Welcome to part three of our Creative Analytics series. Part one provided a suggested road-map for getting more comfortable with analytics tools, and exploring more creative uses. In part two, we discussed some of the challenges common to the presentation phase of the EDRM, which require us to look for creative solutions. This brings us to part three – the solutions. In this post we will provide more detail on a few key tools and techniques that we deploy to overcome those common challenges. This final installment is intended to serve as the closing primer for our co-hosted webinar with kCura that will be taking place tomorrow, Wednesday July 13th – Leveraging Analytics for Depo & Trial Prep. Please tune in then where we will put things into a more visual, workflow-based perspective.
Narrowing The Field – Making The Most of Your Time
Deposition and trial preparations typically begin as production review ends (in some cases the two processes can run over each other as well, adding additional complications). It is here that you are usually faced with making sense of two distinct data sets – your produced documents and productions received. Traditional fact finding efforts here involve simply leveraging reviewer coding and supplemental keyword searches. These techniques are a great place to start, but can be highly time inefficient and almost always suffer in terms of completeness.
One helpful early approach here is to limit your fact finding data set to only unique content as much as possible. Analyzing duplicate content is a painful drain on resources. Whether a false keyword hit, or a true hot document, you generally only need one good look within the four corners to assess its value. This can be a bit counter intuitive, especially if you have been working with family coding guidelines during your review efforts. However, it is best to start small when time is of the essence. Identify key individual documents as quickly as possible and then build context around those items later.
Read more at the Altep blog: Creative Analytics – Part 3: The Toolbox