unique product group with standardised categories and manufacturer information.

    SMEs first define the concrete benefits, clear keys, permissible sources and quality indicators. The origin and date of update of each additional attribute should be traceable.

    At a glance

    Core
    In data enrichment, existing data is augmented by additional attributes, references or metadata. Sources can be internal systems, open data or calculated features. The supplement can increase the utility value, but also introduce errors and risks.
    Use
    Enrichment can improve segmentation, search, analysis and automation if allocation, timeliness and rights of use are right. It is not the same as data cleansing or ML data augmentation.
    Example
    Item master data shall be supplemented by a unique product group with standardised categories and manufacturer information.
    Limits
    Incorrect or ambiguous assignment, outdated third-party data, unexplained license, missing origin and unnecessary personal characteristics. “More data” does not automatically mean better quality.

    In context

    Entity Resolution

    Entity Resolution detects which records describe the same real entity.

    Feature Engineering

    Feature Engineering creates or transforms input features for analysis and models.

    Questions about this term

    Is enrichment equal to cleansing?

    No, cleanup corrected; enrichment supplemented.

    Can external data be used?

    Only with a suitable license, legal basis and quality.

    How do you measure success?

    About hit rate, timeliness and benefits in the target process.

    All details and background

    In data enrichment, existing data is augmented by additional attributes, references or metadata. Sources can be internal systems, open data or calculated features. The supplement can increase the utility value, but also introduce errors and risks.

    ELI5 – Explained simply

    An existing address list is supplemented with checked regions or geocoordinates so that more questions can be answered.

    Why it matters in practice

    Enrichment can improve segmentation, search, analysis and automation if allocation, timeliness and rights of use are right. It is not the same as data cleansing or ML data augmentation.

    Example

    Item master data shall be supplemented by a unique product group with standardised categories and manufacturer information.

    Pitfalls and limits

    • Incorrect or ambiguous assignment, outdated third-party data, unexplained license, missing origin and unnecessary personal characteristics. “More data” does not automatically mean better quality.

    Origin & history

    Enrichment is an established method of data integration and metadata maintenance. W3C describes them as processes for supplementing, refining or otherwise improving existing data.

    Use in mid-sized companies

    • SMEs first define the concrete benefits, clear keys, permissible sources and quality indicators. The origin and date of update of each additional attribute should be traceable.

    Related terms

    Matching Flow-alerta service

    AI assistants & automationData integration and process automation: Evaluate sources, design matching rules and transfer enriched data into processes in a controlled manner.

    Matching tool / use case

    View use casesOpenRefine Reconciliation or API Matching; Use Case: Complement supplier and product master data with standardized reference features.

    Sources

    • www.w3.orgW3C, „Data on the Web Best Practices – Data Enrichment“ (Abruf 05.10.2026)

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

    Editorially reviewed on:

    Next step

    Put knowledge into practice

    Discuss your projectView use cases