🔄
Data Lifecycle
Data Management
End-to-end view of data from collection through processing, sharing, retention, and disposal, with controls at each stage.
Definition
Data lifecycle management defines controls for each stage: lawful collection, access controls, encryption, sharing governance, retention schedules, and verified deletion. It also includes backups and derived datasets.
In plain English
End-to-end view of data from collection through processing, sharing, retention, and disposal, with controls at each stage.
Why this matters
Why it matters: Lifecycle thinking prevents “data hoarding” and supports compliance with retention and deletion obligations.
Example
Example: Implement retention automation for logs, define deletion propagation, and review sharing/processing changes over time.