q2xaWithin the last year, predictive coding and technology assisted review transitioned from mere talking points to active segments of the document review process. In Q2 2013, The Cowen Group surveyed 63 executives of Am Law 200 firms on this issue through our Q2 2013 Critical Trends Survey Report. With the quantity of cases using predictive coding on the rise, partners and litigation support management will be required to make decisions including, but not limited to, (i) which software is the most efficient for their needs, (ii) the importance of training and certifications versus experiential knowledge and (iii) who is best suited to manage these cases? While many players in the industry anticipate an expansion in the quantity of cases using predictive coding, litigation support management’s opinions ultimately vary on the expected payoff from training and internally developing Subject Matter Experts (“SMEs”).

On the one hand, at least some technical training will be necessary, so why not make a wholehearted investment and “create” SMEs? On the other hand, which tool is worth the investment and how will certified professionals perform on real cases with real world data? Furthermore, will predictive coding SMEs require legal expertise in addition to advanced technical skills? We’ve observed a difference of opinion between the pundits on the technology side versus what the traditional lawyers believe; however, many of the largest players in the industry feel that the strongest predictive coding SME encompasses the technical acumen of an eDiscovery expert and the case law knowledge of a lawyer. The SME needs to comprehend the math, the algorithms and the coding while also understanding which documents are relevant to the case matter. They must be able to coherently react to the feedback, knowing when the case requires more documents to be reviewed or when the review can end. While this is ideal, the present reality of successful predictive coding will most likely call for a team effort from both lawyers and eDiscovery managers. Many in the industry recognize that this may be easier said than done. For now, successful predictive coding implementation rests on attention to detail and open lines of communication.

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