Private equity firms request the same core package on almost every deal: three to five years of monthly financials with revenue broken out by customer and by product, EBITDA adjustment schedules, AR and AP aging, customer concentration and churn, industry-specific operating KPIs, headcount and compensation detail, and an inventory of the systems all of those numbers come from. What changes is when each piece gets requested. The list starts small at screening, expands sharply when the data room opens, and then never really ends — it just becomes the monthly reporting pack. Here is the full list, organized by deal stage.
What is a data request list?
A data request list (also called a diligence request list, or DRL) is the itemized inventory of documents and datasets a private equity firm and its advisors ask a target company to produce during a transaction. It expands at each stage for a simple reason: access follows commitment. Before a letter of intent, the target owes the buyer nothing, so the firm works from public data at its own expense. Under an LOI with exclusivity, the firm has earned the right to ask for everything, and does — the confirmatory list commonly runs to a hundred or more line items. After close, the firm owns the company, and the request list stops being a one-time event and becomes a recurring reporting obligation.
Stage 1 — Screening and pre-LOI: the outside-in list
Before any LOI is signed, PE firms build a view of the target entirely from the outside. The goal is not precision — it’s deciding whether the deal is worth pursuing and forming a prior that the data room will later confirm or contradict. Five signals dominate this stage:
| Signal | What firms gather | Typical sources |
|---|---|---|
| Financial baseline | Estimated revenue and growth, filed accounts where available, credit profile, liens and UCC filings | Credit bureaus, D&B, state filings, SEC EDGAR for public comparables |
| Market size & structure | Addressable market estimates, competitor counts and density, local demographics | Census Bureau, BLS, industry associations, state licensing records |
| Customer sentiment | Review volume, rating trends, recurring complaint themes, geographic spread of customers | Google reviews, Yelp, Trustpilot, app stores |
| Hiring reality | Open roles vs. stated growth, headcount trend, attrition and morale signals | Job postings, LinkedIn headcount, Glassdoor |
| Digital footprint | Web traffic trends, published pricing, product catalog changes over time | Web-scraped data, traffic panels, archived pages |
None of this requires the target’s cooperation, which is the point. A deal team that walks into management meetings already knowing the review trajectory, the hiring pattern, and the local market structure negotiates from a very different position than one waiting for the CIM to tell it what to think.
Stage 2 — Confirmatory diligence: the data room request list
Once an LOI is signed and exclusivity granted, the real list arrives — usually within days, usually from several parties at once: the deal team, the quality of earnings provider, legal counsel, and any operational or technology diligence advisors. The requests cluster into seven categories:
| Category | What’s requested | Typical lookback |
|---|---|---|
| 1. Revenue quality | Monthly revenue by customer and by product or service line; cohort revenue retention; deferred revenue and billings; bookings and sales pipeline with conversion history | 3–5 years, monthly |
| 2. Profitability | EBITDA adjustment schedules supporting the QoE; gross margin by segment, product, and location; pricing and discounting history; cost detail by vendor | 3 years, monthly |
| 3. Working capital & balance sheet | AR and AP aging; inventory detail and turns; capex history and forward plan; debt, lease, and guarantee schedules; a 13-week cash flow | 2–3 years plus current |
| 4. Customers | Concentration (top 10 and top 20 as a share of revenue); churn and retention by cohort; customer contracts and renewal terms; CAC and LTV for recurring-revenue models | 3–5 years |
| 5. Operational KPIs | The industry's operating drivers: utilization and billable hours in services, same-store sales in multi-site consumer, occupancy in facilities businesses, net revenue retention in software, on-time delivery and scrap in manufacturing | 2–3 years, monthly |
| 6. HR & organization | Headcount by function and location; compensation and incentive plans; org chart; turnover history; key-person dependencies | Current plus 2 years |
| 7. Systems & reporting | Chart of accounts; inventory of ERP, CRM, and billing systems; the reporting stack and who produces which report; data definitions where they exist | Current state |
Revenue quality is the center of gravity. Monthly revenue by customer is the single most requested dataset in private equity diligence, because one export answers three questions at once: how concentrated the business is, whether cohorts of customers grow or shrink after they land, and how much of the growth story is price versus volume versus new logos. If a target can produce only annual revenue, or only revenue in total, that limitation is itself a finding.
The QoE drives the profitability requests. The quality of earnings analysis — prepared by an accounting firm, distinct from an audit — rebuilds reported EBITDA into the adjusted number the purchase price multiplies. Every add-back claimed in the CIM gets tested here, which is why the requests reach into owner compensation, related-party transactions, one-time items, and anything booked in the twelve months before the process started.
The systems category is the quiet one that matters. Asking for the chart of accounts and the ERP/CRM inventory looks like housekeeping. It isn’t. It tells the buyer how expensive the post-close reporting build will be, whether the numbers in the data room can be traced to source, and — on a roll-up — how painful the eventual consolidation is going to get.
Stage 3 — Post-close: the list becomes the monthly pack
The request list does not end at closing; it changes tense. What was requested once now recurs. The standard post-close package is a monthly KPI pack — revenue versus budget, an EBITDA bridge, cash and the 13-week cash flow, working capital, headcount, and the handful of thesis-specific KPIs the deal was underwritten on — plus quarterly board reporting and covenant reporting to lenders on whatever schedule the credit agreement dictates.
This is the stage where data infrastructure stops being optional. A finance team that produced the diligence dataset through six weeks of heroics cannot repeat that performance every month, and the gap shows up fast: packs that arrive late, numbers that don’t tie to the prior month, definitions that drift between reports. It’s why a data warehouse that consolidates the operating systems into one source of truth is one of the most common first-hundred-days investments — and why, on roll-up platforms where every acquisition adds another set of systems, the problem compounds rather than adds. We’ve written separately about why roll-up data challenges are multiplicative.
What PE firms increasingly request beyond the financials
The newest additions to the request list aren’t requests to the target at all — they’re independent datasets used to test what the target provided. Transaction and card panel data gets pulled to cross-check reported revenue trajectory against observed consumer spend. Review data gets analyzed to see whether customer sentiment matches the churn story in the data room. Job postings and employment data get compared against the claimed headcount and hiring plan. The through-line: management’s numbers are no longer the only evidence in the room.
We maintain a working reference of 20 categories of alternative data sources for private equity, with providers and typical price ranges, and our State of Alternative Data 2026 report sets that catalog against market-level spending trends. Running this kind of validation — sourcing the external dataset, reconciling it against the data room, and being honest about what it can and can’t confirm — is core bespoke data analysis work for us on both the buy side and the sell side.
The request list is also a test
There is one more thing the list measures that never appears on it. Every request has two outputs: the data itself, and the speed and cleanliness with which the target produced it.
The gap between what a PE firm requests and what a target can actually produce is diligence signal in its own right. A company that turns around monthly revenue by customer in a day has reporting infrastructure worth paying for; a company that needs six weeks and three consultants has just told you where the first hundred days go.
Buyers read that signal whether or not they say so. Sellers who understand this prepare the standard list before the process starts — and effectively convert a diligence risk into a valuation argument.