# The hidden data risks in mid-market M&A deals Data risks in mid-market M&A are routinely missed in financial due diligence. Here's where they hide and how to find them before close. Published: 2025-03-03 Author: Rodan Analytics Most M&A due diligence processes are built to catch the problems that look like problems. Revenue concentration, customer churn, working capital cycles, legal exposure. The checklist is long and well-rehearsed. What the checklist misses is data. Not data in the abstract sense — everyone knows data matters. The miss is specific: the quality, integrity and governance of the data underpinning the commercial story a target is telling you. By the time a deal closes, most operating partners have read hundreds of pages of information memoranda built on reporting nobody has stress-tested. The cost of that oversight is not theoretical. It shows up in value creation plans that stall in year one, integrations that take twice as long as modelled and EBITDA bridges that quietly collapse when the acquirer tries to run the business on the data they inherited. This article sets out where the real data risk lives in mid-market transactions, how to find it before you sign and what to do with what you find. --- ## The commercial story is only as good as the data behind it Every IM tells a story. Recurring revenue, improving unit economics, growing cohorts. The narrative is always coherent because it has been constructed that way. The question is not whether the story is false. Most of the time it is not. The question is whether the underlying data infrastructure could produce a different story if you asked different questions. A useful illustration: a business services company in the professional services sector reports 92% revenue retention. Impressive. But when you examine how retention is calculated, you find it measures revenue volume, not customer count. Three of the five largest clients have been consolidating spend as a relationship management exercise. The underlying cohort is eroding. The number is technically accurate and commercially misleading. This is not fraud. It is the natural result of a finance function that built its reporting to tell the board what the board wanted to see, rather than to surface commercial reality. Due diligence rarely catches this because financial DD focuses on whether the numbers reconcile, not whether the definitions are sound. You need someone to go upstream — into the CRM, the billing system, the data warehouse if one exists — and ask whether the metric means what you think it means. --- ## Where data debt accumulates in mid-market businesses Mid-market companies between roughly £50m and £500m revenue occupy an awkward position in data maturity. They have outgrown the spreadsheet era — mostly — but they have not invested in the infrastructure that enterprise businesses take for granted. What you typically find is a patchwork. A CRM that the sales team uses inconsistently. A finance system that is the system of record for revenue but cannot join to anything else. An operational database built by a developer who left three years ago. A data warehouse project that was started, paused and never finished. This data debt is not always visible in normal DD. It becomes visible when you try to build the first post-close management information pack and discover that nobody can produce a reliable weekly P&L without two days of manual reconciliation. The specific risks to look for fall into three categories: - **Single points of failure** — one person in the finance or analytics team who holds the institutional knowledge for how the data actually works. They leave, and reporting breaks. - **Undocumented transformation logic** — numbers that are right but only because of a formula in a spreadsheet that nobody can explain. - **Systems that cannot talk to each other** — common after bolt-on acquisitions, where the acquired business was never fully integrated at the data layer. A consumer goods business acquired through a buy-and-build strategy is a good example. Three portfolio companies, three ERP systems, one consolidated P&L produced manually each month. The acquirer's value creation plan assumed 18 months to integrate. The actual timeline was closer to 36, because the data layer had never been mapped during diligence. --- ## The governance question nobody asks Data governance is the least glamorous topic in any diligence process. It is also the one most likely to create post-close liability. Mid-market businesses frequently have material gaps between what their privacy policies say and how customer data is actually handled. GDPR compliance documentation may exist, but the underlying practices — data retention, consent management, third-party sharing — are often years behind. In sectors like financial services, healthcare adjacent markets or any business that has expanded into European markets, this is not a theoretical risk. Beyond regulatory exposure, governance gaps have direct commercial consequences. If a target is running customer data through third-party tools without adequate data processing agreements, that relationship may be at risk the moment an acquirer's legal team examines it. If marketing automation relies on a consent database that is improperly maintained, the audience you are buying may not be usable in the way the growth plan assumes. The diligence question here is not "are you GDPR compliant?" — every management team will say yes. The question is: show me how consent is captured, stored and audited. Show me the data flows between your CRM and your third-party tools. Show me the last time you ran a data retention audit. Most management teams at this scale cannot answer those questions quickly. That is useful information. --- ## How to structure a data risk assessment in diligence The goal is not to produce a forensic data audit — that is neither practical nor proportionate at deal speed. The goal is to triage material risk, calibrate your assumptions and identify the integration investments the deal model needs to reflect. A working framework in four stages: - **Baseline the reporting infrastructure.** What systems produce the numbers in the IM? Who owns each system? How are they connected? Ask for a data flow diagram. If one does not exist, that tells you something. - **Test two or three key metrics from first principles.** Pick the metrics the valuation depends on most — typically revenue retention, customer count or margin by product line — and ask the finance team to walk you through the calculation from raw data to reported number. Look for manual steps, assumed figures and unexplained adjustments. - **Assess single points of failure.** Identify the individuals whose departure would meaningfully impair reporting capability. Include this in your retention and key person risk analysis. - **Run a lightweight governance review.** Map customer data flows, review the consent framework, check for active data processing agreements with all material third parties. For regulated sectors, commission a proportionate compliance review. For PE clients running multiple processes simultaneously, Rodan's diagnostic engagements can execute stages two through four within the standard diligence window — producing a data risk scorecard that feeds directly into the deal model and the 100-day plan. --- ## What to do with the findings Finding data risk is not a reason to kill a deal. It is information. The question is whether the risk is priced, mitigated or accepted. Some data risks are cheap to fix post-close — a documented data dictionary, a modest investment in business intelligence tooling, a governance policy update. Others are structural: a business that has never separated its operational and reporting data is going to require meaningful investment to run properly at scale. The error to avoid is discounting these costs as operational noise. A management team that cannot produce reliable data within six weeks of close is a management team that cannot execute on the value creation plan. The 100-day plan becomes a three-year plan. The exit multiple you modelled assumes a business you cannot actually build on the data you inherited. Build the remediation cost into the deal model. Sequence the data infrastructure work into the 100-day plan. Do not assume it will resolve itself. --- ## The cost of finding out after close Data risk sits in the gap between what financial due diligence covers and what operational reality looks like on day one. Most mid-market deals close without anyone having looked directly at it. The businesses that get this right treat data diligence as a commercial question, not a technical one. The question is not whether the systems are modern. The question is whether you can trust the numbers you are buying and build on the data you are inheriting. If you are running a process now or preparing for one in the next two quarters, the time to answer that question is before heads of terms are signed — not after. Rodan offers a structured data risk diagnostic designed for PE diligence timelines. It produces a clear assessment of data quality, governance exposure and integration cost that slots directly into your deal process. Speak to our team to scope what is relevant for your current transaction. --- **Meta description:** Data risks in mid-market M&A are routinely missed in financial due diligence. Here's where they hide and how to find them before close. 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