An investor doesn't assess your results. They assess your ability to prove them
Most Founders typically approach exit preparation from the outcomes perspective: growing revenue, improving margin, and healthy EBITDA figures. This is essential, though incomplete.
During due diligence, investors pose a fundamentally different inquiry than "what are your earnings?" Their actual question is: "how reliably can we depend on this figure?" An outcome that can't be rapidly validated becomes, for a potential buyer, a contingent result. Such contingent results receive more conservative pricing.
This explains why two firms with matching EBITDA could receive divergent offers. The distinction stems not from the actual numbers themselves, but from the level of assurance available in defending those numbers.
What Data Debt is and why it works like a tax
Data Debt stems from choices such as "we'll handle this temporarily in Excel," "we'll figure that calculation out manually," or "only one individual understands that report." Each choice seems reasonable during periods of rapid growth. Collectively, they generate an obligation that demands repayment eventually, with additional costs.
Technical debt is apparent within the engineering team. Data Debt typically goes unnoticed by leadership, since the reports function adequately. It emerges only when an external party requests something the company has never encountered: reconstructing a number from 18 months ago, breaking down margin by customer, or showing where an adjustment came from.
This operates as a tax because repayment timing is not your choice. You pay when the investor demands it.
Why "the reports add up" is not enough
Do matching reports indicate healthy data? No. Matching reports mean that someone made them consistent. That says nothing about how they did it, how long it took, or whether they will do it the same way next month.
In companies with Data Debt, "consistency" is often produced by hand. An analyst or controller combines several files, fixes discrepancies, and sends out the final version. Management sees a coherent result. The investor sees a process that depends on one person, has no documentation, and can't be repeated on demand.
From the buyer's perspective, that is not a report. It is a one-off event, and one-off events are hard to value.
Five questions an investor will ask your data
The scope of verification differs between transactions, but a certain pattern repeats in most processes. The investor, their advisors, or auditors look for answers to these questions:
- Where does this number come from? Can it be traced from the management report down to a single transaction, without guessing and without "let's ask someone."
- Can it be reproduced? Would the same method, applied to the same source data, give the same result for a period a year back.
- Does the company have a single source of truth? Single Source of Truth means one place where the authoritative version of every key number is recorded. When sales, finance, and marketing calculate the same metric differently, the investor asks the obvious question: which version is true.
- Whom does knowledge of the data depend on? If key reports are maintained by one person, their departure is a transaction risk, not just an HR issue.
- Does the company have the right to use this data? Customer data, consents, access, and security. Gaps here can end up as warranties in the agreement and, in extreme cases, a change to the transaction structure.
None of these questions requires technical knowledge to understand. Each requires order in the data to answer quickly.
How Data Debt turns into money
The mechanism is simple and worth seeing in numbers. The example below is hypothetical and serves only to illustrate scale.
Assume a company with EBITDA of EUR 3 million, valued at a 6x multiple, so about EUR 18 million. During due diligence, the investor concludes that part of the result is hard to verify. They don't challenge it directly, but they adjust the expected multiple down by half a point, to 5.5x. The difference is EUR 1.5 million. Alternatively, part of the price is shifted into a contingent mechanism, meaning a payment conditional on later confirmation of the results.
This is not an extreme scenario. Such adjustments don't have to result from errors in the data. It is enough that the data can't be defended quickly.
This is where the concept of EBITDA Leak comes in: a value leak that arises when the cost of poor data quality (manual work, errors, delayed decisions) lowers the result, and then lowers its valuation as well. The company loses twice: in operations and in the transaction.
Why you pay at the worst possible moment
Data Debt is especially costly in due diligence for three reasons.
First, the information asymmetry reverses. Until now, the Founder knew more about the company than anyone. In due diligence, the investor gets access to raw materials and examines them with a team of advisors. Gaps that management didn't know about come to light on the buyer's side.
Second, time works against the seller. Every week in which the team reconstructs data at the investor's request is a week in which the deal loses momentum. A prolonged process increases the risk that the buyer changes their assessment or withdraws.
Third, exclusivity limits alternatives. After signing a letter of intent, the seller usually doesn't negotiate in parallel with others. A problem discovered at this stage has to be solved with one partner who knows the other side has no way out.
That is why data debt is cheapest to repay before the process, and most expensive during it.
Can you clean up your data before the sale process begins?
Yes, and the earlier, the cheaper. The window of 12 to 24 months before an exit is a good time, because it allows you to organize data gradually, without firefighting, and to build a history of results calculated by a single method.
What matters is not only the end state, but also continuity. An investor is more willing to trust numbers that have been calculated the same way and documented for several periods than perfect order introduced a month before the process. Fresh order raises the question: why only now?
What to do differently: four decisions 12 to 24 months before an exit
Preparing data for a transaction is not a technology project, but a series of management decisions. Four matter most.
1. Establish one authoritative version of the key numbers. Revenue, margin, customer acquisition cost, retention. For each, set one definition and one place where it is recorded. This is the practical implementation of Single Source of Truth.
2. Replace people-dependent processes with repeatable ones. If a key report is created manually, it is vulnerable to error and to the departure of its author. An automated data flow (data moves from source systems to the report without manual re-entry) turns it from one person's skill into a component of the company.
3. Ensure the history is reproducible. Check whether you can reconstruct key metrics for the last 24 to 36 months using the same method you use today. If you can't, you know where the debt lies.
4. Run your own data due diligence before the investor does. Ask your company the five questions from the earlier section. Write down which ones take days to answer, not minutes. Those places are your data debt register and your priority list.
What this means for the board
For a Founder preparing to sell, the conclusion is practical. Data quality is an element of valuation, not an addition to it. An investor doesn't need technical details to judge whether a company has control over its numbers. They only need to ask a few questions and measure the time it takes to answer.
Companies that go through this stage smoothly rarely have "perfect" data. They have data that is understandable, documented, and repeatable. That is enough to turn due diligence from an interrogation into a formality.
Data Debt doesn't lower valuation because the numbers are bad. It lowers it because no one can quickly prove they are good.