M&A data expertise for financial due diligence

I am Roel Bäumer, founder of Insitely. When an acquisition file asks for more data work than a standard model can handle, you can bring me in alongside your CFO or M&A team. My focus is the data and BI side within the existing deal team.

I have eighteen years of experience in data engineering, business intelligence and financial reporting in complex corporate environments. As an M&A data specialist I support CFOs, M&A advisers and medium to large businesses with the data analysis inside acquisition files.

I do not take over the due diligence process or the deal advice. The adviser guards the process and translates findings into the deal, the CFO knows the business and the financial context. I make sure the necessary data becomes reliable, verifiable and analysable.

With Insitely I build a reusable financial data layer for analysis and reporting. Not every file drops straight into that model. Sometimes the data sources are complex, reliable keys are missing, or financial and commercial data has to be carefully connected first. That is when you bring me in.

When is additional M&A data expertise needed?

In many acquisitions the financial questions are clear soon enough:

• How do revenue and margin develop over time?
• Which customers come back?
• How concentrated is revenue in the largest accounts?
• Which products, regions or customer types carry the growth?
• Which exceptional entries affect the reported result?
• Do the commercial analyses reconcile to the general ledger?

The difficulty rarely sits in the question. It sits in the data needed to support the answer reliably.

Accounting data, sales invoices, CRM data and operational exports are not automatically aligned. Account structures differ, customer names change and essential key fields are sometimes missing altogether. Producing a handsome chart is still possible. Demonstrating that the chart is correct becomes another matter.

Specialist M&A data expertise bridges the distance between the source data and a financially supported conclusion.

What I do

Determining data sources and analysis requirements

Get clear on what is needed first, build second. I translate the financial and commercial DD questions into the required data, definitions and joins.

That makes clear before the analysis which detailed data has to be unlocked and where the limitations may sit.

Connecting financial and commercial data

I connect general ledger postings, sales invoices, customer records and additional sales data. In doing so I look for stable keys that let transactions, customers and financial totals be linked verifiably.

Where such a key is missing, I design a practical repair or matching approach. That might be fuzzy matching, where potential matches carry a match score and only the doubtful cases are checked by hand.

Setting up mapping and reconciliation

Separate data sources only become usable once definitions and totals line up. I help with:

• mapping general ledger accounts to a usable financial model;
• connecting sales and accounting data;
• finding and explaining reconciliation differences;
• recording control and reconciliation rules;
• keeping the link to the underlying detail visible.

Dashboards and analysis in Power BI and Excel

I deliver insights in the environments financial professionals already know: Power BI and Excel.

Power BI is strong for interactive analysis, trends, segmentation and working through larger volumes of transaction data. Excel remains particularly suited to detail checks, additional calculations and collaboration within a deal team.

So it is not a choice between Power BI or Excel. The form follows the analysis and the people who have to work with it.

Insitely first, consultancy where the file asks for it

Insitely stays my core focus. Where data is available in a supported accounting system, the tool retrieves detailed general ledger postings, sales invoices and customer records through a secure connection. For Belgian files that data is mapped to an analysis model based on the Belgian minimum chart of accounts, the MAR.

That makes a large part of the preparatory work repeatable. The data can then be used in Power BI and Excel without rebuilding the same export, cut and paste round for every file.

Sometimes that standardised route is enough. Other files ask for additional data sources, specific analyses or extra implementation work. Then I combine the tool with focused consultancy.

What I bring

My added value sits at the intersection of financial analysis, data engineering and business intelligence:

• eighteen years of experience with data, BI and financial reporting;
• practical experience with financial and commercial data analysis within M&A;
• technical depth without losing sight of the business question;
• at home in Power BI, Excel and complex data sources;
• vendor independent thinking: the solution follows the file;
• a working tool that grew out of that practical experience.

That combination brings together technical implementation knowledge, insight into the questions CFOs and M&A advisers ask, the controls that create confidence and the data risks on which analyses run aground in practice.

How an engagement starts

We start with a substantive intake: how does the DD process run today and where does the team lose time, control or confidence in the data?

After that we determine what the file genuinely needs:

1. Insitely as a standardised data layer;
2. a focused consultancy engagement around implementation or M&A data;
3. a combination of both.

The engagement can be scoped to a single complex data question or extended to match the deal team's analytical needs.

Discuss the data and BI side of your file

In a substantive conversation we map which data is available, what has to be evidenced with it and where the current approach shows its limits. A scoped proposal follows.

Discuss the data expertise for your file

Relevant case: We could not even count the customers