One consequence of the world’s increasing computing power, expanding computer use and the ability of computers to capture and share different types of information is the generation of big data. It is data that because of its core properties is difficult to analyze with traditional data analysis techniques and software.
Despite the analytical processing challenges it poses, new techniques are being developed which make it possible to analyze this kind of data more effectively and allow it be used by individuals, companies and governments in many different business and scientific fields. This will likely have an important impact on many areas of M&A, such as in the definition of M&A strategy, business model validation and valuation.
This article will provide an overview on the matter by looking at a few types of analytical techniques and consider the potential impact of this kind of data analysis on M&A.
Overview and Big Data Analysis
While it can have many different qualities, its key attributes are:
Volume. it is characterized by large volumes. According to one general estimate that was published in October 2017 – the distant past in terms of global data growth – there were 2.7 zettabytes of data in the digital universe. This unimaginably large number is the equivalent of 1 trillion gigabytes.
Velocity. it is characterized by the extremely rapid pace at which it is generated. According to one report, 2.5 billion gigabytes of data were generated every day in 2012. With more than 3 billion people on line, millions of Google searches are now generated and hundreds of hours of videos are uploaded every minute.
Variety. it is also characterized by its great variety. In addition to text, this big amounts of data are also comprised of audio, video and changing combinations of data transmission methods.
Data with these properties are often very difficult to process with traditional data analysis techniques. This means that a great deal of the potential ability to use this data is lost.
Due to the challenges of processing data, various techniques are being developed to process big data. One example of this is the Apache Hadoop system, a set of open source programs which includes a component called MapReduce which reads large amounts of data, reduces it in a form that makes it suitable for analysis and then runs mathematical functions on the data.
Apache Spark is another open source data framework for data analysis. Apache Spark can perform some data analysis techniques 100 times faster than MapReduce.
A program used in statistics is R. R is very useful for data mining and for data visualization.
Big Data and M&A
It will very likely it will soon have a large impact on M&A. The following are some key ways it may change how M&A deals are identified and executed.
Strategy development. There are numerous potential M&A strategies, ranging from realizing operational synergies, creating long-term value, turnarounds of poorly performing companies and risk arbitrage. While strategy selection is defined by the particular goals of the company executing an M&A strategy and the skill sets of the M&A team members, it is also heavily influenced by numerous market factors that determine if a strategy should be launched, when it should be launched and how likely it is that it will be successful if it is launched. These factors will increasingly be able to be reduced to data points that companies can use to make strategic choices.
If you want to create an M&A strategy, you can also listen to our podcast “M&A AND SMES”, where we discuss the key objective of M&A, talk how M&A techniques can create value and list five specific points for SME’s to keep in mind when creating an M&A strategy and evaluating M&A opportunities.
Acquisition targets. Finding targets to carry out an M&A strategy is often a very time consuming process which fails to identify suitable targets and closed deals. Low M&A execution rates are due to various factors, including limited search parameters, search biases, due diligence challenges and buyer/seller price expectation mismatches. With big data it will be possible to drastically improve M&A target searches and pre-screen targets more effectively, which should improve successful deal close percentages.
Business model validation. A significant challenge in analyzing a potential acquisition target is validating a company’s core business model. Particularly for acquirers who are not located in the same market as the target company, it can be very difficult to obtain real time market information and predict what that means for a company’s business prospects. With big quantities of data data, it will be possible to obtain far more detailed analyses of factors such as how fast a target company’s market is growing or shrinking, how cyclical market patterns compare with historical patterns, the amount of customers that are in a market or positioned to enter a market and their preferences and how the market is reacting to the target’s or competitor’s products.
Valuation. Often a major roadblock to executing M&A deals is valuation. Even setting side common biases for buyers to discount firm and asset values and sellers to inflate them, valuation is very challenging due to the fact that it often involves trying to forecast the future. Using big data in connection with market-based valuation techniques, such as EBITDA multiples, it will be possible not only to extract multiples from much wider market data bases, but more quickly and reliably perform comparisons between a target company and the company’s valuation reference set to make appropriate EBITDA adjustments. For valuation models that are based on discounted cash flow analyses, it will become easier to prepare cash flows, identify risks to those cash flows based on existing market information and prepare stronger assumptions about how those risks will affect those cash flows.
Shareholder activism. The existence of data in real time about a company, the execution of company’s business model and a company’s competitors will likely significantly change the relationship between a company’s founders, executives and outside investors. Rather than shareholder activism that is driven by periodic financial reporting, it is likely that increasingly available information will significant shorten the intervals between market events, company actions and shareholder attempts to influence what steps in the market a company is taking or plans to take.
As the amount of data in the world grows, technology will attempt to store the data, break it into intelligible pieces and use the data for different purposes. It is likely that the aforementioned data analytical techniques will have a large impact on M&A given that it is heavily impacted by data points that can be extracted from the market. In light of this, companies as well as investors should try to stay informed about data analysis developments so they can incorporate them into their M&A strategies and increase the likelihood that M&A deals will create lasting shareholder value.
The article was written by Darin Bifani.