Are You Ready for e-Discovery 2.0?
In recent months there has been a furor over new disruptive technologies entering the e-discovery marketplace. Predictive coding, non-linear review workflows and machine assisted learning are changing the landscape and practice of law in the e-discovery arena and being discussed ad nauseam in legal periodicals, blogs and conferences. But amid all the discussion one key component has been overlooked: what will the new e-discovery practitioner look like in this new technology driven ESI landscape? As the suite of services in e-discovery evolves to include technology assisted non-linear review a new breed of skilled knowledge workers are emerging.
Big Data & Evolution of e-Discovery 2.0
Since the first attorneys asked the fateful question “do you think email is discoverable?” about a decade ago, Electronically Stored Information (ESI) has been doubling or tripling every 18-24 months with 85% of that data residing in business domains. The IDC Digital Universe study, “Extracting Value from Chaos” forecasts 1.8 zettabytes (1.8 trillion gigabytes) will be created and replicated in 2011 at a rate faster than predicted by Moore’s law. As corporations and law firms have scrambled to wrap their arms around the ever-increasing mountain of data associated with some litigation and government investigations, the cost associated with the discovery process has skyrocketed rendering certain matters cost prohibitive. Rather than focusing on the merits of a case, attorneys have had to spend exorbitant amounts of money to preserve, collect, search and ultimately produce the proverbial needle in a haystack from first megabytes, then terabytes and now petabytes of data. Linear review is still defensible and ultimately reliable, but as big data dominates aspects of the market, e-discovery practitioners need to evolve to embrace technology assisted review as well.
The first plan of attack was one of brute force; companies hired legions of contract attorneys to painstakingly review every document for relevance, privilege and key issues. Over time the multi-million dollar cost of collecting, processing, hosting and producing the relevant data has been commoditized and reigned in, but the bulk of the cost for an electronic discovery matter, the cost for the human capital has remained a heavy burden. The introduction of enterprise cloud computing and now with the iCloud personal cloud computing, the volume of data is only trending upwards. The eyes on every document linear review process and its brute force approach is simply cost prohibitive when dealing with the new Big Data infiltrating the e-discovery Market.
The Paradigm Shift: Machine Learning and Big Data Analytics
They say that necessity breeds ingenuity, and this has certainly been the case with the e-discovery industry. Out of the morass of data technologies that facilitate early case assessment, advanced algorithms and machine assisted learning or front end semantic indexing have steadily cropped up as viable alternatives to traditional Boolean searching and brute force approaches to e-discovery . The new technology has the capacity to reshape the e-discovery game. If we are to believe the recent ruminations of Judge Peck, it also likely has blessing from the bench if used in a defensible workflow and in a reasonable manner when data volume necessitates its use.
It has also been convincingly argued at TREC and in several respected periodicals that, when performed with a defensible workflow, the precision and recall of computer assisted review has as good as (and at times better) human manual linear review in cases with a large volume of data and limited time frame for completion. Judge Peck went on to add that this new disruptive technology turns the traditional review process on its head. Instead of having only one option, “a factory of contract lawyers” culling document by document through their selection of documents to senior lawyers, the senior lawyers can now do the initial work using their skill and knowledge in iterative passes until the computer is sufficiently trained. This process does not eliminate the human component, it just ensures that human capital is used to evaluate most likely relevant data, perform quality control and work on the merits of the case.
The impact of this shift to technology assisted review has fundamentally changed the way review can be done. Case in point, Bennett Borden of Williams Mullen described the impact of nonlinear review to his practice in a recent article:
“Using the non-linear review model, which the reviewers dubbed “rabbit hunting” or “choose your own adventure reviewing,” the reviewers were set up on the data-set to pursue their topics however they chose. The 22 reviewers completed the review of 1.5 million documents in 124 hours (or roughly 15 working days). The daily average rate of the review was 318 documents per hour… the review was extraordinarily fast, efficient, and a quality control analysis found an error rate of less than 3 percent.” 
The evolution of e-Discovery and the birth of an ESI Maven
Technology is fundamentally changing the e-discovery process into an affirmative tool for case development and analysis, and is also redefining the roles of the key personnel involved in the entire EDRM. The need for temporary attorneys may wane but it will not vanish, rather early case assessment tools and advance technology must be merged with the existing intellectual capital possessed by these individuals. Simply having passed a bar and having a pulse will not suffice in the evolution of e-discovery 2.0, rather law firms and corporation need to invest in true knowledge workers. Specifically, highly skilled project managers, attorneys (both internal and hired on a temporary basis) and technologists who evaluate segments of documents at each phase of the document reviews and collection and collection process ensure that non-linear reviewing accurate contributes case development. We will also see an influx of experts previously not associated with the e-discovery arena entering the field; statisticians, process improvement experts and high level document review experts internally working at corporations, law firms and as permanent employees of legal staffing companies.
The streamlining of the review process with machine assisted review has a two-fold impact:
1) It will fundamentally reshape the pool of professionals involved in document review
2) It allows the attorneys managing the discovery process to focus more on strategic case-driven fact development in lieu of searching ceaselessly for the needle in a haystack to avoid sanctions.
The evolution of e-discovery 2.0 does not mean that the need for outsourced document review practitioners will cease, rather it means that the type of attorney in this role will possess a different skill set and fulfill a different function.
In this new e-discovery universe, those conducting document review will be brought back into the substantive practice of law and be required to possess a more robust understanding of technology, the case and the data set. Rather than the current approach of simply requiring a certain number of licensed practitioners to sit and click-through documents, law firms and corporations have the ability to engage smaller specialized teams of attorneys well versed in the advanced technologies discussed above and intimately familiar with the case, the data set and the entire case team.
Surviving and Thriving in e-Discovery 2.0
In the world of e-discovery 2.0 the sheer volume of data continues to drive innovation and is evolving to a level that requires a greater level of expertise from the practitioners involved. The innovation and efficiencies that are being developed to deal with the scale of data will only be as effective as the practitioners that are implementing the workflows. Contract attorneys need to consider seeking additional platform training, certification, and refine their skills to remain relevant in this new world and law firms. Corporations and litigation service providers likewise should look toward a future with more knowledgeable practitioners increasingly participating in and driving e-discovery matters. As in information technology, finance and industrial arenas is beginning to be a greater specialization of labor that has developed to keep pace with technology. To dovetail the existing linear conceptions of e-discovery with emerging technology and workflows companies should add a layer of program managers or liaisons serving as an interface between high level technical experts and the pre-existing brain trust.
These experts may add a level of cost to the law firms, corporations and service providers that seek out and/or train them initially, but the increased efficiency of culling documents, and expediting overall time and attorney power necessary for a review will far out pace that cost for the early adopters. Application of early case assessment, advanced search algorithms or machine assisted review boast overall culling rates that reduce the corpus of documents to be reviewed upwards of 85-90% compared to traditional keyword searches using Boolean structure, in this savings firms and corporations should look to adding personnel who can best leverage this technology. As matters with an increasing volume continue down the pipeline, implementing a workflow managed by a person or a team of experts leveraging technology will be the best way to remain competitive. This stratification, with a seamless level of overarching management has allowed those industries to increase efficiency and reduce cost without sacrificing the concepts, skills or quality that are essential for success.
Share Your Thoughts…
What do you think the e-discovery practitioner looks like in this new technology driven ESI landscape?
 DC Digital Universe Study, sponsored by EMC, June 2011, http://www.emc.com/about/news/press/2011/20110628-01.htm
 eDiscovery and Big Data Anlytics, http://ediscoveryconsulting.blogspot.com/2011/09/ediscovery-and-big-data-analytics.html
 Technology-Assisted Review in E-Discovery Can Be More Effective and More Efficient Than Exhaustive Manual Review, XVII RICH. J.L. & TECH. 11 (2011)
 The demise of linear review, http://www.williamsmullen.com/the-demise-of-linear-review-10-01-2010/
 Example: rather than hiring 15 $60-per-hour contract attorneys working 40 hours per week for six weeks at a total cost of $216,000, if you have a less risk adverse client your company could conduct the same review with four senior lawyers at $600 per hour for eight hours to train the seed documents for building the machine learning algorithm at a total labor cost of $19,200, saving $196,800 for the largest piece of cost in an e-discovery review.