The GTM Data Hygiene Checklist: A Monthly Ritual
A GTM data hygiene checklist you can run monthly: duplicates, stale owners, broken syncs, and field drift, with a practical fix for each failure mode.
- Run the checklist monthly so every failure mode stays cheap to fix.
- Watch month-over-month counts; a jump means a faucet broke upstream.
- Check records, pipeline, and systems layers in the same session.
- Log every run so trends and ownership survive team changes.
Why a Checklist Beats a Cleanup Project
Data problems compound like interest. A broken sync ignored for a week is fifty bad records; ignored for a quarter it is a migration project. A monthly checklist keeps every failure mode inside its cheap-to-fix window, which is the entire economic argument for the ritual.
Checklists also survive personnel changes in a way that tribal knowledge does not. When the person who knows where the bodies are buried leaves, the checklist is what remains. Keep it in a shared doc with a completion log, and make the same hour of the month sacred.
The People and Company Checks
Run four record-level checks. Duplicates: scan for contacts sharing an email and companies sharing a domain, and merge with your survivorship rules. Stale ownership: list records owned by deactivated or departed users and reassign by territory. Bounced emails: flag hard bounces for suppression so sender reputation stays intact. Missing critical fields: count blanks on the fields routing and scoring consume.
Track each check's count month over month. A number that jumps is a faucet that just broke, and finding the faucet matters more than mopping this month's records.
The Pipeline and Process Checks
Then look at deals and motion. Flag opportunities with no activity in thirty or more days and close dates in the past, and hand the list to sales managers rather than editing records for them. Check for leads stuck in a stage beyond your SLA, which usually reveals a routing gap or an abandoned queue.
Review lifecycle stage distributions for weirdness, like a spike of contacts stuck in a handoff stage. Distribution shifts are the earliest symptom of a broken automation, visible weeks before anyone complains.
The Systems Checks
Finish at the infrastructure layer. Open every integration's error queue and clear or ticket what you find. Verify your key workflows fired at expected volumes this month, because a workflow that silently stopped enrolling is invisible until you look. Scan for fields and properties created since last month and ask who owns them.
Confirm UTM and source values ingested this month match your taxonomy, and quarantine the strays. End each run by logging counts and fixes in the checklist doc, so next month starts with a baseline instead of a blank page.
- Run the checklist monthly so every failure mode stays cheap to fix.
- Watch month-over-month counts; a jump means a faucet broke upstream.
- Check records, pipeline, and systems layers in the same session.
- Log every run so trends and ownership survive team changes.
Frequently asked questions
What should a GTM data hygiene checklist include?
Cover three layers: record checks like duplicates, stale owners, bounces, and missing critical fields; pipeline checks like inactive deals, past-due close dates, and SLA-breaching leads; and systems checks like integration error queues, workflow enrollment volumes, and UTM taxonomy compliance. Log counts each run so you can see trends. The full pass should fit in an hour or two monthly.
How often should we audit GTM data?
Monthly for the operational checklist, quarterly for deeper structural audits like field censuses and workflow inventories. Monthly is frequent enough to catch broken syncs and duplicate spikes while fixes are small, without becoming a burden nobody sustains. The consistency of the cadence matters more than its exact frequency.
Who should run the hygiene checklist?
One named owner in RevOps or marketing operations should run it, with findings routed to the teams that own the data, such as inactive deal lists going to sales managers. Shared ownership without a named runner is how the ritual dies quietly. Rotate the runner if you like, but never leave the slot empty.
What is the fastest sign that data quality is slipping?
Watch the trend lines on your checklist counts, especially duplicates created per month and integration errors. A sudden jump in any count almost always means a new form, import, or integration started misbehaving. Distribution shifts in lifecycle stages are the other early warning, appearing weeks before humans notice.
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