Posted on: July 14, 2016in Blog
Pre-Review Analysis and its Role in the ECA Workflow
kCura’s recent release of its Relativity 9.4 has effectively refocused the eDiscovery gaze back on early case assessment (ECA). Yet while the attention may be on ECA, there is still some confusion as to what ECA really means.
The Difference Between ECA and EDA
For some, ECA is a process by which legal teams evaluate matters, performing analyses that help them to model cost and risk factors associated with defending, prosecuting or settling a matter. For others, ECA is a more defined protocol for investigating a specific universe of data and developing plans for collection, culling, and processing in preparation for document review. Sometimes the latter process is called early data assessment (EDA). EDA is often a macro-level analysis of a particular data set. An EDA process looks at data from several custodians and aims to gather the relevant data from mail stores, share drives, workstations, and other locations where people store potentially relevant ESI. This data then gets collected, filtered by date and key terms and forwarded to review with little consideration for what is actually in the resultant data set.
Whatever we call it, a lot of time is invested in ECA and/or EDA with the hopes that the resultant data is both reliable and relevant. Yet, even with this effort, the percentage of produced files for most document reviews continues to range between 15 to 30 percent of total documents reviewed. This, of course, means that many non-responsive and non-reviewable files are still being pushed out for review. With the high volume of non-responsive and non-reviewable files found in most eDiscovery review databases, even after EDA, new approaches are needed to reduce that population and reduce the cost of document review. This is where Pre-Review Analysis (PRA) comes in.
What is Pre-Review Analysis (PRA)?
PRA is a focused, detailed approach to data reduction performed, after processing and before review. This approach is based on deep analysis instead of general assessment, and all the major review platforms offer the tools needed to perform this level analysis. PRA is different from ECA and EDA in that the intent of PRA to learn specific information about the content contained in the review set. While ECA focuses on the matter as a whole, and EDA targets specific file types, keywords, date ranges and other broad categories of materials, PRA is designed to help teams understand what’s in the review set and how to best leverage that understanding to enhance or prioritize the review.
With PRA, case teams can identify processing problems before they become review problems. PRA allows review leads the ability to organize data by communication pattern, segregating internal emails from outward facing ones. You can separate calendar items, appointments, meeting responses, and contacts which may or may not be relevant based on the assessments already made. With the addition of analytics, PRA, can winnow down a review set based on email threads and near duplicate analyses. Concept searches, keyword expansion, and cluster visualization can tease patterns out of data that a simple linear review cannot. When combined together, all these approaches can help review teams limit the review set and expedite the time to production while reducing costs.
Given the benefits of less data, quicker productions, and lower costs of review, it is surprising that PRA hasn’t become part of every review. Among the challenges facing the adoption of Pre-Review Analysis is the fact that by the time the data has been processed, case teams are chomping at the bit to get the reviewers out of the gate. Time is of the essence and dedicating even 24 – 48 hours for PRA at this juncture seems counter-intuitive for most attorneys. They see the data in the workspace and know that the clock is ticking. What they don’t see is the ROI of PRA and how a little time up front can shave days or weeks off the overall review.
When leveraging the techniques of PRA, a recent case containing 195,000 emails was reduced to 400 total files to review. In another example, PRA helped one review team to focus their review set of 48,000 files down to 26,000. PRA is not just a data reduction tool, but can also be used to identify data problems. In one collection PRA helped identify the presence enterprise vault, email short-cuts before review, rather than during. The shortcuts were segregated from the collection and recollected, saving significant time from being wasted on review. These are just a few examples of how PRA can be leveraged to reduce time and labor costs in document review.
Combining Your ECA, EDA and PRA Workflows
It’s not enough to just define a data universe through EDA; it’s equally important to understand what’s in that universe before you begin the review. While the notion of ECA is back in the limelight, now is the time to capitalize on the amazing tools available in today’s modern review platforms. The ability to analyze data sets, identify non-relevant materials, apply focus to communication patterns, and overlay search results to those patterns makes traditional document review seem burdensome at best. By taking a brief pause between processing and review, Pre-Review Analysis allows review teams to refine and prioritize review data ensuing that the most relevant and production worthy documents are pushed to the top of the pile.
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