Deloitte M&A Focus On: Analytics | Deloitte US has been added to your bookmarks.
Deloitte M&A focus on: Analytics
Building versus buying M&A analytics capabilities
The critical stages of a merger and acquisition (M&A) transaction can benefit from one thing: Analytics. However, much of the value analytics hold, could be lost if the highly specialized skillsets involved in applying the data are not built into the talent strategy. When is it beneficial to use internal talent versus external expertise? Turn the complexities of analytics into clarity with an effective talent strategy.
By Brian Bird, Director, M&A Transaction Services, Deloitte & Touche LLP
Having merger and acquisition (M&A) analytics capabilities at the ready, whether for a transaction or for ongoing operations and strategic execution, has become a must-have for more organizations, both on the corporate and private equity side. M&A analytics is now often an essential part of the deal making process, in either target valuation, implementation, or some other critical stage. A great majority of firms we have surveyed say data analytics are deployed in their M&A activities–in our results, 33 percent of survey respondents said it is a core component of their M&A analysis and another 40 percent said it is used in select areas of analysis. Only 13 percent say it is not used or being evaluated for future use at all.
Creating that capability, and investing behind it, often requires a series of choices and trade-offs. After all, analytics are a new and complex business tool, and complexity ranks, with 29 percent of respondents, as the biggest impediment to deployment, according to our survey.
This can be particularly true when it comes to talent. The skill sets involved in applying data analytics are highly specialized. Data scientists are often in great demand. Any organization seeking to build its own analytics capabilities should expect the need to staff up with people who can develop and maintain algorithms, produce data visualization, monitor analytics, and execute against strategic goals. The cost of such a capability is not insignificant; in fact, 15 percent of all survey respondents say cost is the biggest impediment to deploying analytics in deal making and analysis.
For certain types of organizations, for whom analytics is part of ongoing, steady state operations, building such capabilities, complete with a talent strategy, may be a necessary step. Depending on the industry, analytics have become a core element of strategy building and execution. Among dealmakers, a basic understanding and literacy of data analytics can be expected in nearly all industries. As a result, many organizations have built teams of data scientists to source, manage, and query data sets so that leadership can make better decisions about their business. Increasingly, executives themselves are becoming familiar with analytical capabilities, to the point that they can guide data scientists better, and get more out of their analytics teams.
But even when they have these teams, many organizations cannot always offload all of their analytics needs internally.
This can be particularly true in the case of M&A transactions, where analytics can provide a valuable review of key valuation drivers, which are typically highly confidential and competitively sensitive. To give one such potential example, if an acquiring company is evaluating a target in an industry with antitrust issues, the potential acquirer would likely want to bifurcate its data analysis away from its normal business analytics function.
The reason is simple: The value of data around pricing, customers, contracts, and other market-sensitive information is often essential to a potential transaction and such confidential information must be shared. But if the transaction is not concluded, even temporary access to that data can create burdensome legal oversight to avoid future litigation over the use of the competitive information by the employees in question. For many companies, it makes sense to have an outside entity handle analytics around such transactions in a clean room-type environment, even if they have the capabilities in-house.
Among many private equity firms, awareness of the power of analytics is widespread, and therefore deal principals are expected to turn to analytics tools throughout the deal process. That said, firms rarely maintain analytics capabilities in-house. Many deal principals are prepared to guide outside data scientists on what they need to achieve, and provide meaningful analysis themselves.
What private equity firms will typically do is establish a principal investment in a platform company using an outside analytics resource to provide critical strategic insight. Then, as the platform company looks for roll ups, they leverage any analytics capabilities within the platform, and deploy it, or supplement it as necessary, as the platform roll up additional assets. This typically leads to a bespoke, company-specific analytics capability for steady state operations, and which can be supported, as needed, by outside resources for transactions or other work.
One of the results of all this activity, of course, is greater demand for skilled data scientists. Our experience suggests that hiring and retaining such talent can be a special skill in itself. Like any professional with a highly specialized skill, data scientists typically are drawn to multiple assignments over time, which expose them to a diversity of industries and strategic challenges. A hybrid approach of building some capability in-house and augmenting it as necessary with outside support appears to be the most effective way for any organization to deploy analytics regularly and effectively over time.
About the survey
In December 2015, we talked to 500 corporate leaders at large US companies about analytics. Those surveyed were at director level or above and were at companies with at least 10 million dollars in revenue.
View the full survey results on SlideShare
Don't miss the other articles in this three-part series: