There’s a pattern Larry Cobrin, founder and CEO of MSPCFO, sees play out on nearly every discovery call.
An MSP owner gets on the phone and within the first few minutes, volunteers one of two things: either their data is a mess, or their data is in great shape. And almost without fail, the reality is the opposite of whatever they say.
“The people who think their data is bad, generally it’s okay,” Larry explained during a webinar on AI readiness for MSPs hosted by Flexpoint. “And the people who think their data is great don’t understand what they don’t know.”
He calls it the poker player analogy. Strong is weak. Weak is strong.
It’s a pattern worth sitting with — because in the MSP world, overconfidence in your data isn’t just a blind spot. It’s a business risk.
What the Poker Analogy Actually Means
In poker, “strong is weak, weak is strong” refers to how players signal their hand through behavior. Confidence is often a bluff; uncertainty is often closer to the truth. The same dynamic shows up in MSP data conversations. The owner who comes in sheepish — “honestly, our PSA is a mess” — has usually bumped up against the problem enough to have real self-awareness. Their data tends to be more workable than they fear.
The owner who opens with “our data is solid, we’ve got everything tracked” often hasn’t stress-tested that belief. Reports generate, invoices go out, no one is complaining — but confidence built on the absence of scrutiny isn’t the same as data that can actually hold up to AI, a financial model, or a potential acquirer.
Why the Confident Ones Are Often the Most at Risk
Before any MSP is asked to make a purchasing decision, we conduct a free data health check and report readiness analysis. No commitment, no pressure — just an honest look at where your data actually stands, so you can make an informed decision about what comes next.
When MSPCFO digs in — really digs in — the gaps tend to surface fast. Agreements set up incorrectly in the PSA. Licensing costs not reconciling to what’s being billed. Revenue and service categories that don’t reflect how the business actually operates. Billable time logged in ways that make agreement profitability impossible to calculate accurately.
None of this shows up as an error message. It just quietly skews every report, every benchmark, and every business decision that flows from the data.
The MSP owner who knows their data is messy? They tend to be more accurate about it than they think, and closer to fixable than they realize. The one who’s certain everything is fine? That’s often where the real work begins.
“There Are Absolute Wrong Answers”
There’s rarely a single right way to structure your data stack. PSA setups vary. Workflows differ. What works for one MSP may not translate to another.
But that flexibility has a hard floor.
“I will say there are absolute wrong answers,” Larry said. “You shouldn’t have AI playing with this data yet. You should fix your processes and then hand it over to AI.”
This matters more than ever right now, because MSPs are under real pressure to start leveraging AI — for service delivery efficiency, for financial reporting, for forecasting. The tools are getting better fast, and the fear of falling behind is legitimate.
But AI doesn’t fix bad data. It accelerates it. Feed a model inconsistent billing records or misconfigured agreement data, and it won’t flag the problem — it will pattern-match against the mess and give you a confident, well-formatted, completely wrong answer.
As Victor Lopez, co-host of the webinar and CEO of FlexPoint, put it: “AI is incredibly powerful at doing the wrong things faster.”
The “No Judgment” Starting Point
So what do you do if you suspect — or know — that your data isn’t where it needs to be?
Larry’s advice is simple: start with an honest look, not a defensive one.
“When we talk to new people, the first thing we say on every call is that these are no judgment phone calls,” he said. “Nobody’s going to get fingers pointed at them. The goal is to fix the data.”
That posture matters. Data problems in MSP businesses almost always stem from how systems were set up — often years ago, often by someone who has since left, often before the business grew into the complexity it has today. The goal isn’t to assign blame. It’s to understand what you’re actually working with.
Larry’s concrete recommendation: get a third-party set of eyes on it before you do anything else. “Have someone with a lowercase ‘a’ audit your data — someone who understands what you’re going to do with the data, and can tell you if it’s in a place where what you want done is actually possible.”
That’s exactly where our free data health check comes in. You’ll come away knowing whether your data is ready — and if it isn’t, what it would take to get there.
Know Before You Build
The MSPs who will get the most out of AI — and out of the financial analytics that can actually move their business — are the ones who do the unglamorous work first. Clean customer records in the PSA. Correctly configured agreements. Revenue categories that reflect reality. A named owner of data integrity who’s accountable for keeping it that way.
None of that is exciting. But it’s what makes everything else possible.
The poker analogy is useful here too. The best players aren’t the ones who think they have a great hand. They’re the ones who know exactly what they’re holding.
About MSPCFO
Winner of both the Partner Innovation and Partner Advocate awards at IT Nation Evolve 2022, MSPCFO is a business intelligence platform designed to solve the unique profitability and productivity challenges managed services providers face. Since the application’s introduction in 2014, MSPCFO has helped thousands of MSPs and TSPs in the United States, Canada, APAC, and Europe identify improvement targets that directly boost their bottom line. Founder and CEO Larry Cobrin’s consulting, investment banking, private equity and product management experience coalesced in his development of the MSPCFO software and business model.
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