As we approach the end of 2015 it seems like the more things change, the more they stay the same. Corporations, law firms, and legal service providers are still struggling to contain the growing costs associated with eDiscovery. Early Case Assessment (ECA), which was all the rage and heavily touted nearly a decade ago is again top of mind and re-emerging as the way to control costs and deliver predictability.
There is no shortage of information on ECA, a search for “Early Case Assessment” in Google returns 54,300 results. Yet today, most still don’t seem to understand what ECA truly involves. In fact, if you were to ask ten industry experts at anyone of the legal conferences today to explain to you what ECA is you would receive ten different answers.
One reason for this confusion is marketing hype that drove software providers to package their eDiscovery technology as ECA solutions. This created confusion in the market and led most to improperly believe that ECA was and is still today a technology. Thus “ECA” for the past several years has been relegated to an exercise in filtering data and providing a series of automated reports.
However, true ECA is a process that leverages a combination of technology (client facing) and people to develop a solid foundation from which to make strategic decisions at the inception of a matter allowing a legal team to plan for and zero in on those activities that will have the greatest degree of influence over the outcome.
Various studies have shown that ECA, if performed correctly, not only reduces costs, but also significantly increases the chances of achieving the most favorable outcome possible.
The Financial Crisis and its Impact on ECA
The financial crisis in 2008 hit the legal industry especially hard, reducing the budgets for legal departments and in turn forcing law firms and legal services providers to become more competitive in how they delivered and charged organizations for their services.
Corporations were no longer willing to pay law firm associates hundreds of dollars to perform document review, and engaged their own document review providers who offered this service at much lower rates. Equipped with a preferred provider to perform document review, many corporations didn’t seek out discovery expertise through outside counsel either, and sought to delay paying for outside counsel at all until it was absolutely necessary. At this same time, corporations began to take control of the costs of discovery by bringing technology and services in-house, seeking to “in-source” many of the steps of the EDRM. All of these factors combined with a commoditization of data processing services and dramatic reductions in the cost to load and index documents for review.
This led to a “rush to review” phenomenon, which remains the current state of discovery today. Who can blame folks? They were getting much better deals then they were traditionally used to, and they had cheap document review services available to cull through mountains of data, and low costs to process it. So if they sent out more data for review than they needed to, they were still ultimately paying less and seeing reductions in their legal spend from pre-recession levels.
This “rush to review” mentality led technology vendors to change their marketing and R&D focus away from ECA, and they instead invested in machine learning and predictive analytics to make document review more efficient. This emphasis was further supported by the famous 2012 RAND study, which found that $0.73 of every dollar spent on eDiscovery was being spent on review.
The Rush to Review & Machine Learning
First out of the gates was Recommind with their “Predictive Coding” solution, but it didn’t take long for others to follow. The methodology and debates on how best to control costs between technology providers, law firms, and industry experts centered on which approach to machine learning was best suited for the review phase. Little attention if any was dedicated to the pre-review process of ECA and the downstream benefits it could have in informing and enhancing the machine learning process being developed and applied to review.
Now, almost 8 years later, people are starting to realize that given the explosion in ESI volumes, a “rush to review” approach is not viable in the long term. Unfortunately, costs are still rising even though technology has greatly improved review efficiencies, and even though the cost of manual review labor has fallen dramatically. Moreover, even though the application of analytics to review has shown immense benefits, the adoption rate has not been nearly as high as some would have expected.
Why? Could it be that corporations and attorneys alike are still not sold on the full benefits of technology-assisted vs. manual review? Does the legal indsutry lack experienced attorneys who understand how to use technology assisted review correctly? Has the industry been its own worst enemy by arguing which approach is best?
The Value of incorporating true ECA & Machine Learning in Review
As the industry looks ahead to 2016, it is important to ensure that we properly educate organizations on what true ECA is and the benefits of it.
Organizations that are able to leverage a streamlined ECA process in conjunction with some form of machine learning will prove to be the ultimate winners. This should result in sending less but more relevant data to review, saving law firms and corporations both time and money.
“Rapidly identifying relevant documents, and ultimately finding the key documents, are vital to developing an effective case strategy and providing timely and accurate counsel to clients.”1
When it comes to litigation, knowledge isn’t just power, it’s your responsibility. The earlier you can gather knowledge about your case, the better you’ll be positioned to make appropriate strategy decisions. A well-defined ECA process combined with the right technology is a winning strategy and one that should resonate on many levels.
1Basile, Bobbi, HBR Consulting: Law Firm E-Discovery Strategy Survey (April 2015 )