What deletion schedules do

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Rules that remove old data automatically. Useful, permanent, and worth understanding before you create one.

What they are

A rule that removes data of a given type once it reaches a given age. Tickets, attachments, end users, custom object records and AI agent conversations each have their own.

Once configured, they run without anybody deciding again.

Why that is the point

A retention policy nobody enforces is a document. A schedule is the policy actually happening, every month, without depending on somebody remembering.

Why that is also the risk

It runs whether or not the period was right. A schedule with a wider scope than intended removes a great deal, efficiently, permanently.

Nothing asks whether you are sure after the first time.

Deletion is not redaction

Redaction removes information from a ticket and keeps the ticket. Deletion removes the record entirely, including from your reporting.

For a data subject request, redaction is usually right. For retention, deletion is what the policy means.

It affects your history

Deleted tickets leave Explore. A schedule removing everything older than a year ends year-on-year comparison, and that is discovered later, by somebody building a report.

Decide what aggregate history you need before removing the detail.

Start with attachments

Most storage, most risk, least reporting value. It is the schedule with the best ratio of benefit to regret.

See also

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