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Data Contract

Data managementIntermediate Level

A data contract is an agreement between the producer and the consumer of data that defines its structure, allowed values, quality rules and ownership.

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What is Data Contract?

A data contract is an agreement between whoever produces data and whoever uses it, written so software can check it. It defines the fields, their types and allowed values, which fields are required, quality rules, who owns the data and how changes are announced. A data contract goes further than a data schema. A schema only describes the structure; the contract adds rules, ownership and a promise about how the data will behave. In e-commerce a typical contract sits between a supplier and a retailer, or between a PIM and a webshop: every product must have a GTIN, a price in euros and a category from an agreed list. Automated checks then stop records that break the contract, as the data validation guide explains.

Why Data Contract matters for e-commerce

Product data passes through many hands: suppliers, ERP, PIM, webshop, marketplaces and feeds. Without clear agreements, one side changes a field name or format and the other side breaks without warning. A data contract makes those agreements explicit and testable. For retailers it means supplier files arrive in a predictable shape. For brands it means channels receive what they expect. And when something does go wrong, the contract shows which side broke the rule, which shortens the search for the cause.

Examples of Data Contract

  • 1Field: gtin, Type: String of 8, 12, 13 or 14 digits, Rule: Required, valid check digit
  • 2Field: price, Type: Decimal, Rule: Greater than 0, currency EUR
  • 3Field: category, Type: Text, Rule: Must match the agreed category list
  • 4Change rule: Renaming or removing a field needs 30 days notice to all data users
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How WISEPIM Helps

  • Define attributes with types and allowed values on the Attribute values page, so products follow one shape.
  • Turn contract rules into Quality Guard rules that flag products which break them.
  • Map supplier columns to your attributes during import, so incoming files match your model.
  • Check per channel in Channel Readiness whether products meet what that channel expects.

Common mistakes with Data Contract

  • Writing the contract as a document nobody checks automatically.
  • Defining field types but no rules for allowed values or required fields.
  • Not naming an owner, so nobody decides when the contract changes.
  • Changing fields without notice, which breaks downstream systems.

Tips for Data Contract

  • Start with the few fields that break things most often, such as identifiers, price and category.
  • Check the contract automatically on every import, not only once a quarter.
  • Share the contract with suppliers as a template with examples.
  • Version the contract itself, so everyone knows which rules apply.

Trends around Data Contract

  • Data contracts move from data warehouses into operational flows such as supplier onboarding.
  • Contracts are written in machine-readable formats such as YAML or JSON Schema.
  • AI tools generate first drafts of contracts from sample data.

Tools for Data Contract

  • WISEPIM (attribute model and Quality Guard rules)
  • Great Expectations
  • dbt tests
  • Confluent Schema Registry
  • JSON Schema

Related Terms

Attribute CollisionCanonical Data ModelCircular Economy Data ModelCross-Border Trade (CBT) AttributesData DecayData Deduplication

Also Known As

Data agreementSchema contractData interface specificationData sharing agreement

Frequently Asked Questions

A data contract is an agreement between the producer and the user of data. It defines the fields, types, allowed values, quality rules and ownership, in a form software can check.

A schema describes the structure of the data, such as field names and types. A data contract includes the schema and adds quality rules, ownership and how changes are handled.

A typical example is the agreement between a supplier and a retailer on product files: required fields, valid GTINs, price format and allowed categories. Each incoming file is checked against those rules.

Usually the team that produces the data owns it, in agreement with the teams that use it. The owner decides on changes and announces them before they take effect.

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