The pattern: enable Data State in Data Classification, classify fields per current usage, build downstream consumers (DSR scoping, retention automation, compliance reports) that respect the state, audit quarterly as fields transition. The classification is most valuable when the workflows that consume it exist; configuration alone is inventory.
- Enable Data State in Data Classification Settings
Setup, Data Classification Settings. Confirm Data State is in the enabled dimensions list.
- Decide whether to use standard or customized values
Active/Archived/Purged works for most orgs. Customize only when downstream tooling will consume the additional values.
- Classify existing fields through bulk Download/Upload
Per-field classification is impractical past a few dozen fields. Use the bulk pattern: download, classify in CSV, upload back.
- Build DSR workflow that respects Data State
Privacy Center or custom Flow/Apex. Scope DSR scoping by Data State = Active (or per policy).
- Build retention automation for Archived fields
Scheduled Flow or Apex that clears Archived fields on records past retention window. The automation is the enforcement of the classification.
- Build compliance reports filtered by Data State
Active-only filters answer regulator questions about live data; Archived-included filters answer broader inventory questions.
- Audit field lifecycle quarterly
Workflow changes propagate to fields; the Data State classification needs updates. The quarterly cadence catches transitions before they go stale.
Active, Archived, Purged. The trichotomy that works for most orgs.
Pending, Deprecating, Legal Hold for orgs that need finer lifecycle distinction.
Which Data State values are in scope for DSR exports and deletions.
Scheduled clearing of Archived fields per the retention policy.
Active-only, Active+Archived, all states based on report purpose.
- Static Data State classifications go stale. Workflows change; the classification needs updates or it stops reflecting reality.
- Custom Data State values add value only when downstream tooling consumes them. Custom values without consumers are inventory only.
- Retention automation requires explicit build. The Data State classification is the upstream decision; admins build the clearing logic.
- DSR scoping that ignores Data State includes Archived and Purged fields, producing broader exports than the policy requires.
- Bulk classification via Download/Upload is the only practical path for orgs with hundreds of fields. Per-field assignment does not scale.