A field-quality specification
The conventions, required fields, and ownership rules that define a usable record.
Sales / CRM data hygiene
A CRM your team can work from.
Find inconsistent fields, likely duplicates, and missing ownership before they distort routing and reporting.
Book a call ↗Free 30-minute call. Bring one task.
Where it gets stuckCRM data hygiene,
CRM data hygiene,
with the context attached.
CRM cleanup can become a permanent side project. Different spellings, imports, and stale fields make it hard to trust a list. But a plausible duplicate is not permission to merge two customers or overwrite a useful history.
A good first scope
Pick the fields affecting one downstream job, such as lead routing or a pipeline report. Do not begin with “clean everything.”
The workflow
- 01 / The starting point
A scheduled review inspects the agreed CRM fields.
- 02 / The right context
Compare formatting rules, required values, and possible duplicate signals.
- 03 / Prepared for review
Prepare suggested corrections and a review list for ambiguous matches.
- 04 / A person decides
A record owner approves merges or consequential changes before application.
Before we startThree things
Three things
to settle first.
Which downstream job is hurt most by bad data: routing, reporting, or outreach?
Which fields decide a match, and which shared values (a company email, a branch address) are legitimate?
Who confirms a merge, and how is a rejected suggestion remembered so it is not proposed again?
Tangible work
A useful working handoff
Proposed corrections
A reviewable change set with original values, suggested values, and reasons.
A controlled update path
Preview, approval, change history, and recovery expectations for the operations in scope.
How we would startMap it. Test it.
Map it. Test it.
Then decide.
Agree the trigger, source systems, reviewer, and acceptance criteria. Build the bounded preparation path, then test it with the person who will own it.
Include the awkward cases
Test two people with similar names, a legitimate shared email address, and an update that arrives during review.
Keep this boundary explicit
No bulk deletion or irreversible merge is assumed. External enrichment, purchased data, and consent or retention decisions require separate consideration.
See how acceptance evidence works ↗A few useful answers
Before we begin.
Will it delete duplicate records?
Not by default. It should distinguish likely matches from approved merges and preserve a human decision for risky changes.
Can it keep data clean over time?
Recurring checks can be scoped, but ownership and upstream entry rules still matter. Automation cannot define your data policy for you.