Relevant and reliable data is an asset in any negotiation. This is especially true in the mergers and acquisitions (M&A) space, where data and analytics drives value at every stage. Even so, what happens when a global pandemic throws historical data in question? That's when the need for real-time data science becomes clear.
COVID-19 has rendered 2020 an anomaly. Companies cannot wholly rely on historical data from the prior year to inform their current and future M&A moves. However, tools and capabilities exist to take a more forward-looking approach that leverages timely, accurate and relevant alternative data.
Real-time data science can provide valuable insight throughout all stages of an M&A transaction. For example, right from a deal's onset, we can leverage alternative data and predictive analytics to:
Data science is also proving critical throughout the deal evaluation stage. Here, we can take advantage of machine learning, automation, and visualization tools to quantify value creation opportunities and validate investment hypotheses to help buyers bid with confidence.
Take a multi-location retail business, for example. Internally focused metrics (e.g., foot traffic, loyalty programs, sales trends) can be combined with third-party data (e.g., local demographics, competitor activity, market trends) to produce an in-depth location score using spatial analytics. The resulting insights can then be used to pinpoint success factors, marketing and sales strategies, and untapped growth opportunities at a store level. What's more, that same information can be used to find similar M&A opportunities moving forward.
But the value of real-time data science is not limited to the merger or acquisition process itself. Data science can also provide an edge long after the deal has been done, whether by quantifying and realising upside opportunities, applying data-driven optimization and conducting granular cost savings calculations. Certainly, using timely market data to offer full-scope coverage is simply more valuable to a company's strategy than outdated or anomalous benchmarks or samples.
The use cases for data science are expanding. So much so that data and analytics is no longer a siloed function but something that leading forms are embedding into every part of the M&A process. In recent years, advances in this field have helped KPMG create tools such as our Strategic Profitability Insights and Working Capital optimization platforms, the latter of which uses granular transaction-level information to calculate exact potential cost savings and a clear path to value creation execution.
Whether the aim is to gain an early perspective on an asset, strengthen the due diligence process, or generate long-term value after the final signature, there is no shortage of benefits to a real-time data-driven approach. Assuming, of course, that a company makes the best use of the data and analytics tools within their reach.
Taking full advantage of data science means having proven methodologies that can be executed at deal speed. This requires people with skills to wield data science in a deal environment and the technologies and digital resources to do so effectively. Moreover, it means having access to proprietary and third-party data sources and the infrastructure to handle large volumes of data with speed and flexibility. At KPMG, these resources and capabilities have enabled us to provide clients with a live, data-driven edge at the M&A negotiation table.
2020 has been a year of exceptional uncertainties and anomalies. Unpredictable circumstances have made it difficult to extract data that has relevance to today's deals. But rather than disregard the advantages of data and analytics, the key to success is using what we know in the moment to drive value both now and beyond the final handshake.