Data Strategy for Mergers and Acquisitions
Despite the impact of the global pandemic there has been no shortage of merger and acquisition (M&A) activity.
Despite the impact of the global pandemic there has been no shortage of merger and acquisition (M&A) activity. Volatility often creates opportunity to acquire assets at attractive prices. During 2020 we have seen a number of sizable transactions including Morgan Stanley/E-trade, LVMH/Tiffany, Intuit/Credit Karma, Grubhub/Just Eat Takeaway, AMD/Xilinx, Aon/Willis Towers Watson and Liberty Global/Telefonica.
Data has always underpinned successful M&A from conducting effective due diligence, through data harmonization and synergy realization. In recent years the proliferation of data has created new opportunities to integrate data and data strategy as essential parts of the M&A toolkit from the initial identification of potential acquisition targets to long term strategic value creation.
The Crucial Role Data Strategy Plays in the 5 Stages of M&A
Looking at the five main stages in the M&A process (figure 1), it is possible to identify the critical role that data and data strategy play throughout the M&A process.
The rapid expansion in publicly available data has allowed investors and acquirers to garner significant intelligence about acquisition targets. One large U.S. financial institution has been maintaining target company data books that are updated every quarter for more than 20 years. The data books track the financial performance and gather market intelligence of potential acquisition targets for each of its major divisions. In recent years, manual data collection processes have been replaced by artificial intelligence and data mining tools that feed advanced analytic models to update valuations and possible bid scenarios.
Due diligence has long been foundational to structuring M&A transactions. Historically this was largely confined to financial data, however in recent years the scope has expanded significantly. Due diligence now processes data around a broad range of topics including potential litigation risks, employee demographics including diversity, sustainability strategies, enterprise data quality, system complexity and the relative strength of customer, supplier, regulator and investor relationships. This demands a set of processes that can handle not just financial data but operational and market data that is both structured and unstructured.
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Day One Operation
Planning for a successful ‘Day 1’ following the consummation of a M&A transaction is crucial for deal success and sets the foundation for both near term synergy realization and strategic value creation. Much of the effort is focused around ensuring that the essential data required to run the combined business is available, accurate and harmonized where necessary. Companies with well-defined M&A playbooks can mobilize SWAT teams around key data elements that combine business, functional and technical expertise. Today these teams are using sophisticated data sourcing, cleansing, migration and harmonization tools to rapidly assimilate and merge data across all parties to a transaction. This accelerates integration and reduces the risks of data driven integration challenges.
Almost all M&A deals are founded upon some strategic logic for combining two or more entities. However, that is rarely enough to convince investors and lenders to back a deal. They are not prepared to wait for the benefits to accrue; they want a payback in the short term through the realization of synergy savings. Again, data and data strategy are central to the value proposition. Estimating savings from organizational streamlining, contract renegotiation, overhead cost reduction, elimination of duplicate activities, scale economies is founded upon the quality of the data used to make estimates and more importantly track and report savings as they are realized. The longer it takes a merged organization to truly harmonize data the longer synergy savings take to be realized and the greater the risk of a deal’s economics not meeting expectations.
Today, more and more M&A deals are predicated on strategic logic that is underpinned by the value of combining data, products, relationships and/or markets to create value. Combining product sets, customer bases, market access, sourcing, administration and innovation all require a robust, scalable and adaptive data architecture over time. All too often organizations lose focus on the strategic rationale for a deal after the high hurdles or deal closure and synergy realization have been cleared. Those deals that pay-off handsomely are those where the management and use of data is a well-defined and executed set of processes, tools and technologies that don’t just enable the realization of the original value proposition but uncover new sources of value over time.
How Data and Data Strategy as Core Competencies Pay Off
The ability to manage data sustainably across an enterprise as the business portfolio changes is a core competence that underpins strategic and operational success. Investors, regulators and board members are becoming increasingly conscious of the value (and risks) associated with data governance. Emerging technologies are shortening cycle times, reducing risk and delivering more predictable outcomes; however, management needs to commit to managing data with the same care, discipline and investment as they do other enterprise assets.