Due diligence is at the heart of every mergers & acquisitions (M&A) transaction. A fair assessment of historical facts as well as future potential requires data, and lots of it. While the due diligence process will continue to remain part art and part science, the latter aspect has come a long way with the advancement in data analytics tools and techniques. Prominence of scalable computing platforms along with the emergence of affordable artificial intelligence and machine learning (AI/ML) toolkits has democratized the data allowing for faster and better information value extraction from raw data.
While traditional M&A analysis has been confined to Excel spreadsheets, the sheer volume and variety of data available today requires deploying a more robust analytical framework to extract actionable insights from the underlying data. Abundance of AI/ML algorithms allows for advanced techniques being deployed and non-traditional data to be brought under the purview of due diligence.
Certain aspects of M&A such as accretion/dilution analysis and balance sheet impact are considered straight-forward quantitative exercises. However, even these well-defined analytical processes can be better performed by leveraging lower level data to better understand anomalous trends and explain unique phenomenon that may be subject to further assessment and potential exclusion. AI/ML algorithms can help analyze rich underlying data in addition to pro forma results to identify outliers, seasonality deviations and other events of interest that may warrant further scrutiny, consideration and specialized treatment.
In terms of assessing synergies, the prospect of exploring the benefits of combined operations also introduces redundancies into the mix. Redundancies often present themselves as a low hanging fruit, but experienced deal makers know that cutting back too deep can have grave consequences to the future of the unified operations. The exploration of an optimal model involves a deeper assessment of various linkages and complimentary nature of processes not just today but also in the future. It is here that macro analytics can help inform all parties about future prospects of products and services, and leveraging alternative KPIs can serve as a barometer for assessing soundness of strategic initiatives.
Consideration, the form of payment used to pay for the M&A, is also subject to intense analytical rigor. While cash is the best type of payment, stocks, notes or a combination of these forms of payment are available options. Particularly in the case of stock, it is imperative to assess short, medium and long-term prospects, investor sentiment, customer experience and outlook and market projections, among other things. Sentiment analysis is an excellent mechanism to ascertain both positive and negative perceptions prevalent in the market. This can lead to proactive data-driven assessment of future potential versus relying solely on anecdotal evidence or news media.
One area in which data analytics really shines is in the goodwill and other intangible assessment analysis. Alternative KPIs have the potential to help utilize a vast array of available information, along with the surplus of data extraction, manipulation, enrichment and classification tools, and potentially provide decision-makers with a rich set of valuable metrics for the business.
In continuation to this series, we will pivot to industry specific use cases and dive deeper into some of the data analytics tools available at our disposal. DHG is positioned to help you and your business assess M&A opportunities as part of your strategy and help you emerge strong in the post-COVID-19 era. For more information about our data analytics capabilities, reach out to us at firstname.lastname@example.org.