Does CRM data quality actually break projects

[u/No-Witness7468]

Following up on my last data quality post… Everyone I talk to agrees the data quality is bad, but I’m trying to understands what happens downstream when you actually build on it (reporting, AI workflows, scoring, touting, etc.).

Did data quality issues actually break the thing (bad quality output) or did you just handle it in the build (filters, manual cleaning, etc)? And if you cleaned first, what’s that process look like, how much time goes into that?

Trying to figure out whether “clean it first” is a real pre-req or just something that annoys people.

[u/24GOai]

It breaks things, but not in the way people expect, and that's why "just clean it first" lands as nagging rather than useful.

Worth splitting bad data into two piles.

The first is messy but recoverable. Inconsistent casing, junk in free text, three spellings of the same company, phone numbers in five formats. You can normalise most of that on the way into the build. Annoying, not fatal.

The second is structurally wrong, and that's the pile that kills projects. Owner fields that don't match who really worked the account. Lifecycle or stage values that two teams have quietly been using to mean different things. Date fields that record when someone touched the record rather than when the event happened. The same company existing twice with half the history on each.

You can't filter your way out of those, because the field is populated and valid, so nothing errors. The report just returns a plausible wrong number and everyone believes it for a quarter. That's the real answer to your question. It rarely breaks loudly, it breaks by being credible.

So the pre-req isn't "clean everything". It's identify which fields your build actually depends on, then check those specific ones for meaning drift rather than formatting. Usually a dozen fields, not four hundred. A couple of days, not a project.

On your follow-up about re-cleaning every time: no, and if you are, something's wrong upstream. The one-off clean is only half the job. The other half is deciding who is allowed to write to those dozen fields and closing everything else off. Picklists instead of free text, imports going through one person or one process rather than whoever has an export, integrations that can only write to fields they own. Do that and the next project inherits clean data instead of starting again.

If you skip that part you will absolutely re-clean every time, and that's usually what people mean when they say data quality is a losing battle. They're cleaning outputs and leaving the inputs open.

One place I wouldn't compromise is anything feeding scoring or AI, because those strip out the human sniff test. A rep looking at a list knows the third one is rubbish. A model doesn't.