data-lifecycle-and-retention-management

name	data-lifecycle-and-retention-management
description	Designs lifecycle, retention, archival, deletion, legal hold and disposal controls. Use when defining data retention policies, archival strategy, or compliant deletion.

Data Lifecycle and Retention Management

When to use

Use for datasets, products, logs, events, metadata, quarantine stores and refined datasets with retention obligations.

Objective

Produce a practical, concise, traceable architecture artefact that a coding agent can use to guide implementation or review.

Procedure

1. Identify data classes and lifecycle states.
2. Define retention by class and purpose.
3. Define archival/delete triggers.
4. Define legal hold exceptions.
5. Define ownership and approval.
6. Define disposal evidence.
7. Define lineage/audit impact.
8. Test retention enforcement.

Required outputs

• Lifecycle states
• Retention schedule
• Archive/delete rules
• Legal hold process
• Disposal evidence
• Implementation checks

Best-practice alignment

Apply DAMA-DMBOK2-style separation of data governance, architecture, modelling, security, integration/interoperability, master/reference data, metadata and quality. For cloud/shared data, apply CDMC-style expectations: ownership, classification, entitlement/access evidence, lineage/provenance, lifecycle/retention, quality controls and auditable evidence.

Quality checks

• Retention is purpose-based.
• Disposal is auditable.
• Legal holds override deletion.
• Lineage/audit impact is explicit.

Avoid

Do not keep data indefinitely because deletion is inconvenient.

References

• DAMA-DMBOK (DAMA International)
• EDM Council CDMC (Cloud Data Management Capabilities)
• ICO — storage limitation / retention

Verification

• Required artefacts produced and linked to scope.
• Decisions, assumptions and risks stated explicitly.
• Quality checks or validation performed.
• Files changed reported with traceability preserved.