Your CRM Is Lying to You

Here is a test. Open your CRM and search for your three biggest customers. Count how many times each one appears. Then look at how many open deals are dated more than a year ago. Then find out how many company records have no phone number in the phone field, because the phone number is in a note.

If you winced, keep reading.

Dirty data is a tax on everything you build

A CRM that is 80% accurate is not 80% useful. It is worse than useless, because you will make decisions from it and the errors arrive with the confidence of official numbers.

The damage compounds in a specific order. First the reports are wrong, so people stop trusting them and go back to their own spreadsheets. Then you build an automation, and instead of a person being annoyed by the duplicate, a system emails the same customer twice, or a forecast double-counts a deal. Then you point AI at it, and it confidently summarizes a pipeline that is half fiction.

Automation and AI do not fix bad data. They industrialize it. A human catches a duplicate once a week; a workflow fires it a thousand times a month. This is why I will not build an automation on top of a CRM I have not cleaned first, and why any vendor who promises you otherwise is selling you a demo.

Two weeks, not two quarters

The mistake is treating this as a project with a start and an end, or worse, trying to fix every field on every record. You will not finish, and the team will conclude that data work is hopeless. Do this instead.

1. Pick the fields that matter. Three to five, not thirty. Which fields does a report actually depend on? Which one does your quoting break without? Usually it is something like: company name, contact email, deal stage, deal value, close date. Everything else is decoration. Give yourself permission to leave decoration dirty.
2. Enforce at entry, from day one. Make the critical fields required. Replace free-text with dropdowns wherever a human is guessing at spelling. Kill the stages nobody uses, because a dead stage is where deals go to hide. Fixing the input stops the bleeding; everything after it is mopping.
3. Archive the dead weight. Old records from a product you no longer sell, leads from a campaign three years ago, contacts who left the industry: archive them. Not deleted, archived. A smaller working set makes every later step faster and every report less wrong.
4. Merge the duplicates you find while doing the above. Do not attempt a heroic deduplication pass on day one. You will find the worst duplicates naturally while fixing the fields that matter, and you will care about them, which is what makes the work stick.
5. Install a fifteen-minute weekly ritual. One person, one slot, one checklist: new duplicates this week, deals sitting in one stage too long, records missing critical fields. Fifteen minutes, same time every week, forever.

That is the whole plan. Two weeks to get to a baseline you can trust, then a habit that maintains it.

The part nobody wants to hear

The cleanup will not survive without an owner. Not a committee, one person whose name is on the weekly ritual and who has permission to tell a salesperson their record is wrong. Data hygiene fails at companies this size for exactly one reason: it is everyone's job, which means it is no one's job.

Clean data is not a project. It is a habit with a checklist. The companies that get value out of reporting, automation, and AI almost always have one thing in common before any of it worked: someone owns the fifteen minutes.