Documentation / Operating model
The data lifecycle
Entry conditions, participants, controls and evidence in four stages.
Purpose
Start small. Define a purpose, follow the four stages and use feedback to improve the next iteration.
Cultivate / Prepare with purpose.
Entry condition: A defined use case and identified source.
Accountable participants: Product owner + data engineering
Working practices: Connect sources; document provenance; prepare data; establish reproducible delivery.
Evidence to keep: Source inventory, transformation record and initial quality checks.
Decision gate: The source is authorised and the output can be explained.
Example measures: Source coverage and delivery failures.
Govern / Make trust actionable.
Entry condition: Prepared data with a proposed meaning and use.
Accountable participants: Steward + product owner + domain owner
Working practices: Agree definitions, classification, quality thresholds, permitted uses and decision rights.
Evidence to keep: Glossary, versioned contract, quality results and policy decisions.
Decision gate: Ownership, terms and quality expectations have been reviewed.
Example measures: Ownership coverage and quality exceptions.
Share / Publish an agreement.
Entry condition: An approved product and a clear audience.
Accountable participants: Product owner + steward + technical custodian
Working practices: Publish metadata and terms; review access requests; apply controls; communicate changes.
Evidence to keep: Published contract, approval record, access policy and version history.
Decision gate: The intended consumer can discover and access the authorised version.
Example measures: Access lead time and contract adoption.
Activate / Observe the value.
Entry condition: An authorised consumer and an agreed purpose.
Accountable participants: Consumer + product owner
Working practices: Use the product in analytics, applications or AI; monitor outcomes; report issues; review continued use.
Evidence to keep: Usage record, outcome indicator, issue log and improvement decision.
Decision gate: Value and limitations are reviewed; improvement or retirement is decided.
Example measures: Use against purpose, incidents and value indicator.
Synthetic example
A fictional service-planning team needs a weekly aggregate of requests. The service domain owns the meaning; a subdomain team prepares the data; a product owner agrees freshness and allowed use. The first release is an internal aggregate, not a copy of identifiable source records. The team records quality results and reviews whether the product supports the intended planning decision.
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Original KDV Framework knowledge. Working community edition 0.1. CC BY 4.0. References retain their owners’ rights; no official assessment or certification is implied.