Big data analytics (big data) has established a reputation as a tool useful in the financial services arena, where it has enhanced banks’ abilities to personalize data of their customers to predict trends. More recently, big data is becoming popular in the context of mergers and acquisitions in all sectors.
What is big data analytics?
Big data refers to the collection and analysis of large volumes of structured and unstructured data, in real-time to create value for companies. Big data’s opportunity to leverage unstructured data is the real value-add it provides for companies. Unstructured data refers to information that either does not have a pre-defined data model and/or is not organized in a pre-defined manner. For example, a standard email may contain 3 sets of information that can fit into columns of a table: recipient, subject-line and email body. When compiling thousands of data points, the column of the table that includes the email body (due to the large amount of text) does not provide much opportunity for a user to perform analysis. Big data analytics allows a user to mine this information as well as information stored in social media posts, videos, audio files and images.
Big data potentials in M&A
Ernst and Young (EY) has recently recognized the potential of using big data analytics in private equity deals. They have coined the term “transactional analytics”, which combines 5 aspects of big data: the target’s data, your company’s data, 3rd party data, statistical algorithms, and quantitative analysis. Transactional analytics provides insights and quicker decision making with regards to mergers, acquisitions.
Nearly always, the first step of a merger or acquisition is determining a target. Data analytics can allow buyers to visualize a wider playing field, allowing for comparisons, combinations or cutting of duplicate resources to be made to help maximize revenue and minimize costs.
The amount of competition in the marketplace has shortened due diligence time frames. It is not unusual for this period to be approximately 2-3 weeks. From a due diligence perspective, data analytics provides the opportunity for firms to focus the diligence on the key issues in order to drive a quicker close. According to EY, the number one reason for reducing an offer price or walking away from deal is a lack of information. Big data allows executives to draw insights from new data sets, arming them with unrealized information. For example, in a retail deal, data analytics can reveal how customers are segmented, what they’re buying, when they’re buying and the influences on that buying behavior.
In addition, big data also adds value to companies after the close by allowing for the quicker realization of synergies between the merging companies. For example, during the due diligence process, the buyer can look at the potential target’s customer base, compare it to their own customer base and identify areas and opportunities for cross-selling and up-selling. This provides for immediate advantages after the close.
The author would like to thank William Chalmers, Summer Student, for his assistance in preparing this legal update.
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