You don’t fix data problems by starting with tools. You fix them by starting with people.

Let me show you what I mean.

A few weeks ago, we conducted a pro bono data assessment for a small sandwich shop in Vienna, Virginia, at a local spot I’ve been eating at for the past four years. It’s a family-owned business with a loyal customer base, a strong lunch rush, and the best roast beef sandwich in town.

This wasn’t a client engagement. It was a way to give back to a business I genuinely care about. However, what we uncovered in that process aligns directly with what we observe inside much larger companies preparing for an exit.

No single source of truth. Manual reconciliation. Good tools, underused. Data is being collected, but not trusted. And a growing sense that something important is missing, even if it’s hard to name.

The gaps we found and the way we addressed them demonstrate precisely how our approach works, whether you’re serving 400 sandwiches a day or preparing to sell a $400 million business.

Our Framework: People → Process → Platform → Data

Whether we’re helping a founder clean up systems or working with a PE-backed CFO preparing for diligence, this is the approach we follow.

1. People

We started by sitting down with Bennett, the assistant manager, and walking through the business hour by hour. We mapped who does what, how decisions are made, and what actually happens when the lunch rush hits.

We found that many of the most important operational decisions — such as ordering, scheduling, and even margin tracking — resided in one person’s head. That’s not a criticism. That’s just how most high-functioning teams run when they’re busy. But it’s also the first thing that breaks when a business tries to scale.

2. Process

Next, we traced the movement of orders through the system.

In-store and pickup orders are handled through Square, their primary point-of-sale system. But delivery orders are different. Orders from Uber Eats, DoorDash, and Grubhub are all received on separate tablets. Staff then have to manually enter each delivery into Square to print a ticket and get it into the kitchen.

Once the sandwich is made, the order is deleted from Square. This prevents revenue from being double-counted — since payouts from delivery platforms are separate — but it also deletes the only system-wide record of what was sold.

So even though the sandwich was made, paid for, and served, there’s no trace of it in their primary data set.

What that creates is a system where delivery orders, which make up a large portion of revenue, are totally siloed. The business can’t analyze product performance or customer behavior across all channels. And the reconciliation effort gets pushed downstream.

That problem isn’t unique to small shops. We observe the same structure within companies generating $50M or more in revenue. Different departments using different systems, trying to tell the same story with different numbers.

3. Platform

The tech itself wasn’t the problem. The shop utilizes Square, which offers built-in loyalty features, time tracking, online ordering, and email marketing capabilities.

But those features weren’t being fully used. Not because they weren’t needed, but because the team didn’t have the capacity to turn them on. When you’re running a fast-paced operation with a small staff, data falls to the bottom of the list.

This is something we see repeatedly, even within companies with full-time finance teams and expensive platforms. The software is there. However, the insights never reach the executive level because no one owns the handoffs between systems.

4. Data

Finally, we examined what data was actually available and how it was being used.

• Sales data was split between Square and third-party apps

• Loyalty activity existed, but wasn’t informing marketing

• Menu performance couldn’t be analyzed across order types

• No one had a clear, consistent view of what was driving margin

The data wasn’t missing. It was just trapped. And without structure, it couldn’t be trusted.

Why This Matters for You

If you’re working with a portfolio company approaching exit, these patterns should sound familiar.

• Multiple systems

• Conflicting metrics

• Inconsistent data definitions

• Revenue streams that can’t be compared in a single view

This wasn’t a diagnostic of a tech stack. It was a diagnostic of how decisions get made when things are busy, and the systems haven’t caught up.

We fixed the gaps using tools the team already had. No new platforms. No new headcount. Just clearer thinking, better workflows, and a reporting structure that reflects reality.

Apply that to a $75M company, and it’s the same story. Same friction. Same solutions. Bigger stakes.

From Sandwiches to Strategic Buyers

This wasn’t a client project. It was a local favor for a business I’ve supported for years. However, it was also an opportunity to demonstrate how our approach works in the real world.

We didn’t start with dashboards. We started with people. We mapped the work. We documented the friction. We followed the process. Then we cleaned up the data.

That’s the same approach we use when we help PE firms prepare a portfolio company for exit. Whether you’re selling sandwiches or software, your buyer will ask for proof. Our job is to make sure your systems can deliver it.

If we can do that for a shop with two tablets, a register, and a laminated inventory list, we can do it for your next exit. PS...If you're enjoying Transformed With Data, please consider referring this edition to a friend. They'll thank you for helping them avoid expensive data mistakes.

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