Quick answer: An FDD databook is the workbook that holds the analysis behind a financial due diligence engagement. It reconciles management information to source records, rebuilds earnings, working capital and net debt on a consistent basis, and keeps every adjustment traceable. A good databook lets a reviewer follow any number back to a ledger, schedule or management response without asking the analyst.
Deal teams often see only the report or the summary slides. The databook sits underneath them. When a buyer’s adviser asks how adjusted EBITDA was reached, or why the working-capital target looks the way it does, the answer is in the databook. That is why reviewers spend so much time on it, and why its structure matters as much as its arithmetic.
What an FDD databook is, and what it is not
A databook is an analytical workpaper, not a presentation. It is usually built in Excel, with one tab per workstream and a clear flow from raw data to analysis to summary. It is not an audit file: the work tests how earnings, cash and balances behave for a transaction decision, and it does not give an opinion on the financial statements.
Most databooks share three layers:
- Source layer: trial balances, ledgers, management accounts and schedules supplied by the target, kept as received.
- Analysis layer: mapped and reconciled data, adjustments, monthly trends and calculations.
- Output layer: summary tables and exhibits that feed the report, each referenced to the analysis behind it.
The tabs you will usually find
The exact list depends on scope, sector and the lead adviser’s templates. The table below shows a common structure and what a reviewer should look for in each area.
| Tab | Purpose | What to check |
|---|---|---|
| Cover and basis of preparation | Scope, periods, entities, data sources, version and open items | Periods and perimeter match the engagement letter |
| Trial balance mapping | Maps each ledger account to the databook’s reporting lines | Every account mapped once; mapped totals equal the trial balance |
| Summary P&L, balance sheet and cash flow | Historical financials on a consistent basis, often monthly and LTM | Agrees to management accounts and, where available, filed accounts |
| Quality of Earnings bridge | Reported EBITDA to adjusted EBITDA, adjustment by adjustment | Each adjustment described, quantified, sourced and classified |
| Revenue and customers | Revenue by customer, product, channel or location; concentration and churn | Revenue analysis reconciles to the ledger |
| Gross margin and operating costs | Margin trends, cost behaviour, payroll and headcount | Unusual movements explained, not just shown |
| Net working capital | Monthly trade working capital and its components | Definition consistent with the deal; seasonality visible |
| Net debt and debt-like items | Borrowings, leases, deferred items and other financing-type balances | Nothing counted in both working capital and net debt |
| Capex and cash conversion | Capital spend and the bridge from EBITDA to operating cash | Cash proof agrees to bank balances |
| Forecast bridge (if in scope) | Latest actuals to the forecast starting point | Assumptions linked to historical evidence |
| Open items and questions log | Outstanding data requests and management questions | Each item has an owner, date and status |
How a reliable databook is built
The difference between a databook a reviewer trusts and one they have to rebuild usually comes down to a few disciplines applied from the first day.
- Tie out before you analyse. Mapped trial balances should agree to the source for every period before any adjustment is made.
- Keep inputs separate from formulas. Hard-coded figures belong in source or input tabs, clearly labelled, never buried inside calculations.
- Reference everything. Each adjustment and exhibit should point to the ledger detail, document or management response that supports it.
- Use one sign convention and one period convention across all tabs, including how LTM and year-to-date figures are calculated.
- Version deliberately. Date-stamp versions, log what changed and keep the reviewer’s comments with their resolutions.
Checks a reviewer should run
Whether you built the databook or received it from a team supporting you, these checks catch most of the problems that surface late in a deal.
- Do the mapped totals agree to the trial balance for every month in scope?
- Does the summary P&L agree to management accounts, and are differences explained?
- Is every EBITDA adjustment described in plain language, supported and classified, for example as management-proposed or diligence-identified?
- Are working-capital and net-debt definitions consistent with each other and with the draft transaction documents?
- Does the cash proof reconcile to bank balances?
- Are open items still open, and has anything material been closed without evidence?
- Would a second reviewer reach the same number from the same data?
Illustrative example: one line of an EBITDA bridge
The figures below are invented to show the format. They do not describe any client or engagement.
| Line | Amount (illustrative) | Support referenced |
|---|---|---|
| Reported EBITDA, LTM | 4,200 | Summary P&L, agreed to management accounts |
| Add back: one-off legal settlement | +150 | Ledger detail, settlement agreement, management confirmation |
| Less: owner salary below market rate | −90 | Payroll records, market comparison agreed with the lead adviser |
| Adjusted EBITDA, LTM | 4,260 | QoE tab, adjustments 1 and 2 |
The point is not the arithmetic. It is that each line tells the reviewer where to look.
Common issues that slow a deal down
- Adjustments presented as a single net figure, with no list behind it.
- Working capital analysed at two year-ends only, hiding seasonality.
- Accrued bonuses or deferred revenue treated differently in working capital and net debt.
- Forecast starting points that do not bridge from the latest actual month.
- Management explanations recorded in emails rather than in the questions log.
Databook vs diligence report
The report tells the story: the key findings, their likely effect on value and the questions that remain. The databook proves it. Many lead advisers keep the report short because the databook carries the detail. If a finding in the report cannot be traced into the databook, treat that as a review point, not a formatting issue.
When outside databook support helps
Building a clean databook is labour-intensive, and the work tends to arrive in peaks. Accounting firms and deal teams often add capacity for data mapping, reconciliations, QoE schedules and working-capital analysis while keeping scope, judgement and sign-off in-house. Plus One provides outsourced FDD and Quality of Earnings support on that basis. For a wider review framework, see the financial due diligence checklist, and for how this work differs from assurance, read Quality of Earnings vs audit.
Frequently asked questions
What is an FDD databook?
It is the workbook, usually in Excel, that holds the financial due diligence analysis: mapped trial balances, the Quality of Earnings bridge, revenue, margins, working capital, net debt and cash conversion, each traceable to source records.
Is there an example of an FDD databook?
This guide shows the usual tabs and an illustrative line of an EBITDA bridge with invented figures. Real databooks are confidential to the engagement, and the layout depends on the scope and the lead adviser's templates.
What is the difference between an FDD databook and a due diligence report?
The databook is the analytical workpaper with all the detail and references. The report or presentation summarises the findings for decision-makers and should only contain numbers that can be traced back to the databook.
Finance and advisory team based in Gurugram, India, working since 1992. This guide is general information and does not constitute transaction, accounting, tax or legal advice.