Data and BI expertise for financial due diligence

When a transaction requires more data work than a standard model can handle, you can bring me in alongside the CFO, financial adviser or M&A team. I am Roel Bäumer, founder of Insitely and an M&A data specialist. My focus is the data and BI work within the existing deal team.

With eighteen years of experience in data engineering and business intelligence, I support CFOs, M&A advisers and mid-sized to large companies with data analysis for acquisitions and due diligence engagements. That experience includes financial reporting in complex corporate environments.

My role is not to take over the due diligence process or provide deal advisory. The adviser leads the process and translates the findings into deal implications. The CFO or finance lead contributes the knowledge of the business and its financial context. As an M&A data specialist, I ensure that the necessary data is reliable, traceable and ready for analysis.

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. For that kind of work you can bring me in as a complement.

When is additional M&A data expertise needed?

In many acquisitions the financial and commercial analysis questions become clear quickly:

• 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 contribute to the growth?
• Which exceptional entries affect the reported result?
• Do the commercial analyses reconcile to the general ledger?

The difficulty usually does not sit in the question but 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 good-looking chart is usually still possible. Demonstrating that the chart is correct becomes another matter.

Specialist M&A data expertise bridges the distance between the available 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. The financial and commercial DD questions are translated into the required data, definitions and joins.

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

Connecting financial and commercial data

General ledger postings, sales invoices, customer records and additional commercial data are connected to each other. 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 work out a practical repair or matching approach. That might be fuzzy matching, where potential matches carry a match score, doubtful cases are assessed by hand and the outcome is validated through controls and reconciliations.

Setting up mapping and reconciliation

Separate data sources only become usable once definitions and totals line up. That includes:

• 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.

That way analyses stay not only usable but also verifiable and defensible within the deal team.

Dashboards and analysis in Power BI and Excel

The insights are delivered 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 and 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 the required data is available in a supported accounting system, the tool retrieves data such as detailed general ledger postings, sales invoices and customer records through a secure connection.

For Belgian files the general ledger accounts are mapped to a financial 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 exports, Excel files and mappings for every file.

Sometimes that standardised route is enough. Other files ask for additional data sources, specific analyses or extra implementation work. In that case 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 engineering and business intelligence;
• experience with financial reporting in complex corporate environments;
• 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 and insight into the questions CFOs and M&A advisers ask, the controls that create confidence and the data risks on which analyses can run aground in practice.

How an engagement starts

An engagement starts 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. On that basis a clearly scoped proposal follows.

Discuss the data expertise for your file

Relevant case: We could not even count the customers