Much can be done to empower e-discovery users and legal teams to take advantage of e-discovery analytics within the platforms they are already using.
Despite growing data volumes, evolving data types and increasing e-discovery case complexities, e-discovery analytics technologies still lack utilization and awareness across the legal field. As an industry, we see numerous roadblocks to analytics adoption. They range across the resistance to change, uncertainty about defensibility, lack of knowledge about what analytics are and perceived (or in some cases actual) difficulty of use for certain advanced functions.
These roadblocks are becoming a serious hindrance to e-discovery teams, for both in-house counsels under pressure to reduce costs and law firms struggling to remain competitive and relevant in an evolving market. Unfortunately, utilization isn’t likely to increase in a meaningful way until counsel understands the scope of analytics tools and methods, technology providers make them easy to use, and proper education and training are provided.
Despite the widespread perception that analytics are gaining momentum, a recent study of e-discovery software users confirmed that adoption lags where experts believe it should be. This article will share key findings of the e-discovery study and outline the most critical factors e-discovery buyers must consider to address barriers and stimulate adoption.
The survey, executed by EMC Research on behalf of FTI and Ringtail, revealed the following:
Fifty per cent of users indicate they use analytics technologies less than a couple of times per year.
Predictive coding is used on a “regular or semiregular” basis by 54 per cent of respondents, while 53 per cent use content analytics and 10 per cent use social network analytics.
When a matter grows to the point where a legal team’s perceived internal resources can no longer handle it, 57 per cent said they turn to a service provider to conduct the review.
Analytic technologies such as predictive coding, clustering, content analytics and social network analytics (a form of AI) are designed to take the data from large and complex e-discovery matters and organize, visualize, in some cases reduce, and review it so that it becomes more manageable for legal teams. However, when analytics technologies could be called into action to reduce the size and complexities of a matter, 50 per cent of users are instead turning to service providers to provide assistance through a combination of technology and manpower.
Some software platforms do not include built-in analytic capabilities, offering a network of applications that can be added to a standard platform to enhance the baseline technology. While the flexibility to customize a platform gives users greater control over their technology stack, 95 per cent of users stated they did not use or were not aware of using any third-party analytics applications with their software platform. This indicates that most work with only the standard platform or remain unaware of enhancements made by others in their organization.
So, what can be done to empower e-discovery users and legal teams to take advantage of e-discovery analytics within the platforms they are already using?
Analytics should be easy to access: The more steps a user is asked to take to access and deploy analytics, the less likely the analytics will be utilized. To the extent, platforms can include analytics as part of the core technology platform and workflow, barriers to usage will be reduced. Another deterrent is when users are asked to build and customize their analytics application. While some users appreciate the flexibility, many will seek pre-configuration for out-of-the-box usage. Ideally, both should be offered so users can become familiarized with the analytics technology before advancing to a point when they can build upon and customize the solution.
Analytics should be easy to use: The number one response, by a ratio of 3:1, of why users have selected the e-discovery platform they utilize is the perceived ease of use. By the same proportion, users opt to not choose a platform to its perceived difficulty of use. While selecting a platform that is easy to use may seem obvious, it may be easier said than done. Emphasis should be placed on usability, with time spent on usability testing to ensure simplicity.
Education: What people understand and are familiar with will impact perceived ease of use. Providing education and teaching users best practices on where and how to deploy analytics can greatly increase adoption and optimise usage. Education can come from certification programs, webinars, tutorials, on-site training and product documentation. The key is recognizing that people learn in many ways, thus offering multiple learning paths for them to choose. Information should also be available on-demand, so materials can be readily accessed as questions arise.