Executive Summary
Your portfolio company is preparing for an exit, but the data room is a collection of conflicting spreadsheets and dashboards. This can make potential buyers a bit nervous.
A disorganised data room can lead to a lower valuation, a longer due diligence process, and in some cases, a failed exit. Every question from a potential buyer that takes a long time to answer chips away at their confidence.
The answer isn't a frantic, last-minute clean-up. It's about building a solid, permanent data structure that creates a Single Source of Truth. This turns due diligence into a straightforward check, not an emergency.
Why the data approach that got you here can become a problem
You've hit your growth targets, the private equity firm is happy, and there's talk of an exit.
Suddenly, the collection of Google Sheets, Looker dashboards, and departmental reports that has served you well enough until now starts to look like a bit of a problem. The ARR in the sales CRM doesn't quite match the finance ledger. The product team's churn calculation is different from marketing's. This isn't just a small niggle. It's the point where the manual effort that holds your reporting together starts to come unstuck.
You've probably moved to the cloud and hired some very capable engineers. The trouble is, you may have just moved the existing tangle of data into a faster environment. Automating a messy process just means you get the wrong answers more quickly. The issue isn't a lack of effort from the team. It's simply that the systems that work perfectly well for an early-stage company often aren't robust enough for the detailed inspection that comes with a later-stage exit.
How inconsistent data can affect your company's valuation
A common mistake I see is confusing the reporting itself with the structure that produces it. A dashboard is just the final output. If the process that creates the numbers is flawed, the dashboard will be, too. This happens quite a lot in fast-growing companies: there's often a focus on the visualisation tools, but not quite enough attention paid to the underlying logic that feeds them.
This can lead to a situation where the data warehouse is full, but nobody really trusts the metrics. This is where the pressure of an audit really starts to build. Potential buyers don't just want to see your revenue figures. They want to understand exactly how they're calculated. If it takes your team three days to answer a simple question about historical cohort profitability, it suggests that things might not be running as smoothly as they could be, and that erodes confidence.
I worked with a company recently where the management pack for the board took several days to put together by hand each month. We didn't just build them a new dashboard. We rebuilt the process behind it. By bringing their separate departmental reports into a single, automated system, we turned Investor Reporting into something that just runs, rather than a last-minute effort.
Moving from reactive reporting to a reliable data structure
The fix for this isn't to hire more analysts to check spreadsheets more quickly. It's about making a conscious decision to move from a reactive approach to one where data is treated as a reliable product. This requires a change to the underlying structure, not just the way things look.
The internal challenges of getting your data in order
To be clear, putting this sort of thing in place is often as much about people as it is about technology. It means getting the Head of Sales and the CFO to sit down and agree on a single definition of ARR, and then setting that in stone. It means taking those useful spreadsheets the operations team relies on and building their logic into the main, shared system.
You might get some pushback, because you're asking people to trade a bit of departmental autonomy for consistency across the whole company. It's a trade-off. You might find things slow down for a quarter, but the idea is to move much more smoothly for the next few years and, importantly, to get through the Due Diligence process without any major headaches.
What a smooth due diligence process looks like
Imagine a due diligence process where any question from an investor can be answered in minutes, not days. Imagine presenting a data room where every number can be traced back to a single, agreed-upon definition. This isn't wishful thinking. It's what happens when you treat your data infrastructure with the same care as your financial accounts.
By sorting out the foundations, that unglamorous but essential layer of data logic and governance, you put yourself in a much stronger position to get a better valuation. You're not just selling a company. You're selling a business that is predictable and runs smoothly. And that is a much more valuable thing.