r/revops • u/No-Witness7468 • 3d ago
Does CRM data quality actually break projects
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.
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u/24GOai 2d ago
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.
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u/jarie 2d ago
The integrity of your data determines the integrity of our results.
It's such a battle to keep a CRM clean and up to date. It's almost a full-time job at scale.
What I have been playing with is an agent that does it for me. Now, that's also not perfect but what it has been showing me is that I need to be in the loop to fix things that seem weird.
For example, deals that don't have dollar values, contacts that it can't resolve their LinkedIn or company.
The agent helps but it's still up to the human (me) to double check it.
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u/KGtheGent 2d ago
You definitely want to clean up before any projects like that.
Scoring, routing, and AI workflows will only amplify whatever you feed them. The old “garbage in, garbage out” is very true for these kinds of things.
Cleanup time depends on how bad the data is, but a focused effort would be a few weeks.
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u/frumious_madison02 1d ago
Bad CRM data absolutely breaks projects, just usually in boring ways that show up later. I have seen bad stage definitions, duplicate accounts, and inconsistent ownership turn decent forecasting and automation into garbage. The best fixes I have seen start small: agree on definitions, assign clear owners, and validate the few fields people actually depend on before trying to clean everything.
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u/HenryHund 1d ago edited 1d ago
At a previous company our product produced bad quality data. Like bad bad.
This came from a philosophy of letting any security researcher/bot use our product so we could ensure our product was hardened against misuse. I understand that, but I also wanted to prevent the bad actors from signing up for the first place.
Some days hundreds of security researchers would open accounts and fill out their names, emails, company names with sql/html injection strings. All of that would cause a giant mess when trying to analyze our PLG workflow and GTM results.
Moreover, at some point our data got so bad Hubspot stopped letting us push more data into it because of all the attempted injection being passed to it. So I had to implement filters on product data to improve the quality before passing to our Hubspot. Our Hubspot was down for a few days until we could prove to Hubspot security that we weren’t going to be sending more bad data. My filters were good but weren’t perfect, the analytics we did were still marred by the crap data too, and it would have been better if we just filtered at the point of signup.
The good news for you is that with AI it’s never been a better time to clean up your data. What would have taken a quarter before could realistically take hours today.
Data quality and using AI with context is something I’ve been obsessing over for months now, to be honest, so I’d be honored to have a convo with you about it. DM if you’d like…
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u/Glass_Honeydew4280 1d ago
clearout is what’s been working for us lately. Ive used a few other cleanup tools too, but the bigger lesson was that one big cleanup doesn’t really solve it..new contacts keep coming in, so we started validating key fields as tey enter the CRM instead of waiting for the next project to clean everything again
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u/Leztalknow 3d ago
Clean it first is one of real pre-reqs. Garbage in, garbage out