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    What is a data contract?

    A data contract is an agreement about what data means, who supplies it and what happens when it changes. It is how a definition gets an owner instead of an opinion.

    Max van GenderenFounder of Datahub, data and AI architecture4 min read
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    What is a data contract?

    A data contract is an explicit agreement between the supplier and the consumer of data: what does this data mean, what shape does it have, what can you expect from it, and who is responsible when it does not hold.

    The word "contract" sounds heavy, but the idea is plain. Today, data flows run on implicit assumptions. Someone builds a report on an export and assumes column G always holds net revenue. Six months later a colleague changes that logic in the source system, with the best intentions. The report keeps working and is quietly wrong. A data contract makes assumptions like that visible and enforceable.

    What it contains

    The definition. What is this, in the language of the organization? "Order complete" means: physically shipped and invoiced. Not something else.

    The shape. Which fields, which types, which units, which key is unique. This part is technical, which is exactly why it is easy to check.

    The expectation. How fresh the data is, how complete, which values are allowed, what deviation is acceptable. This is the quality promise.

    The owner. One person or team that set the contract and announces a change before it goes live.

    01The definitionOrder complete =shipped and invoiced02The shapeFields, types,units, unique key03The expectationDaily before 07:00,max 0.5% missing04The ownerAnnounces a changetwo weeks aheadDefinition, shape, expectation and owner on one page.

    Why it matters

    Data contracts solve a problem organizations usually experience as human conflict: two departments with two numbers for the same thing. Without a contract that is an argument about who is right. With a contract it is an argument about which definition applies, and that one can be closed, recorded and reused.

    For automation and AI it is even more direct. A system acting on data has to know when that data can no longer be trusted. A contract gives it a measurable point: if the promise breaks, the action stops, instead of quietly producing wrong decisions.

    The promisestandsShape and freshnessagreedThe check runsMeasured on everydeliveryThe promisebreaksA column shifts, aload runs lateThe action stopsNo quietly wrongdecisionThe ownerrepairsContract updated, thechain reopensIf the promise breaks the action stops, instead of failing quietly.

    How heavy should it be

    Far lighter than it sounds. One page per dataset is enough to start, and your first five contracts cover the five sources you actually lean on. A contract is not a governance programme; it is the shortest way to write down an assumption before someone breaks it by accident.

    Data contracts are part of the meaning layer under an organization. How that layer relates to the rest is in The company brain: where your organization's knowledge lives.

    A contract in four lines

    A workable contract fits on one page. For an order-line dataset it looks roughly like this: the definition ("order complete = shipped and invoiced"), the shape (order number is the unique key, amounts in euros excluding VAT), the expectation (refreshed daily before 07:00, at most 0.5% missing rows) and the owner (the logistics manager, changes announced two weeks ahead).

    Frequently asked questions

    Is a data contract legally binding? No. It is an internal agreement, not a legal document. Its power is in the clarity and in the checks that run on it.

    Who writes it? The supplier of the data, together with the main consumer. Both sides have to recognize it, otherwise it is a wish rather than an agreement.

    How many contracts do you need? Start with the five sources you actually lean on. More contracts than used sources is admin without return.

    What happens when a contract breaks? The automated action stops and the owner gets a signal. That is precisely the difference from failing silently.

    About the author

    Max van Genderen

    Founder of Datahub, data and AI architecture

    Max works on data foundations for logistics, retail and manufacturing: the governance, meaning and access layer that analytics and AI agents lean on. He designs the Datahub architecture, leads client implementations, and writes most of the articles and research pages on this site.

    Why this source

    • Designs and implements data foundations at logistics, retail and manufacturing organizations
    • Owns the foundation scan: the first-party measurement behind our research pages
    • Author of the pillars 'The company brain' and 'Managing intelligence'

    Writes about: Data governance · Semantic layer and data modelling · Private AI and AI agents · EU AI Act and data rules

    Reviewed by: DatahubDatahub editorial team

    More about the team

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