Mergers and acquisitions (M&A processes)

What is the challenge?

Mergers & acquisitions processes (M&A), such as company acquisitions and sales, company valuation and due diligence, management buy-out and buy-in, company succession, venture capital and private equity, company financing and IPOs, require the combination of a wide range of information, selection and valuation metrics and cost models. Such transactions are therefore complex and time-consuming undertakings.

What data can help?

  • Qualitative and quantitative information on companies (EBIT, EBITDA, cash flow, CEO salary, owner profit, gross profit, debt)
  • Market data (identification of eligible companies, number of customers, sales, margins, brand value, competitive developments, risks of market development)
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Use case category: mergers and acquisitions (M&A processes)

How can companies use their data?

Data and digital solutions help companies optimize M&A processes. With the help of automated analyses, the volume of M&A-relevant data is processed. The resulting insights are used to derive assessments and predictions (predictive analysis) for recommended actions. The benefit is that the data analyses generate transparent and more reliable bases for decision-making. These can be combined with the empirical knowledge of decision-makers. 

Where is this use of data already being applied?

The management consultancy KPMG has developed a data-based solution for corporate transactions together with the real estate fund management company TPV. Using data analysis, this enables promising investment deals in real estate funds to be screened automatically on a global scale.

How does this use of data contribute to value creation?

Data-based support for M&A processes allows for more objective selection, processing and efficient analysis of a lot of information. Value creation changes directly in the improvement and acceleration of the M&A process and indirectly with improved management decisions. This can result in increased value creation.

Aim of data use

Sources: KPMG (2018)