# What operating partners should ask about data before signing an LOI What operating partners should ask about data before signing an LOI — a practical framework for assessing data maturity and execution risk in PE due diligence. Published: 2025-05-05 Author: Rodan Analytics Most operating partners walk into a deal with a clear view on EBITDA, management quality and market position. They have a thesis. They have a model. What they rarely have is a clear view on whether the business can actually generate the data-driven performance improvements that thesis depends on. That gap is where value creation plans fall apart. The problem is not that private equity firms ignore data in due diligence. They do not. They ask for it constantly — revenue by segment, churn rates, cohort analysis, customer lifetime value. The problem is they ask for *outputs* without interrogating the *infrastructure* that produced them. A clean data room does not mean a clean data architecture. It means someone worked hard to prepare a document. By the time you are twelve months post-close and your portfolio company cannot run a reliable demand forecast or segment its customer base for a marketing push, it is too late to reprice the deal. This article gives operating partners a practical framework for assessing data maturity before signing an LOI — and explains what the answers actually tell you about execution risk. --- ## The difference between data and data capability A business can have ten years of transaction data and still be analytically useless. The data exists. The capability to use it does not. When you ask a target company for customer retention metrics and they send you a spreadsheet built by the FD's assistant, you are looking at a symptom. The underlying condition is that no one in the business owns data as a strategic asset. There is no pipeline. There is no single source of truth. Every number you receive has been manually assembled for the purpose of answering your specific question — and it will never be assembled the same way twice. This matters for value creation because almost every operational improvement playbook — pricing optimisation, customer segmentation, churn reduction, cross-sell — requires repeatable, trustworthy data flows. If the business cannot generate those flows, you are not buying an analytics capability. You are buying the raw material and the construction bill. Ask these questions before LOI: - Who owns data in the business — is there a dedicated function, or does it sit across finance, ops and marketing with no single owner? - What is the core transaction system, and how long has it been in place? - Have any third-party analytics tools been integrated, and are they actively used or shelfware? - Can the business reproduce any of the metrics in the data room from first principles, on demand? That last question is the most revealing. Ask the CFO to walk you through how a specific metric — say, 12-month rolling customer retention — is calculated and from which system. Watch the process. If it takes three people and two days, that is your answer. --- ## Where data problems hide in mid-market businesses Mid-market companies in the £500m–£1.5bn revenue range tend to share a common data profile. They have grown faster than their systems. They have typically patched together a core ERP with a CRM, a separate ecommerce platform, a finance system and half a dozen reporting tools that do not talk to each other cleanly. This is not a failure of ambition. It is a consequence of organic growth. But it creates specific risks that operating partners need to price. Consider a B2B distribution business acquired on a thesis of pricing power. The investment case depends on identifying which product lines and customer segments have the most inelastic demand and repricing accordingly. That is a sensible thesis. But if the business runs on an ERP that stores list prices but not actual transaction prices — with discounts applied manually by the sales team and recorded only in email threads — you cannot run the analysis. The data does not exist in usable form. The remediation cost is not trivial. Building a clean pricing data model, integrating it with the ERP, training the sales team and establishing governance typically takes six to nine months and costs meaningful six figures. None of that appeared in the deal model. The sectors where this pattern appears most often: distribution and logistics, industrial services, professional services, and any business that has grown through acquisition and never consolidated its systems. --- ## A pre-LOI data due diligence checklist You do not need a full technical audit before LOI. You need enough signal to identify red flags, size the remediation risk and decide whether to proceed — and on what terms. Structure your pre-LOI data assessment around four areas: **1. Data infrastructure** What systems hold the core operational data? Are they integrated, or does consolidation happen manually? When was the last major system change? A business that migrated its ERP eighteen months ago may have a historical data gap that makes pre-migration performance analysis unreliable. **2. Data ownership and governance** Is there a head of data, a data engineer or any dedicated analytics function? What is the data literacy of the senior leadership team? A CEO who cannot read a SQL query does not need to — but they do need to understand what their data can and cannot tell them. **3. Analytical output quality** Review the metrics the business actually uses to manage itself — not what they prepared for the data room. Ask for the last three board packs. What KPIs appear consistently? Are they trended? Are they segmented? A business that tracks revenue but not margin by customer, or that reports churn as a single blended number without cohort visibility, is telling you something about its analytical culture. **4. Remediation complexity** If there are gaps, how hard are they to close? A business running a modern cloud ERP with clean master data is six months from being analytically excellent. A business running a legacy on-premise system with fifteen years of unstructured data is a two-year programme. --- ## What good looks like — and what it enables An analytically capable business at acquisition is not just a lower-risk investment. It is a faster one. The value creation clock starts at close, and the first twelve months matter disproportionately. A consumer retail business with a clean customer data model can run segmentation and personalisation programmes within ninety days of close. A business without one spends those ninety days arguing about how to define a customer. Operating partners working with Rodan on pre-deal data assessments typically find one of three profiles. The first is genuinely capable — modern stack, owned data, some analytical talent. These businesses are rare below £750m revenue. The second is structurally fixable — the right core systems are in place but capability is underdeveloped; a focused six-month programme closes the gap. The third is a significant programme — fragmented systems, no ownership, manual everything; factor £500k–£1.5m and 18 months into your value creation plan before you model the upside. Knowing which profile you are buying changes your price, your first 100-day plan and your team requirements. It also changes your confidence in the numbers you used to underwrite the deal. --- ## The cost of finding out post-close Operating partners who skip structured data due diligence do not avoid the problem. They defer it — into the post-close period, where remediation is more expensive, the management team is distracted and the board is watching the clock. The businesses that compound value fastest are the ones where the operating partner arrived at close with a clear data architecture plan, a remediation budget already in the model and a mandate to execute. That level of preparation is only possible if you asked the right questions before LOI. If you are currently in a live process or assessing a pipeline target, Rodan offers a focused data due diligence diagnostic — typically delivered in two to three weeks, sized at £1,500–£2,000 — that gives you a clear read on data maturity, remediation complexity and the specific risks relevant to your value creation thesis. Request a diagnostic at rodan.io. --- **Meta description:** What operating partners should ask about data before signing an LOI — a practical framework for assessing data maturity and execution risk in PE due diligence. 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