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Developing A Clear Picture With Early Case Timelines

Getting a grasp of the strengths and weaknesses of one’s case early becomes more important and more challenging with substantial ESI. Creating early case timelines can help keep your case perspective accurate.

Assessing one’s case early is as important as ever. Keyword selections, used for discovery requests and Rule 26 Meet and Confer agreements, depend on accurate identification of the important case issues. If the critical issues of a case are not identified until later, then it will become necessary to request additional documents based on new keywords, or from different custodians. This can add substantial cost to discovery collections and tries the patience of opponents and potentially the court. At some point in the discovery process the cost of additional collections will shift to the requestor, or it simply will not be possible to identify new issues too late in the process. A related concern involves identifying potential claims and defenses early enough in the discovery process to be able to include them in the pleadings.

ESI intensive cases are even more difficult to assess early because distilling information about the case from large amounts of data can be expensive and is dependent on both knowing the issues to generate keywords — a sort of ‘catch-22’ – and being in possession of the data. If one is trying to get the important facts early in the case from the ESI (as opposed to witness interviews and case participants, etc.) then the review of ESI will be an iterative process of trying proposed keyword searches, manually reviewing a sample of the documents returned by the search results, and then revising search terms.

As part of the early case assessment process, counsel can identify what discovery is needed, identify key witnesses and data custodians, construct factual timelines, and determine the strengths and weaknesses of the case. The goal of this proactive approach is to develop an early strategy for the case, realistically evaluate the case, determine the cost and budget to prosecute/defend, and identify business practices that might be modified to minimize litigation exposure in the future.

This probably sounds like ‘mom and apple pie’ and is certainly not a new idea. But then why isn’t this done in every case? There are several real life barriers to doing a comprehensive early case assessment. One is the need for client support. Conducting an early case assessment will cost money and a client may view it as ‘over-working’ the case if they don’t understand the process and benefits. These benefits include a better early understanding of the case that can lead to opportunities for settlement and reducing unnecessary eDiscovery costs.

A more subtle objection is the concern with being the bearer of bad news. A legitimate worry is that the client may not be ready to hear bad news about the case and may be inclined to ‘shoot the messenger’. Some clients think they need a very gung ho ‘junkyard dog’ type of litigator and might interpret an early balanced analysis of case strengths and weaknesses as lack of confidence from their attorney. This problem is compounded when the litigator has been recently hired and does not have experience with the client. For these types of clients, some lawyers take the approach of letting the adverse facts of their case sink in over time. Eventually the client begins to accept the shortcomings in their case and are more inclined to settle.

The cost of the ‘wait and see’ approach of not doing early case assessment is greater today, and will continue to grow, because of increased volumes of ESI. Some cases may have so much potentially relevant ESI that the cost to review it may outweigh the benefits of litigating. In reality, a case in which the opposition has enough merit in their case to allow extensive eDiscovery justifies an early evaluation of that cost and raises the case’s settlement value. Also, not understanding one’s case early can greatly increase the cost of conducting eDiscovery by requiring repeated requests and reviews as key issues and keywords are not understood – and custodians and data repositories not identified – until relatively late in the case.

When constructing chronologies very early in the case, they may be developed from limited available sources: facts from initial client interviews, review of key documents forwarded by the client, and alleged facts from the opponent’s’ pleadings. However, even limited sources can begin to give a good picture of where the strengths and weaknesses of the case lie. Early case assessment tools, like those found in Lexbe eDiscovery Platform, can help you construct timelines at the earliest stages. Certainly all lawyers construct timelines as a case progresses towards trial, but there are advantages in doing it as early as possible in an environment that is integrated with the case data. The most important of which is that, as iterative ESI collections arrive, early case timelines can be updated automatically and in real time.

Early case timelines can also include facts that are needed to support the claim or defense based on anticipated jury instructions for the claims involved. These facts remain ‘orphaned’ until associated with one or more documents or portions of deposition testimony. The existence of these orphaned facts serve as red flags for evidence that needs to be developed during the discovery stage. Similarly tracking facts that one’s opponent needs to prove for his or her case can serve as focal points for developing opposing evidence and summary judgment motions for evidence that has not been successfully developed during discovery.

Timelines also serve as a good way to begin educating clients about the potential holes or weaknesses in their case. A client can begin to digest a truer picture of his or her case through periodic review of timelines showing positive and negative facts backed up with documentary evidence or deposition testimony. Such chronologies can also help the client to develop supporting evidence, both documentary and oral, by jogging the client’s memory for missing or contrary evidence. As the case develops and more ESI comes in, the issue timelines can be supplemented more easily. With functional case timelines, an attorney is also in a better position to quickly develop and defend summary judgment motions and to identify specific facts backed with evidence.

Understanding Precision and Recall

Technology assisted review is a powerful tool for controlling review costs and workflows. But, to maximize the benefits of TAR, we must be able to understand the results.

Predictive coding has, for years, promised to reduce the time and expense of increasingly large scale litigation reviews. For attorneys and project managers assessing different methodologies, it has been challenging to understand what evaluative metrics are relevant. F-scores are often inappropriately interpreted as measures of review quality when evaluating predictive coding results. But to get a better understanding of how an application of predictive coding has performed and to manage the defensibility of your review, the component elements of the f-score – precision and recall – should be reviewed. But how do precision and recall scores relate? And, more importantly, what do these results tell you about your production?

In the context of TAR and predictive coding, precision is a measure of how often an algorithm accurately predicts a document to be responsive. In other words, what percentage of the produced documents are actually responsive. A low precision score tells us that there were many documents produced that were not actually responsive, potentially an indication of over-delivery. A high precision score on its own doesn’t mean much, either. One could deliver just 10 documents to opposing counsel, and if all 10 were responsive, we would have 100% precision but we would have almost certainly failed to deliver a very significant percentage of the responsive documents in the collection.

PerfectRecallPerfectPrecision To give our precision score any context relative to the over-riding goal of predictive coding — to quickly and defensibly deliver responsive documents to opposing counsel — we need to look at recall. Recall is a measure of what percentage of the responsive documents in a data set have been classified correctly by the TAR/predictive coding algorithm. When recall is 100%, the algorithm has correctly identified all of the responsive documents in a collection. A low recall score indicates that the algorithm has incorrectly marked responsive documents as non-responsive.

LowRecallHighPrecisionSquare To get an idea of how a predictive coding application has performed we need to look at precision and recall relative to each other. Due to the fundamental limitations of predictive coding technology, it would be very difficult to ever achieve perfect precision and recall on a collection. There is ultimately going to be a trade-off between optimizing the two measures. To improve precision, that is to reduce the proportion of false positives, we are likely going to reduce true positives — recall — as well. Similarly, to improve recall, or reduce the proportion of false negatives, we are likely going to increase the percentage of false positives and negatively affect precision. Because of this interrelation, much of what can be understood about TAR results is obscured by just looking at the f-score and accepting the result if it exceeds some arbitrary measure. Evaluating precision and recall in relation to each other tells a much more detailed story about TAR results.

HighRecallLowPrecisionSquare Given what we know about recall scores, it may occur that predictive coding actually gives us an explicit measure of how many responsive documents we didn’t deliver. How can we look at predictive coding results that indicate 80% recall and not be entirely focused on the 20% of responsive documents that haven’t been produced? The answer is that 80% recall may be a far better result than if a massively more expensive manual review of the documents was performed, instead. Though this seems controversial, it is a notion shared by The Sedona Conference, TREC legal track, and the judges who have been approving TAR use.

Controlling eDiscovery Costs

With eDiscovery becoming increasingly typical and financially burdensome, every litigation professional is looking to keep costs down while still delivering high quality document reviews. This search for low costs, at least, has remained constant. What has changed rapidly is the amount of Electronically Stored Information (ESI) subject to discovery. As the amount of ESI created through normal business activity grows, the need to keep eDiscovery costs down and leverage best of breed technologies grows correspondingly. Let’s take a look at this explosion in ESI volume and how it affects eDiscovery costs.

The amount of ESI collected from employees for commercial litigation has grown by 35% annually. A recent report by Microsoft Corporation found that the average collection of data per individual custodian involved in litigation increased from 7 GBs (~0.5 Million pages) in 2008 to 17.5 GBs in 2011 (~0.9 Million pages). This shows an astounding 150% increase in just three years (35% a year, compounded).

BlogPostControlCosts This ESI explosion has a direct effect on the costs associated with eDiscovery. The industry standard prices for processing services are falling but not nearly fast enough to keep up with the exponential growth of ESI collected. The cost to process one GB of raw ESI (~50,000 pages) in 2006 was $1,800. This cost declined to $500 by 2011, showing a 72% decrease in 5 years (22.6%, compounded).

The data demonstrates an annual compound growth of collected ESI of 35% and an annual decrease in processing costs of only 22.6%. With discoverable data growth outpacing cost decreases by 12.4% annually, controlling eDiscovery costs is increasingly crucial. Finding and selecting a quality eDiscovery provider that develops scalable, technology driven solutions that push back against typical cost drivers should be the focus of every litigation professional faced with an eDiscovery challenge.

eDSG Poll Suggests Current eDiscovery Software is Too Expensive — We Agree!

An eDSG poll conducted back in April suggests that litigation professionals find current eDiscovery software too expensive, too slow, and they are dissapointed that the software doesn’t run in the cloud. We couldn’t agree more. Lexbe has focused on addressing these issues through an innovative operations architecture that takes advantage of the latest, highly scalable, and most secure computing technologies. The result is fast, affordable, cloud-based eDiscovery that gets the job done. Lexbe is easy to use, all features are included at no additional cost, and you can even get free native processing services when you host for 6 months. If you are also tired of slow, expensive, inefficient, and complicated eDiscovery software and services that are living in the past, learn more about how Lexbe eDiscovery Platform is changing the game by responding to these concerns.

Affordable Casemap Alternative: Case Analysis with Lexbe eDiscovery Platform

Casemap™ is a litigation fact and issue management application from LexisNexis™ designed to assist lawyers and staff in organizing facts and issues in a case to prepare for depositions and trial. Casemap requires local installation, licencing expenses, user fees, and is not offered as a native web-based application. Lexbe eDiscovery Platform, our web-based document management tool, includes robust case analysis functionality including fact and issue management, timelining, and more. Unlike Casemap, there are no additional charges associated with using these features and they are immediately available from any location when you sign up for Lexbe eDiscovery Platform.

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