Flexibility and adaptability are two of the more important traits of any highly successful legal professional. Those traits are rarely more in demand than right now, when growing data volumes mean we continue to see and solve new and different discovery challenges—many of which would have seemed impossible or too difficult to resolve just a few years ago.
As the average case size continues to grow, and the definition of “unduly burdensome” continues to develop, a premium has been placed on discovery strategies that are both defensible and cost-effective. Both new and existing technologies have become the key to addressing this evolution.
Step 1: Build a strong foundation.
A strong understanding of technology provides the foundation for innovation. A deep understanding of what’s available, when combined with flexibility, creativity, and an understanding of how the tools can work together, opens up a world of problem-solving possibilities. Armed with technical know-how and a drive to think outside the box, you are not limited by when and how these tools are most commonly used. Playing off the strengths and weaknesses of each tool and its ability to solve a problem can fill in gaps and increase efficiency.
Analytics as a technology was primarily introduced in e-discovery to solve the challenge of growing data sizes and associated, often prohibitively high, review costs. Applications such as clustering, categorization, technology-assisted review (TAR), email threading, and near-duplicate detection are now implemented on a daily basis to do just that. Growing acceptance means these tools often come with templated workflows that make getting started much easier. Starting small by putting these workflows into practice can help you build enough expertise to identify new opportunities for tackling your most complex projects with customized, combined, and creative workflows ideated with your unique goals in mind.
Step 2: Explore each analytics feature on its own merit.
Did you know the same technology supports both categorization and technology-assisted review? In both use cases, the technology is trained by users’ decisions to organize documents based on content. There are, however, differences in the training and quality control methodologies that allow each of these options to be more applicable in certain situations. Categorization might be a useful exercise for QC purposes when it’s performed in conjunction with a privilege review, for instance, while a TAR project can help accelerate the earliest stages of reviewing your data.
These differences in approach exemplify the ability to use the same underlying technology to solve various challenges. As you begin implementing analytics in your projects, get to know each feature and how it can benefit different use cases. What makes email threading valuable? What about clustering?
Step 3: Explore analytics features in different combinations and stages.
The beauty of analytics is that it is more a method than a tool. “The method of logical analysis” is how Merriam-Webster defines analytics, and its very nature makes it ideal for flexing and adapting to new use cases.
Once you’ve gotten comfortable with each analytics tool and the benefits it can present, you’ll have the confidence to start combining features in the same workflows to see how one tool’s results can improve another’s. Maybe just one feature will do the job for the small case that just came through your door. But maybe a combination of features is required for the next big case. For example, how does email threading cut down noise in a TAR project? How might foreign language identification make your team’s approach to clustering results of international data more efficient? The possibilities are endless.
With a little education and expert guidance, you can apply analytics tools creatively, without limiting your team to a single, go-to approach that may not be up to snuff for solving your most complex e-discovery challenges.
You can even apply analytics beyond the scope of initial review. We at Altep will be releasing a series of posts on the Altep blog discussing how to use analytics in e-discovery in new and exciting ways, leading up to a thought-provoking webinar discussing its use in the presentation phase of the EDRM. There has been little focus on leveraging analytics during this phase, though it can benefit teams faced with organizing sets of produced data while courtroom deadlines loom.
By creatively leveraging analytics during deposition and trial, you can considerably cut time and costs during this phase, as well as locate key information more quickly to increase your chances for success. The techniques at your disposal are certainly a departure from traditional strategies, but they are tested and proven solutions that work.
Sara Skeens is a consultant for advanced review and analytics with Altep’s litigation consulting group. She has over 10 years of experience providing solutions and workflow guidance to case teams and enterprise clients in the areas of preservation, review, analysis, production, and presentation. She is a Relativity Certified Expert and has held positions in law firms, government, and providers working in both criminal and civil litigation, as well as investigations.
Joshua Tolles is a senior consultant for advanced review and analytics with Altep’s litigation consulting group. In this role, he provides process, solutions, and workflow guidance to case teams and enterprise clients in the areas of preservation, collections, processing, review, analysis, and production. Joshua is a licensed attorney in Washington State and the District of Columbia, and a Relativity Certified Expert.
Also available via the kCura blog: 3 Steps to More Creative e-Discovery Analytics