Insights
Data Due Diligence for Acquisitions: Can You Trust the Target's Numbers?
Standard diligence assumes the numbers are real. This is how an acquirer finds out whether they are, before the wire clears.
Before an acquisition, data due diligence evaluates whether reported numbers are trustworthy, traceable, and transferable. It checks if revenue and margin can be traced to their source, whether key metrics are consistently defined, the degree of key-person dependency, access governance, and the actual cost of post-close data integration. Financial diligence assumes the numbers are accurate; data diligence confirms this.
Most diligence checklists treat data as an IT issue, but it is fundamental to valuation. The reliability of reported numbers depends on underlying systems, which are not visible in a data room. You see the output, but rarely whether it can be reproduced.
I have been on the other side of this table. I built a data services company, grew it onto the Inc. 5000, and sold it in 2021. I know what a seller's reporting looks like from the inside, including the parts that are held together by one person and a spreadsheet, which is precisely what tells you where to push when you are the one buying.
What does data due diligence actually check?
Do the numbers trace to source?
Can the target's reported revenue, margin, and growth be reproduced from the systems underneath them, or do they live in a model nobody can reconstruct? A number you cannot trace is a number you are taking on faith, and you are about to pay a multiple on it.
Are the definitions consistent?
If sales, finance, and operations define expressions such as "revenue" or "active customer" differently, headline figures may conflict. Consistent definitions indicate solid management, while inconsistencies indicate that the reported numbers may not reflect actual business performance.
How much rests on one person?
If critical reporting relies on one analyst or an unversioned spreadsheet, you are acquiring individual knowledge rather than a sustainable system. This creates a risk of losing value if key personnel depart after closing.
Is access governed?
Who has access to data, and how is it controlled? Weak governance is an immediate risk for the acquirer and is typically less costly to address before closing than during a post-close audit.
What will integration actually cost?
Integrating a target's data into your environment is often where optimistic deal models are tested. Poorly structured data can turn a straightforward integration into months of unplanned work. Knowing these costs before closing is key for reliable modeling.
Key takeaways
- Financial diligence verifies that reported numbers are internally consistent. Data diligence verifies these numbers can be reproduced from underlying systems.
- Key areas to examine include traceability to source, consistency of definitions, key-person dependency, governance, and integration cost.
- Messy data is rarely a dealbreaker, but it affects both the nature of the acquisition and the appropriate valuation.
- The difference between a reported figure and one that can be traced to its source is where acquisition risks often arise.
Financial diligence assumes the numbers are real. Data diligence is how you find out.
When should a PE firm or acquirer run data diligence?
Data diligence should be conducted early enough to guide decision-making. Performing it before or alongside confirmatory diligence reveals underlying assumptions in the financial model, permitting adjustments to price, terms, or integration plans. Delaying this process may result in post-close surprises rather than informed negotiations.
I run it as a fixed-fee engagement scoped to your deal timeline, not an open-ended consulting project. You get a clear read on where the numbers trace cleanly, where they will not survive scrutiny, and what integration will really cost. The combination that makes this useful is specific: data architecture judgment from someone who has also run and sold a company, so the read covers both the systems and what they mean for the deal.
Before you sign
Considering an acquisition with numbers you can't fully trace?
A fixed-fee data diligence read, on your deal timeline, on whether the target's numbers hold and what integration will actually cost.
Talk through a target →Frequently asked questions
What is data due diligence in an acquisition?
It is a focused read on whether a target company's reported numbers can be trusted, traced, and transferred. It checks whether revenue and margin trace to source, whether key metrics are defined consistently, how much of the reporting depends on one or two people, how access is governed, and what it will cost to integrate the target's data after close. Financial diligence assumes the numbers are real. Data diligence is how you confirm it.
How is data due diligence different from technical due diligence?
Technical diligence looks at the software, the stack, and the engineering team. Data diligence looks at whether the information those systems produce is trustworthy enough to base a valuation on. A target can have clean code and still report numbers that do not trace to source. I focus on the second question, because that is the one the financial model quietly depends on and rarely tests.
We are a PE firm evaluating a target with messy data. Is that a dealbreaker?
Typically, messy data is not a dealbreaker, but it does affect both the acquisition and valuation. Messy data is common in mid-market companies and is often correctable. The purpose of diligence is to determine before closing whether the numbers are reliable, the cost of remediation, and the extent of key-person risk. I provide this assessment as a fixed-fee engagement within your deal timeline, enabling you to price risk appropriately.
How quickly can data due diligence be completed within a deal timeline?
The process is designed to fit within active deal timelines. I offer data diligence as a fixed-fee engagement, not an open-ended project. The objective is to deliver actionable information on data traceability, possible issues, and integration costs so you can make well-informed decisions.
Can't our financial diligence team just check the data?
Financial diligence teams verify that numbers are internally consistent, but they seldom assess whether these figures can be reproduced from underlying systems. This requires data architecture expertise. The gap between reported figures and those that can be traced to source is where acquisition risks arise, and my role is to address this gap.