The evolution of data analytics in M&A due diligence
October 3, 2024 | Authored by RSM US LLP
ARTICLE | October 03, 2024
Authored by RSM US LLP
For more information, please contact Thomas J. Emmerling at tjemmerling@dopkins.com.
Due diligence is the foundation of successful mergers and acquisitions (M&A). With the evolution of technology and the abundance of available data, traditional methods of due diligence have given way to advanced analytics, enabling more thorough evaluations of business opportunities. Today, companies leverage cutting-edge tools and extensive data sources to gain a comprehensive understanding of assets, liabilities and future business potential. These developments enable deeper insights and more strategic decision making, fundamentally transforming how companies approach M&A.
Data sources: Fueling informed decision making
Data is the bedrock of modern analytics, and today’s M&A deals benefit from a wide range of internal and external (aka alternative) data sources:
- Internal data: Financial statements, operational reports and customer transaction databases have always played a role in due diligence. Modern analytics now allow for deeper exploration of these datasets, moving from aggregate views to granular analysis by customer, geography and product or SKU.
- External/alternative data: Publicly available data, including consumer behavior trends and industry benchmarks, provides valuable context. By integrating external data, companies can assess how the target company is performing relative to peers and industry trends.
- Unstructured data: Traditionally challenging to analyze, unstructured data, such as consumer or client reviews, and any online footprint can now be processed using modern tools. These insights help companies measure client sentiment, brand perception and operational risks in real time.
Tools: Empowering the analytics engine
While data provides the fuel, advanced tools are the engines driving modern M&A analysis. The due diligence process is now supported by a suite of technologies that streamline data processing, visualize insights and apply predictive models:
- Data transformation tools: Solutions like Alteryx, SQL and Azure Synapse Analytics allow analysts to extract, clean and structure data, automating much of the manual work traditionally done in spreadsheets. This enables faster, more accurate assessments.
- Visualization platforms: Tools such as Tableau and Power BI transform complex datasets into visual stories, helping deal teams see trends, compare performance and identify outliers or risks quickly.
- Predictive and machine learning tools: Programming languages like Python and R enable companies to build predictive models based on historical data. These tools leverage AI to project future growth, client retention rates and operational inefficiencies before they become problematic.
Maximize efficiency with integrated data and tools
When combined, modern data sources and powerful tools create a symbiotic relationship that amplifies the value of each. Here’s how they come together in practice:
- Comprehensive insights: By integrating internal and external data, businesses can analyze not only how a target has performed historically but also how it compares to competitors and market trends. Benchmarking client retention, for example, provides valuable context that was previously unavailable.
- Data-driven forecasting: Predictive analytics models incorporate structured and unstructured data to project future revenue growth and identify opportunities. AI-enabled forecasts offer deeper insights into customer sentiment and competitive activity.
- Value creation: Data analytics doesn’t just mitigate risks—it uncovers hidden opportunities. By drilling down into granular data, companies can identify growth potential in underperforming customer segments or geographies.
Application across the M&A lifecycle
The synergy of data and tools enhances value creation throughout the entire M&A lifecycle:
Pre-letter of intent: Data analytics uncovers early insights into a target’s financial health and operational efficiencies, identifying risks or growth opportunities before a formal offer is made.
Post-close integration: After the deal closes, analytics tools monitor performance, track synergies and ensure business targets are being met. Data integration is crucial in this phase as businesses harmonize legacy systems and gain real-time visibility.
Ongoing analysis: Unlike traditional due diligence, modern approaches create a continuous feedback loop, enabling companies to make ongoing adjustments and drive long-term value creation.
The future of M&A due diligence
As technology continues to advance, data analytics will play an increasingly vital role in M&A and value creation. AI-enabled tools will synthesize data from diverse sources, including consumer behavior, offering deeper insights into market conditions, competition and consumer preferences. Predictive models will continue to be refined, providing greater accuracy and helping companies identify not just risks but growth opportunities.
The companies that master the combination of cutting-edge tools and diverse data sources will gain a distinct advantage, making quicker, more informed decisions and positioning themselves for long-term success.
This article was written by Abay Zhunussov and originally appeared on 2024-10-03. Reprinted with permission from RSM US LLP.
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For more information, contact
Thomas J. Emmerling PhD, CFA®, CVA®
Tom counsels clients in exit planning and mergers and acquisitions, helping them navigate the complex and often lengthy process as businesses are transitioned to new ownership.
