Most companies assume their CRM data is fine. The dashboard loads, the reports run and the integrations show no major sync errors. And yet, somewhere upstream, data is drifting from reality, contradicting itself, or never entering the system at all — and nobody knows.

With more than a quarter of organizations estimating they lose over $5 million annually because of poor data quality, this is not a niche issue or edge case few need to worry about.

No amount of “good data hygiene” addresses this problem. It’s a data trust problem. And they’re not the same thing.

Data hygiene — deduplicating contacts, filling in missing fields, standardizing formats — is necessary, but it only addresses what’s already in your system. It can’t tell you what your integrations silently dropped, whether your CRM and ERP actually agree, or how much of your pipeline lives in a sales rep’s spreadsheet instead of HubSpot.

Real data trust requires an extra layer: independent verification that checks your data from the outside, surfaces discrepancies before they become decisions, and gives you actual proof that the numbers are right — not just a dashboard that looks right.

What does that look like in practice? Below is our checklist we follow to assess data trust.

10-Point Data Trust Checklist

  1. All relevant data is captured — nothing missing or dropped
  2. Data is entered correctly — right fields, no errors
  3. Data is ingested correctly from integrations or imports
  4. No process corrupts, overrides, or duplicates the data
  5. External systems agree with the data
  6. The correct data is pulled into reports
  7. Data is assembled and calculated without logic errors
  8. AI classifications are correct
  9. Final analysis and AI enrichment are reasonable and consistent
  10. You can independently spot-check and verify the results

In this episode of Machine Logic, Charlie and Jill walk through the most common ways CRM data fails behind the scenes, why the system itself can’t tell you when it’s happening, and what it actually takes to trust your data at scale.

Full Transcript

Hey, what’s up? Jill and Charlie from Simple Machines here, back with another episode of Machine Logic. And today we are talking about data trust. Charlie, what does that mean?

Yeah, so I think most people, when they think data trust, they’re kind of working within a system and they’re checking the basics, either natively in the CRM like HubSpot or at the native integrations. But really, most instances, what we see when data starts to drift, there is no alert. There’s no red flag. There’s nothing that says, hey, your data is wrong — because really, your dashboard looks fine. Your reports seem to be working. Integration sync health looks fine at first glance and your pipeline’s got numbers. The fact is that doesn’t mean you can trust it.

An unknown and unspoken discomfort. It should be discomforting.

I mean, when we think about how much companies are pushing AI right now — massive investments, using data to push marketing, sales, service at scale — if that foundation isn’t sound, then you’re not erasing that problem. You’re magnifying it.

For sure. Yeah. As glamorous and exciting as leveraging AI and leaning into more AI is, the start of that conversation is often a boring data governance and data hygiene conversation. And that is not something that a lot of organizations have.

No. And I think we’ve got to move past the fundamental basic data hygiene conversation — which is yes, it’s important, like how many contacts are missing emails or how many duplicates do you have — but if we’re going to take this seriously, there has to be independent verification. A way to think about this: just because your company has accounting software doesn’t mean you won’t still get audited. The audit is an acknowledgment something could be wrong. It’s a way to provide a proof layer that you can trust it and that it’s right. The same thing applies here. If you really want to use data at scale and you’re going to put that much investment in it, you better be sure it’s right. It’s essentially the validation layer that checks the data in the system from the outside.

Yeah. So simple machines’ data trust does go beyond that — it provides that layer. And I think what’s helpful, maybe we can cover today, is how does data trust start to fail? Because it is usually silently, and oftentimes companies don’t know what’s happening.

Right, yeah, let’s get into it.

Yeah, there are definitely some ways and some places that are leaky buckets — silent data loss, data erosion — that happens not just in HubSpot, but in the connective tissue and the systems it’s integrated with. So yeah, I think we’ve got a few things to talk about that will probably ring true for a lot of listeners and watchers here.

So let’s start with one of the biggest ones, which is integrations. Most of us are using multiple tools and platforms along with our CRM. And this is a big one where, whether you’re connecting with a native app or middleware, it looks like it’s working. There’s no red flags saying this is broken. But I think most of us have felt that — you’re looking at it and it’s like, I think I had more meetings than HubSpot is saying I had last month. Something’s up, but it’s not obvious what’s going on.

Sure. So people assume it’s working. It’s still losing data. Nothing’s creating or logging the activity in HubSpot or the CRM. No one notices because nobody’s cross-referencing. They’re just assuming all of these data syncs are working.

How would you even know this is happening?

Yeah, you probably wouldn’t unless you decided to go in and manually compare the two systems. One example: a lot of companies use Calendly because they like certain features. That integration with HubSpot is fine and for all intents and purposes it’s working — but if your Calendly numbers start to drift from your HubSpot ones, HubSpot’s not going to tell you that. It’s just going to show the report, it’s going to look right. So the way to do this — and this is part of the layer we’re talking about — is to actually query both systems and compare the output in an automated way. Pull the records, pull the date range, the count, all the stuff that makes sense. And then it’s telling you either you’re good, these match, no discrepancies — or here are the discrepancies and you can go troubleshoot exactly what’s going on.

Right on. So calendly, calendar syncing, meetings, making sure those are set, held, kept, logged — probably a good example. What’s another one?

HubSpot is really good at capturing bounced marketing emails. Sales emails, not so much. If you’re sending a one-on-one email or sequences and that contact bounces, HubSpot captures that in many different ways — none of them very conducive to segmentation, reporting, or automation. So that’s another one where we’re using our extra layer to use logic to go and find those, pull them out, compare. And then every week or month we have an update of how many have bounced from sales email, put them into a list, exclude them, suppress, whatever needs to be done.

Cool. So that’s data getting lost in transit. What about when the data actually makes it into both systems, but they’re telling you different things — like your ERP says revenue is $24 million, but your CRM says $21 million. And both have had reports and dashboards built by smart, credible people. What number do you trust?

That is a problem. And the thing is, that tends to be the question. But it’s a bit of a trap — because what we tend to see is that it doesn’t get resolved. People trust the number that they trust. I’m in the ERP more, so I’m going to go with this number. But there’s no real resolution. The disagreement is declared over, and you move on. And that could be a major problem.

So is this a technology problem or a process problem?

It’s mostly process. The tech itself isn’t broken — it’s doing its job. But different tools work differently. They’re built on different tech, use different languages, parameters, filters. Even when they start to disagree, there’s no mechanism to surface a red flag or an alert. And that’s just because you’d have to account for every possible discrepancy, which isn’t manageable. So again, that’s where the validation layer comes in. If we stick with the CRM-ERP example, it’s going to query both. And this isn’t necessarily to say it has to match 100% all the time — but it tells you to what degree it’s mismatched and where the discrepancies are, so you can decide: is this worth remediating, or do we bake it into our numbers going forward?

Got it. So this automated reconciliation is kind of like an automatic referee — it’s not picking a winner, it’s just saying, hey, timeout. This is saying this. This is saying this. Now how does your organization want to deal with it?

Yeah. This team is doing it this way, this team is doing it this way. If you want these to match, now you know how you’d go about resolving them.

Let’s talk about data that never enters the system. Because we all know there are salespeople who — do as I say, not as I do.

Especially the good sales reps. They kind of get a pass. They don’t enter all their stuff. And it’s like, well, it’s fine, they’re solid, right? But that creates a certain problem — if you’re trying to use this as a source of truth to track revenue, pipeline, and forecast, and you don’t know how far off you are, it becomes unusable. If you don’t know if it’s 5% or 20% off, what are you supposed to do with that number?

Why can’t reps just use the damn CRM?

I don’t know. You tell me.

I can see it because it’s slowing them down. I’m working on something hot, I’m trying to push along next steps, and from my perspective this process is clunkier than me just doing it my way. Lack of consequence is another one. I don’t think any organization is going to cut off their nose to spite their face if they’ve got a really productive top seller.

Yeah. And those consequences are real. If you’re making investments based on those numbers — another sales resource, more tools, product — that’s business-changing. And to your point, is the goal that 100% of the time every rep is entering every bit of data? No, that’s just not realistic. However, what this extra layer can provide is: is it even there at all? To what extent is it missing? So that as a sales leader or RevOps, what do I want to do about that, and how do I need to address our numbers to account for it?

I mean, even hearkening back to the ERP-CRM discrepancy — this is saying $24 million, this is saying $21 million. That’s like a 12–15% discrepancy. That’s no cup of coffee.

Yeah. Back to the original point — is this the fun, sexy part of AI? No. But it’s incredibly important when you’re talking about that magnitude of discrepancy.

Got it. So let’s pull this together. We’ve got integrations that drop data silently, systems that contradict each other, and data that never enters the system in the first place. And none of these announce themselves. The system doesn’t tell you it’s broken. So you need this external validation layer — something that checks the system against itself, against the reality you’re seeing.

Yeah. And we won’t rattle them off here, but we’ve put together a 10-point checklist of what we’d consider to be trustworthy data — everything from how it gets captured to AI classification, the full lifecycle of the data. We can link to that in the video, and in future episodes we’ll go more in depth on specific examples.

Cool. If you’re not sure where your data stands, this is definitely the place to start — not with more dashboards and reports, but with more proof and defensibility. Thanks for walking us through it, Charlie.

Thank you.