Due Diligence: The Data Contradiction Killing Your Round

Executive Summary

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When the numbers in the data room don't match the pitch deck

It's a familiar moment for many. You're on a call with a potential lead investor. They liked the pitch deck, with its impressive growth curve and strong retention figures. Now they are looking through the data room, and the detailed questions begin.

“Can you explain why the ARR figure in this dashboard is 8% lower than the one in your financial model?”

“Your deck says churn is 1.5%, but this product analytics export suggests it’s closer to 2.1%. Which is it?”

That quiet pause on the line can feel uncomfortable. Your team promises to look into it and come back with an answer. It creates a delay and introduces a note of doubt. The problem isn't that the business is performing badly, it's that the data isn't telling a clear, consistent story.

The cause: a common side-effect of growing quickly

To be frank, this is a very common situation. You've probably done all the right things: moved to the cloud, hired good engineers, and adopted tools like Snowflake, dbt, and Looker. The issue is that without a solid foundation, these tools can sometimes just help you produce inconsistent data more quickly.

This isn't about blaming individuals. It's a systems problem that often arises when a company has, quite sensibly, prioritised speed during its early growth. The root of it isn't the dashboarding tool. It's usually that the core business logic, the actual calculations, are scattered across various SQL scripts, spreadsheets, and dbt models. The definition of an 'active user', for instance, can end up differing from one team to the next.

In my experience, this is typical for companies raising at Series B to D. The finance team has its number, the product team has another, and the board might see a third. This sort of inconsistency points to a data structure that hasn't kept pace with the business. Without a clear Metric Definition process, the story you're telling is on shaky ground. To get through due diligence cleanly, you need a single, reliable Single Source of Truth.

How to build a single, reliable source for your data

The way to fix this isn't about working longer hours, but about changing the approach. It's not about creating more dashboards, but fewer, more reliable ones. The solution is to treat your reporting with the same discipline you'd apply to a production system.

Define your logic in one place: The first step is to stop the sprawl. We define all the core SaaS Metrics in a single, controlled place, like a semantic layer in your business intelligence tool. The calculation for ARR, LTV, or churn is written down once, agreed upon by everyone, and version-controlled. This ensures every report and every chart is based on the same, correct calculation.

Introduce some light governance: This doesn't mean creating lots of bureaucracy. Think of good Data Governance as a bit of insurance against awkward questions in a board meeting. We can put in place simple, automated checks so that a metric definition can't be changed without a proper review. It helps turn your data from a set of opinions into a reliable record.

Curate your reports, don't just create more: It's worth doing a thorough review of all existing reports. With one company I worked with, we managed to reduce their collection of over 200 dashboards down to about 30 that really mattered. This helps everyone focus on the same core numbers and removes the noise that can cause confusion during Due Diligence.

Getting agreement on definitions can be a challenge

To be realistic, creating a single source of truth isn't always straightforward. It often involves getting the Head of Sales, the CFO, and the Head of Product to sit down together and agree on one, single definition for something as fundamental as a 'customer'.

You are, in effect, asking people to give up the spreadsheets and local dashboards they might use to tell their own team's story. It's natural to expect a bit of resistance. The process can feel as much about people as it is about technology. The aim is to get everyone to use and trust the same set of numbers, even on days when those numbers aren't telling the story they'd like.

The outcome: answering investor questions with confidence

Once this foundation is in place, the difference is noticeable. Investor questions can be answered in minutes, not days. Follow-up requests are often as simple as sharing a link to a dashboard, with the confidence that the logic behind it is sound and well-documented.

You shift from scrambling to find answers to being able to provide them calmly. Your data room stops being a potential weakness and instead becomes a tool that demonstrates your operational grip, helping to support your valuation. It means you can provide clear, consistent Investor Reporting that helps the funding round move forward smoothly.