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eDiscovery Glossary 2.0

Navigate the next Generation of eDiscovery

There’s a lot of information and “new” terms swirling around the eDiscovery world these days. It can be difficult to decipher the true meaning behind the terminology… and even more difficult to determine what is a relevant legal technology solution vs. industry and vendor hype. This eDiscovery glossary aims to provide a neutral information resource to define eDiscovery 2.0 terms. If you’d like a basic foundation of eDiscovery terms and definitions, take a look at our Legal Document Review and eDiscovery Glossary.


Computer Assisted Review

A wide blanket term used to describe any methodologies leveraging technology to enhance the effectiveness of human document review. It is the integration of conceptual search tools into document review platforms. Other terms often used interchangeably with computer assisted review include technology assisted review, automated review, and computer informed review have the same meaning as computer assisted review.

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Concept Clustering

Concept clustering falls under the umbrella of predictive analytics, is a form of unsupervised machine learning that may use statistical data analysis or front end semantic indexing to group the most highly relevant or frequent concepts within a data set.  This methodology is applied both as an early case assessment tool and as a review workflow and prioritization resource.

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eDiscovery 2.0

The next generation of software products, methodologies, and analytics as applied to the collection and analysis of electronically stored information (ESI). Much like the commonly used term Web 2.0, eDiscovery 2.0 centers on the continuing advancements of technology and collaboration.

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Information Governance

A multidisciplinary approach to managing enterprise information to support regulatory, legal, risk and operational requirements. Information Governance is not a new concept, but its application to eDiscovery continues to evolve as collective industry best practices are established.

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Predictive Analytics

Predictive Analytics encompasses a variety of statistical techniques from modeling, machine learning, data mining and application of advanced algorithms to analyze current and historical facts and make predictions about future events.  In terms of eDiscovery, the above methods are applied to raw data and combined with human decision making to index, prioritize or even fully review a data set.  This process diverges from the traditional keyword or Boolean based applications used in traditional linear review. The term predictive coding is often used interchangeably with predictive analytics, but only applies to cases where the predictive analytic process is used to make coding decisions on the data set.

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Technology Optimized Linear Review

Technologies, processes and workflow applied to traditional linear review that aids speed and efficiency with advanced searches, clustering, searching for concepts, and de-duplicating data.

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