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Data Integrity Module

Automatic Data Health Checks

A background system that quietly checks your data for a handful of known trouble spots every day, and fixes what it safely can — with no screen of its own to visit.

Runs daily, automatically Nothing to click — there’s no dashboard for this module

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What this keeps healthy

This module isn’t a screen you open — it’s a set of checks that run on their own, across every company on your install, looking for the same handful of specific issues that have shown up before in this app: things like an employee record missing its linked Contact, a department that lost track of which company it belongs to, or a GL journal reference pointing at nothing. Where a check can safely correct what it finds, it does — automatically, without asking.

How it runs

A scheduled job runs once a day and works through each registered check in turn, across every company. Every check is written to be safe to repeat endlessly — running it again when there’s nothing wrong simply finds nothing, so there’s no risk in it running quietly forever in the background.

Where to see what it found

You won’t see this module’s own page anywhere in the app — but if a run actually fixes something, it leaves a record. Open System Logs and filter Source to data-integrity to see exactly what was found and corrected, and when.

No entry means nothing needed fixing

A quiet day for this module is the normal, healthy outcome — it only writes to System Logs when it actually repairs something.

What this module doesn’t do

No manual “run now” or settings screen

There’s nothing here to configure or trigger by hand — it’s an unattended background process by design, the same way System Logs has no “add entry” button for the same reason: a self-reported health check isn’t a trustworthy one if a human can also switch it off or fake it.

Fixes known issue shapes, not everything

Each check targets one specific, previously-seen problem — it’s not a general-purpose data validator that catches anything that could ever go wrong.

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