Our goal continues to be to provide you with relevant and helpful material in The Discovery Update that you will use in your practice or will forward to other legal professionals.
In this issue, our feature article, “Best Practices in e-Discovery for Handling Unreviewed Client Data,” shares best practices in handling large stores of client data in e-discovery. We hope this article gives you new information about managing your e-discovery process or is a confirmation of the practices you already have in place.
As a reminder, there is a “What You Might Have Missed” section in the sidebar to the right which includes informative articles from past issues of The Discovery Update that you may have missed previously or that you would find helpful again.
As always, we invite you to send us any articles or tips you may have regarding the discovery process so we can share them with our readers.
Best Practices in e-Discovery for Handling Unreviewed Client Data
By Ralph Losey
Big data security, hackers, and data breaches are critical problems facing the world today, including the legal profession. That is why I have focused on development of best practices for law firms to handle large stores of client data in e-discovery. The best practice I have come up with is simple. Do not do it. Outsource.
Attorneys should only handle evidence. Law firms should not take possession of large, unprocessed, unreviewed stores of client data, the contents of which are typically unknown. They should not even touch it. They should stay out of the chain of custody. Instead, lawyers should rely on professional data hosting vendors that have special expertise and facilities designed for data security. In today’s world, rife as it is with hackers and data breaches, hosting is a too dangerous and complex business for law firms. The best practice is to delegate to security professionals the hosting of large stores of unreviewed client data. Read Full Article
Predictive Coding Semantics: Step Out of the Rain!
By Brian Meegan
A few years ago, we wrote about predictive coding going mainstream; shortly thereafter we wrote a seriesdebunking the most common myths about predictive coding; then, we even went so far as to break downpredictive coding lifecycles (actually, we did that twice!), explained how to maximize training for machine learning, and taught a short lesson about the most commonly-used stats for evaluating your machine’s work. So, to make a long story short, we’re pretty big on using predictive coding as part of a search methodology, and we’re doing everything we can to demystify the process so that organizations can use this technology to increase their ediscovery efficiency and, in turn, save a significant amount of money. If you don’t believe me, check out one of our case studies illustrating the myriad benefits of employing predictive coding for search, analysis, and review. Read Full Article