How to Analyse an FDD Datapack

One of the first real challenges a TS analyst faces in fieldwork is the datapack — the package of financial information provided by the target company. Knowing how to navigate it efficiently separates productive analysts from overwhelmed ones.

What Is an FDD Datapack?

A datapack is a structured set of financial schedules and supporting analyses provided by the seller or target management, typically prepared as part of a sell-side or vendor due diligence process. It usually includes:

A multi-year P&L by month or quarter
A gross margin bridge by product line or segment
A headcount and salary schedule
An analysis of non-recurring and one-off items (management's version)
Balance sheet and NWC schedules
Customer revenue analysis (sometimes)

In a buy-side process without a vendor pack, analysts must build all of this from raw management accounts and board packs.

Step 1: Understand the Structure Before Diving In

Before opening any spreadsheet, spend 15–20 minutes understanding what is in the pack:

What years and periods are covered?
What accounting standard applies (IFRS, UK GAAP, local GAAP)?
Is the data consolidated or entity-level?
Is the P&L pre-IFRS 16 or post-IFRS 16?

Answering these questions prevents hours of rework later.

Step 2: Sense-Check the Numbers

Before detailed analysis, run a quick sanity check:

Does the revenue trend make sense given what you know about the business?
Do EBITDA margins appear plausible for this sector?
Are there any obvious gaps, blanks or inconsistencies in the data?
Do the numbers reconcile to statutory accounts?

A reconciliation between the datapack P&L and the audited statutory accounts is a critical early step. Unexplained differences are always a flag.

Step 3: Build Your Own Model in Parallel

Never rely entirely on the seller's datapack model. Build your own:

Reconstruct the P&L in a clean Excel format
Apply your own adjustments and trace each item to source documentation
Build a parallel NWC and net debt schedule

This allows you to independently verify the seller's figures and identify any items they have omitted or presented differently.

Step 4: Identify Key Areas of Risk

As you work through the data, flag items for further investigation:

Revenue lines that grow unusually quickly in the final reported period
Cost lines that are suspiciously flat despite revenue growth
Items classified as non-recurring that appear in multiple periods
Debtors ageing schedules with high concentrations in older buckets

Step 5: Prepare Your Q&A List

Every unexplained item in the datapack becomes a question for management. A well-structured Q&A list demonstrates analytical rigour and drives a productive management session.

Conclusion

Datapack analysis is a core TS skill. Analysts who can move from a raw datapack to a clean model with a prioritised issues list in two to three days are invaluable to deal teams.

The Transaction Services Interview Programme (€119.99, one-time) includes a real FDD datapack exercise so you can practise the full analysis process before your interview. Enrol now.