Predictive Analytics across the Enterprise: From eDiscovery to DoJ Second Requests to Proactive Protection of Intellectual Property

The accelerating increase in the volume of Electronically Stored Information (ESI) is resulting in knowledge workers reaching a point where they may not be able to utilize traditional data management and analytic technology and processes to keep pace. However, the increases in knowledge worker productivity and decreases in eDiscovery costs made possible by predictive analytic technology are coming to the point where they are applicable to other knowledge management tasks within the enterprise.

Predictive analytics has been trending up in the eDiscovery market with the promise to address the issue of how to manage the increasing amount of ESI in a legally defensible manner.  Referred to as predictive coding, eDiscovery software vendors are providing different applications of predictive analytics throughout the entire eDiscovery lifecycle.

The early feedback on predictive coding indicates that it does in fact enhance legal knowledge workers ability to more quickly and cost effectively identify potentially relevant documents in a statically significant manner during document review. As such, some eDiscovery tool vendors are now offering predictive analytics to support culling of ESI during Early Case Assessment (ECA).

Other vendors are now offering predictive analytics to assist with identification of potentially relevant documents before they are collected and preserved for further analysis. Examples of eDiscovery vendors that are offering predictive analytics throughout various phases of the eDiscovery lifecycle include; Recommind, Orcatec, Rational Retention, Equivio, Daegis and Autonomy.

If these predictive analytic technologies prove to be successful during the identification and preservation phases of the eDiscovery lifecycle, then knowledge workers throughout any enterprise with the task of analyzing any large amounts of ESI (i.e. Big Data) should be able to successfully utilize predictive analytics. Further, eDiscovery vendors have developed these predictive analytic technologies for the eDiscovery market will have the opportunity to expand into other even bigger markets.

For example, in United States antitrust law, a DoJ Second Request is a legal discovery procedure (or request to produce documents) by which the Federal Trade Commission (FTC) and the Antitrust Division of the Justice Department investigate mergers and acquisitions which may have anti-competitive consequences.

Under the Hart-Scott-Rodino Antitrust Improvements Act, before certain mergers, tender offers or other acquisition transactions can close, both parties to the deal must file a “Notification and Report Form” with the FTC and the Assistant Attorney General in charge of the Antitrust Division.

If either the FTC or the Antitrust Division has reason to believe the merger will impede competition in a relevant market, they may request more information by way of “Request for Additional Information and Documentary Materials”, more commonly referred to as a “Second Request”.

A typical second request asks to gather information about the sales, facilities, assets, and structure of the businesses which are party to the transaction. This frequently requires a large amount of documents to be produced, and law firms representing parties to a transaction in which a second request has been issued often must hire contract attorneys to review all the documents involved.

The process that the parties to a merger have to go through to fulfill these DoJ Second Requests are for all practical purposes very similar to the eDiscovery lifecycle that these same enterprises would go through to fulfill a request to produce documents in a legal matter. As such, the same predictive analytics technology that these enterprises utilize for eDiscovery could also be utilized for DoJ Second Requests.

In another example, the proactive protection of intellectual property (IP) and other confidential information has become a high priority for many enterprises. In response enterprises have turned to proactively scanning employee created ESI such as email, text messages and other unstructured documents and looking for keywords that may indicate a breach in the security of IP and other confidential information.

However, just as keyword searching has been proven to be less than adequate for finding relevant documents in eDiscovery, the same would be true using keyword search to protect IP and other confidential information. As such, the same predictive analytics technology that these enterprises utilize to replace keyword search for eDiscovery could also be utilized for the purpose of protecting IP.

In addition to eDiscovery, DoJ Seconds Requests and the protection of IP and other confidential information, there are many other tasks that enterprises require their knowledge workers to perform that are quickly reaching a point where they are physically will not be able to utilize traditional analytic techniques to keep up with the accelerating increase in the volume of ESI.   

As is the case with eDiscovery, DoJ Seconds Requests and the protection of IP and other confidential information, every one of these similar tasks should benefit from the use or predictive analytics to increase knowledge worker productivity and reduce the overall cost of performing that task.


About charles.skamser

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