CTRL Survey Spotlights Increased Use of Advanced Analytics in eDiscovery
By Phil Favro, Director of Legal Education and Resources, CTRL
One of the big questions in the eDiscovery industry is whether counsel and clients are increasingly turning to advanced analytics and machine learning for eDiscovery. Indeed, many in the industry have openly questioned whether such analytical tools, particularly predictive coding, are gaining traction. To better understand the extent to which advanced analytics are being used in the discovery process, the Coalition of Technology Resources for Lawyers (CTRL) commissioned a survey to address the issues. CTRL, which is dedicated to advancing the discussion on the use of technology and analytics in the practice of law, engaged the Information Governance Initiative (IGI) to conduct the survey.
The express purpose of the survey was to specifically gauge in-house legal departments’ use of data analytics. Survey respondents included attorneys (around two-thirds), along with IT, analytics, and other professionals within or providing support for the in-house legal team. While the survey respondents were predominantly from the United States (89%), they hailed from across the spectrum of industry verticals, including financial services, manufacturing, pharmaceutical, government, and others.
The eDiscovery Use Case for Analytics
The survey questions focused on six principal use cases for advanced analytics: (1) eDiscovery/Other Investigations; (2) Legal Matter Management, Billing and Budgeting; (3) Information Governance; (4) Outcome Analysis or Risk Assessment; (5) Contract Review; and (6) Selection of Outside Counsel. While the survey results for each use case are fascinating, those surrounding eDiscovery are particularly illuminating on the nature and extent of how in-house legal departments are using analytical tools.
According to the survey report, a majority of legal departments (56%) reported that they were using data analytics to address eDiscovery obligations. While not totally surprising given that the legal profession has been doing eDiscovery work for well over a decade now, the report confirms that data analytics have become a critical component for basic eDiscovery processes. Indeed, the report indicates that the top three uses for data analytics in eDiscovery are culling (72.4%), early case assessment (72.4%), and relevancy review (71.1%).
Beyond those metrics, the report reflects increasing reliance on analytics for review prioritization (64.5%) and fact finding (55.3%). Such a trend is noteworthy since it suggests that legal professionals are using analytical tools in a more intelligent and strategic fashion, i.e., to identify the key documents in a particular matter.
Another key metric is the heavily reported use (56.6%) of analytics for privilege reviews. That trend implies that in-house legal departments are getting beyond the exclusive use of search terms and are turning to a variety of tools, including concept search, email threading, near duplicate identification, and predictive coding, to more effectively isolate privileged content. While advanced analytics certainly won’t obviate the need to obtain orders under Federal Rule of Evidence 502(d), they may very well lessen the number of privileged documents inadvertently produced in discovery.
What The Survey Means for Lawyers
The CTRL survey report is significant since it clarifies that the use of advanced analytics is an integral aspect of in-house legal departments. Irrespective of the number of reported cases involving predictive coding, the report confirms that the use of analytical tools is here to stay. Indeed, 71% of legal departments indicated that their spending on analytics for eDiscovery would increase or stay the same next year. Such a prospect spotlights the importance for lawyers – both in-house and outside counsel –to become proficient in the use of such tools. Beyond the requirement of technological competence in an increasing number of jurisdictions, rainmaking and career survival for many lawyers may ultimately turn on understanding the ins and outs of advanced analytics.