Historical Essays

Migrated from eDJGroupInc.com. Author: Amber Scorah. Published: 2012-02-14 12:32:56  Policies are like laws… we need them, they give structure.  But policies tend to be reactive, just like law enforcement.Take, for example, the war on drugs.  Passing drug laws alone isn’t enough – we have programs in schools to teach kids, communities offer programs to help people get off drugs.  This is, of course, because [...]

Migrated from eDJGroupInc.com. Author: Mikki Tomlinson. Published: 2012-02-15 08:55:34  As hinted to be forthcoming during the LTNY Man vs. Machine: The Promise/Challenge of Predictive Coding & Other Disruptive Technologies session, The Honorable Andrew J. Peck, United States Magistrate Judge for the Southern District of New York, is now on record on the topic of Predictive Coding – Technology Assisted Review (“PC-TAR”) in the Da [...]

Migrated from eDJGroupInc.com. Author: . Published: 2012-02-17 09:08:26  Most of the conversation these days in the eDiscovery arena has been around Predictive Coding, the Cloud, and Big Data topics, so I wanted to bring us back to one of the basic issues in the eDiscovery marketplace…pricing.The Predictive Coding/Technology Assisted Review (PC-TAR) makes the world a bit fuzzier as it blends the processing and review [...]

With budget constraints and reduced internal staffing putting pressure on in-house corporate departments that analyze and make decisions on an ever-increasing pool of potentially discoverable material, creating a solid in-house eDiscovery team is a big challenge.Even so, there are ways to leverage your current corporate organization structure to create a solid in-house eDiscovery team. In part one of this two part article, we offer some good strategies for getting multi-department collaboration, and advice on how to determine which eDiscovery procedures are more effectively and economically handled internally.

Matters like Pippins v. KPMG LLP, ---F.R.D.---, 2012 WL 370321 (S.D.N.Y. Feb. 3, 2012) provide a unique opportunity to discuss eDiscovery, and many of the considerations involved. How do the seven Zubulake factors apply? How much data constitutes an undue burden? Can predictive coding be trusted, and at what point is human interaction required for review? Who is going to do all of the work? These are only a few of the questions brought to light by this matter. Though the Pippins case alone does not resolve any of these questions, it does offer a platform for discussion.

Big Data is a new paradigm for many professionals, and it is relevant for eDiscovery professionals as it is a new artifact to consider when pondering the collection and discoverability of content. Oracle defines Big Data as an aggregation of data from three sources, which include: traditional (structured data), sensory (log data, metadata), and social (social media)1 data. Big Data is often stored in non-relational, distributed databases using new technology paradigms, such as NoSQL (Not only Structured Query Language).