Massive mergers are never a simple matter for organizations and their antitrust attorneys. The Second Request process can be a major burden for merging organizations since it requires that companies review, analyze and produce massive volumes of data in what can be a very short amount of time. If that doesn’t already cause panic, keep in mind that failing to fully comply can lead to substantial civil penalties and even the rejection of the merger transaction.
While Second Requests are a daunting task for any antitrust lawyer, my colleagues John D. Pilznienski and Sheldon A. Noel recently discussed the requirements counsel must meet to comply with a Second Request and how utilizing analytics like predictive coding can help simplify the process. The article written by Pilznienski and Noel, “Never Second Guess a Second Request: Leveraging Predictive Coding for Reviewing Documents in Antitrust Matters,” appeared in the March 2016 edition of Digital Discovery & e-Evidence, a Bloomberg BNA publication.
What is a Second Request?
For those of you not intimately aware of the corporate merger process, a Second Request is the issuance of a request by the Department of Justice (DOJ) or Federal Trade Commission (FTC) for “additional information and documentary material relevant to the proposed acquisition.” When corporations intending to merge meet a certain financial threshold, their proposed merger is subject to review by federal antitrust agencies. If the DOJ or FTC determines that more information is needed to ensure that there is no violation of federal antitrust laws during the initial review, they can request more information from the merging corporations. These requests can be extremely broad, requiring extensive resources, time and manpower to collect, process and produce the relevant documents and data.
Second Requests, Ediscovery and Predictive Coding: A Case Study
As discussed in the article by Pilznienski and Noel, in a recent technology sector merger the two companies utilized Kroll Ontrack’s predictive coding technology and review platform to respond to an FTC Second Request. The merger presented a number of hurdles – a large data set, multiple jurisdictions, complicated review guidance based on the document requests and an unexpectedly short production deadline – all of which were easily overcome by leveraging predictive coding. From the approximately 600,000 searchable documents, a random sample of approximately 2,300 documents was generated, sampled at a 95% confidence level and a 2% margin of error. After applying a variety of review methods, the most relevant documents were reviewed first, aiding the merging companies in meeting the FTC’s deadline and significantly reducing the costs of review.
Be sure to read the full article, Never Second Guess a Second Request: Leveraging Predictive Coding for Reviewing Documents in Antitrust Matters, for a more in-depth discussion on the application of predictive coding to the Second Request process.