| 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
- Identify data classes and lifecycle states.
- Define retention by class and purpose.
- Define archival/delete triggers.
- Define legal hold exceptions.
- Define ownership and approval.
- Define disposal evidence.
- Define lineage/audit impact.
- 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
Verification
