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This entry was posted on Friday, February 1st, 2013 at 7:23 pm. It is filed under chronology, risk and tagged with information governance, privacy, risk, security. You can follow any responses to this entry through the RSS 2.0 feed.
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As lawyers, we hear a lot about the technological advances in e-discovery and information governance. How do you describe the current state of e-discovery from an opportunity and growth perspective, and how does this market opportunity impact the pulse rate of mergers, acquisitions, and investments? For lawyers purchasing e-discovery packages, there are several types of vendors and pricing models, and they need to be asking the right questions. What does the data governance solution need to do, how much does it cost, what are the time constraints, and how complex is the system?
Since its 2007 introduction, kCura’s Relativity product has become one of the world’s leading attorney review platforms. One of the elements of Relativity’s strong growth and marketplace acceptance has been kCura’s focus on and support of partnerships. Provided as a by-product of review platform research and presented in the form of a simple and sortable table is an aggregation of kCura Premium Hosting Partners and Consulting Partners.
Taken from a combination of public market sizing estimations as shared in leading electronic discovery reports, publications and posts over time, the following eDiscovery Market Size Mashup shares general worldwide market sizing considerations for both the software and service areas of the electronic discovery market for the years between 2013 and 2018.
Technology assisted review has a transparency problem. Notwithstanding TAR’s proven savings in both time and review costs, many attorneys hesitate to use it because courts require “transparency” in the TAR process. Specifically, when courts approve requests to use TAR, they often set the condition that counsel disclose the TAR process they used and which documents they used for training. In some cases, the courts have gone so far as to allow opposing counsel to kibitz during the training process itself.
In the wake of Judge Peck’s recent Rio Tinto opinion on technology assisted review, the ediscovery blogosphere has been repeatedly quoting its bold pronouncements that judicial acceptance of TAR “is now black letter law” and that “it is inappropriate to hold TAR to a higher standard than keywords or manual review.” And rightly so — these statements appear intended to put outdated predictive coding debates to rest once and for all. Yet a good deal of the focus is going to the question Judge Peck raises but does not fully resolve: whether disclosure of TAR seed sets may be required.
One advantage of using computer assisted review, for example, predictive coding, is that the computer does, in fact, examine all of the available evidence in a document. Unlike human reviewers, the computer sees all parts of the elephant and, as a result, consistently judges documents based on the full complement of information in them. Each of reviewer judgment used to train the system may be based on a sample of features, but the computer system aggregates all of these partial judgments and chooses the category that is most consistent with this aggregation of cues, rather than with any individual sample. As a result, the computer can be more consistent than the human reviewer who trains trains it. Under appropriate circumstances, this consistency further enhances the accuracy and reliability of computer assisted review.
Because so much useful information is unavailable to text analytics engines, they are unsuited for enterprise-scale document classification processes that involve placing documents in discrete document types so that subsequent classification-dependent initiatives can be undertaken, e.g., retention, remediation, migration, and digitization.
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