When you are evaluating information governance and electronic discovery solutions do you ask your vendor/service provider the basic questions of:
1) Does your system/process identify both textual and non-textual ESI files?
2) How does your system/process index and classify non-textual ESI files? (Example: Image only PDFs.)
3) How does your system/process identify text within non-textual ESI files? (Example: Graphics with words published to an image only PDF.)
If your vendor/service provider cannot adequately answer these three simple questions, then you may want to consider the potential risk and exposure associated with not fully considering non-textual ESI in your information governance and eDiscovery efforts.
Good vendors share what they know they see. Great vendors share what they may not see so you can make informed decisions as to risk and exposure.
Worthy questions. Worthy considerations. Worthy of answers (from your vendor/service provider).
This entry was posted on Sunday, May 11th, 2014 at 2:25 pm. It is filed under chronology, Insight, original, views and tagged with electronic discovery. You can follow any responses to this entry through the RSS 2.0 feed.
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.
In his much touted recent opinion in Rio Tinto v. Vale , US magistrate Judge Andrew Peck noted that, “If [a technology-assisted review] methodology uses ‘continuous active learning’ (as opposed to simple passive learning or simple active learning), the contents of the seed set is much less significant.”
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.
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