Learn critical concepts in
E-Discovery tool selection.
Disruptive Technology, Savvy Clients and Cost Pressures are Changing the e-Discovery Game
Excerpt - Download Full Article for More:
Leveraging technological advancements to minimize the cost and maximize the accuracy of human analysis required for large data reviews is the next step in the world of electronic discovery. Rapidly expanding data volumes and skyrocketing costs are driving this evolution. It is increasingly in counsel’s interest to make use of technology to find the most cost-efficient and accurate ways to review large volumes of data.
Service providers, counsel and cost-conscious clients are looking to nonlinear methods for relief from this burgeoning volume of data and the costs associated with it. When used effectively, emerging technologies can create efficiencies in prereview categorization of data, expand the scope of content analysis and accelerate the speed and accuracy of review. Rather than a one-size-fits-all approach to e-discovery, there is now a spectrum of alternative technologies and processes that can be tailored to the case at hand.
The exponential growth of electronically stored information (ESI) during the past decade has forced organizations to reevaluate how e-discovery is handled. Rather than focusing their efforts on the merits of a case, companies and their counsel have been forced to spend exorbitant amounts of money and time to preserve, collect, identify and produce the proverbial needle in a haystack from first megabytes, terabytes and now petabytes of data.
ESI has been doubling or tripling every 18 to 24 months and 85 percent resides in business domains. International Data Corp. estimated that 1.8 zettabytes (1.8 trillion gigabytes), contained within 500 quadrillion files, would be created and replicated in 2011. This means, as of today, there are more bits of ESI than there are stars in the known universe. See John Gantz & David Reinsel, "Extracting Value from Chaos" (2011). http://idcdocserv.com/1142. This tidal wave of data and the cost associated with it is driving innovations in nonlinear review.