Use data contracts to make ownership executable

A data contract makes the expectations between a data producer and its consumers explicit. It covers more than column names: meaning, ownership, quality, timeliness, compatibility, access, and operational response all belong to the interface. Start with a named data product Identify the dataset, event stream, or API output as a product with an accountable owner. Define the business entities and measures in plain language. If “active customer” has three definitions, a schema registry cannot resolve the disagreement. The contract should include: field types, nullability, units, and accepted values; keys, uniqueness, ordering, and partition behavior; freshness, completeness, and availability objectives; classification, retention, residency, and access rules; compatibility policy and deprecation period; owner, support channel, and incident process. Validate near the producer Generate or validate schemas in the producer’s delivery pipeline. Test critical semantic rules against representative data. Detect contract violations before publication where possible, and quarantine or mark invalid records when blocking the entire stream would cause more harm. Consumers should validate assumptions too. Contract testing is strongest when registered consumers can express the fields and behavior they depend on. This turns an apparently harmless producer change into visible impact before deployment. The OpenAPI Specification, AsyncAPI Specification, and schema systems such as Avro each describe structural interfaces for different transports. A data contract uses those mechanisms but also carries semantic and operational commitments. Change through negotiation Prefer additive, backward compatible changes. For a breaking change, identify consumers, publish a migration path, run versions in parallel for a bounded period, and measure adoption. Do not keep unused fields forever because ownership is unclear. Version meaning, not just shape. Changing currency, time zone, aggregation window, or source logic can break a consumer without changing a type. Operate the relationship Monitor freshness, volume, schema conformance, distribution shifts, and access failures. Route alerts to the owner able to act. Review recurring exceptions as product feedback rather than treating them only as consumer misuse. A contract does not eliminate coordination. It makes coordination concrete, testable, and proportional to the impact of change. Define semantic rules explicitly Structural validation can confirm that amount is a decimal, but consumers also need currency, tax treatment, rounding, sign convention, and time of recognition. For timestamps, specify event time versus processing time, time zone, precision, and late arrival behavior. For identifiers, state stability, uniqueness scope, and whether values can be reassigned. Include representative valid, invalid, boundary, and redacted examples. Examples make ambiguity visible earlier than abstract field descriptions and can become executable fixtures for producer and consumer tests. Assign objectives and consequences Freshness might mean 95 percent of records available within fifteen minutes, measured from a named source timestamp. Completeness might compare received records with an authoritative control total. Define the measurement location, window, exclusions, and response when the objective is missed. Not every violation should stop production. A missing optional description may be quarantined or defaulted; a duplicated financial transaction may require immediate containment. Classify rules by impact and connect them to alerts, ownership, and remediation. Manage discovery and lineage Publish contracts in a searchable catalogue linked to owners, upstream sources, downstream consumers, transformations, quality history, and access policy. Automated lineage is useful but incomplete when data leaves through files, spreadsheets, or manual exports. Allow teams to declare and verify those edges. Readiness checklist Before declaring a contract active, verify business definitions, structural schema, examples, ownership, quality objectives, classification, retention, access, compatibility rules, consumer tests, monitoring, and incident routing. Before a breaking change, show affected consumers and a measured migration plan. A contract without a consumer or enforcement point is documentation; a contract with both becomes an operating boundary.