New Generation eDiscovery Software Increases Knowledge Worker Productivity and Enables Defensible Data Destruction

In today’s information based world economy, Electronically Stored Information (ESI) is the new lifeblood that enterprises use to power mission-critical operations and drive strategic business decisions. Stored in multiple geographic locations across the world in different file formats across multiple technologies and growing exponentially every day, management of ESI has become a major operational challenge for many enterprises worldwide.  As the volume of ESI continues to grow, many enterprises have become overwhelmed and are no longer able to effectively identify, collect, analyze and produce the relevant ESI their knowledge workers require to fulfill legal and governance requirements and drive business success.
The answer is for these enterprises is to systematically, defensibly, cost effectively and permanently reduce the amount of ESI that knowledge workers have to deal with and in turn make them more productive managing the remaining ESI.  When applying this objective to information governance and more specifically eDiscovery, enterprises are required by law to develop and follow a defensible data reduction policy that will ensure potentially relevant ESI is not destroyed.  In today’s distributed global enterprise with ESI potentially stored across multiple geographic locations in different file formats across multiple technologies, this is easier said than done.
Traditional Early Case Assessment (ECA)
Over the last five years, enterprises have traditionally utilized various independent and non-integrated eDiscovery software solutions and followed a broad waterfall methodology to move ESI through the eDiscovery lifecycle to fulfill their legal requirements to produce ESI for litigation.  This traditional approach calls for the enterprise to initially identify, collect and physically hold a large amount of ESI based on a broad set of search criteria such as employee names, dates and keywords potentially relevant to the litigation.  As indicated, in today’s distributed global enterprise with ESI potentially stored across multiple geographic locations in different file formats across multiple technologies, this is easier said than done.
The traditional approach then requires the enterprise to load the initially collected ESI into an Early Case Assessment (ECA) solution to cull this initial collection set by eliminating duplicate documents and systems files and perform cursory analysis referred to as a first pass review to further reduce the volume of ESI.   The traditional approach then requires the enterprise to load the remaining ESI into a document review solution for litigators to manually review and determine which documents are required to support the litigation.
Although the use of ECA solutions has greatly improved the efficiency of the eDiscovery process and reduced the overall cost of eDiscovery, having to move ESI between various eDiscovery tools is inefficient and still results in 70% of the cost of eDiscovery residing in document review because this approach process does not adequately reduce the amount of ESI that has to be manually reviewed by litigators.
Further, the traditional eDiscovery approach with its waterfall methodology lacks the ability to efficiently support analysis and multiple collections over time for the same legal matter, enable analysis and collection of the same ESI for multiple legal matters, and does little to address the question of systematic, defensible, cost effective and permanent data reduction.
Computer Assisted Predictive Intelligence (CAPI)
In an effort to initially reduce the cost of document review, eDiscovery tool vendors have introduced computer assisted technology into the ECA and review process in the form of Technology Assisted Review (TAR) and Predictive Coding solutions.  These solutions utilize standard computer learning technology and predictive mathematics to automatically review and identify relevant documents at a verifiable rate that is statistically similar to that of human reviewers resulting in increases in productivity and significant cost reductions.  
Most eDiscovery tool vendors have initially limited the application of TAR and Predictive Coding to ECA and document review.  As a result, enterprises must complete the initial time consuming and costly ESI identification, hold and collection process with traditional methods and technology. In addition, the current use of TAR and Predictive Coding technology does little to address the question of systematic, defensible, cost effective and permanent data reduction.
Early Analysis and Identification is the Key
The most promising approach to reducing the overall cost of eDiscovery and enabling the systematic, defensible, cost effective and permanent reduction of data is to expand the use of Computer Assisted Predictive Intelligence throughout the entire eDiscovery lifecycle.  Using CAPI during initial identification and before the collection and hold phase is known as “early identification” or “identification in the wild”.  The use of CAPI for early identification should enable legal knowledge workers to iteratively develop and test multiple collection models with the following beneficial results:
  • Automatic identification of relevant ESI in a statistically significant and defensible manner
  • Better-informed legal strategies
  • Much smaller and more accurate collection and hold sets, reducing of the cost of document review and of overall eDiscovery costs.
  • More accurate estimates of downstream eDiscovery lifecycle costs
In addition, utilizing CAPI will also enable enterprises to support the systematic, defensible, cost effective and permanent reduction of data with statistically significant and verifiable conformity to their enterprise data disposal policies.
Summary
As the volume of ESI continues to grow, many enterprises knowledge workers have become overwhelmed and are no longer able to identify, collect, hold, analyze and produce the relevant ESI require to fulfill legal and governance requirements and drive business success.  What these enterprise knowledge workers require is an integrated software solution that enables them to systematically, defensibly, cost effectively and permanently reduce the amount of ESI that knowledge workers have to deal with and in turn make them more productive managing the remaining ESI.
Exterro’s Fusion® is one of the only fully integrated eDiscovery platforms on the market that enables litigation knowledge workers to gain full visibility and control over the entire eDiscovery lifecycle from ESI identification through production.  Its Web-based user portal provides a single point of view into and control of the entire eDiscovery process.  It seamlessly integrates and shares ESI with current enterprise IT infrastructure and other eDiscovery tools as may be required and provides comprehensive workflow management across the entire eDiscovery lifecycle supporting both Exterro and non-Exterro technology.  
Exterro’s newly released Predictive Intelligence module uniquely provides advanced machine intelligence across the entire eDiscovery lifecycle providing a more robust and comprehensive use of computer assisted eDiscovery.   As a truly integrated yet open eDiscovery platform that supports that entire eDiscovery lifecycle with advanced predictive intelligence, Exterro Fusion should be considered by any enterprise that has opted to bring eDiscovery and information governance in-house with the goal to support the systematic, defensible, cost effective and permanent reduc

tion of ESI and increase legal knowledge worker productivity for the remaining ESI.

About charles.skamser

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