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.
Daily we read, see, and hear more and more about how technology is changing the game of document classification and revolutionizing document review. While there may be evidence that new data governance and discovery technologies can absolutely change current approaches to document classification and review, it is important to remember that technology is only as good as its ability to be delivered, managed, and supported by vendors and integrators.
While some may dispute the existence of unstructured data, definitions for the term “unstructured data” do exist. This week our cartoon and clip provides a quick look at how people convey meaning in different ways (cartoon) and provides a short list of definitions for the term “unstructured data” (clip).
The focus on the technology and talent elements of an information governance vendor’s capability is certainly warranted as these elements ultimately provide the cutting edge for the knife of task execution. However, just as there is much more to the utility of a knife than its edge (especially if you want to use it more than once), there are additional areas worthy of consideration in vendor selection if one is considering the long term strategic utility and viability of a vendor.
Attorneys and judges often rely exclusively upon “precision” and “recall” thresholds for acceptance of dichotomous classification models in what is commonly referred to in the legal industry as “predictive coding.” Because these measures fail to provide a complete understanding of the proposed model’s characteristics and efficacy, this paper will argue that interested parties should go beyond the precision and recall metrics and include other, more effective performance measurements such as Receiver Operator Characteristic (ROC) and Area Under the Curve (AUC).
Courts have so far provided mixed guidance on this issue, leaving litigants guessing as to whether their choice of blending keyword and predictive coding search methodologies – if challenged by an adversary – would receive judicial imprimatur. Nevertheless, a new ruling from the Rio Tinto v. Vale litigation confirms that parties may combine these search methodologies to achieve reasonable and proportional productions of highly relevant information.
Technology-Assisted Review (hereinafter TAR) is broadly defined as the use of computer tools to determine the relevance of selected documents to any issues in a given controversy. The most utilized form of TAR, known as predictive coding, allows a human reviewer to utilize a select sample of documents to “train” a computer to recognize patterns of relevance in the universe of documents under review.
In this episode of Digital Detectives, Sharon Nelson and John Simek interview Judge Andrew Peck, an expert in issues relating to electronic discovery. Together they discuss the current state of technology-assisted review, how FRCP amendments will affect the way lawyers do discovery, and best practices when using TAR.
ZEN document review is designed to attain the highest possible level of efficiency and quality in computer assisted review. The goal is zero error . The methods to attain that goal include active machine learning, random sampling, objective measurements, and comparative analysis using simple, repeatable systems.
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