“The value of personal financial and health records is two or three times [the value of financial information alone], because there’s so many more opportunities for fraud,” said David Dimond, chief technology officer of EMC Healthcare, a Massachusetts-based technology provider. Combine a Social Security number, birth date and some health history, and a thief can open credit accounts plus bill insurers or the government for fictitious medical care.
The core challenge of discovery is identifying information that is responsive but not privileged, achieved without undue burden or expense. There are multiple ways to approach the task, none optimal. The most labor-intensive method is called “linear human review,” where lawyers (for the most part) look at everything and cull responsive and privileged items.
Regularly we read, see and hear incredibly serious presentations and pontifications related to the theory, practice and business of electronic discovery. This week our cartoon and clip features a quick look at Rule 26(f) conference planning for a frivolous lawsuit (cartoon) and a quick reference link to a very serious retrospective listing of 26 eDiscovery-related cartoons (clip).
Logikcull – a file organization and discovery firm – is one of these. The company just announced a $4 million seed round led by Storm Ventures, with participation from Gainsight CEO Nick Mehta and investor Anshu Sharma. Logikcull will use the new money to hire another ten sales and marketing jobs to its roster to facilitate growth.
The NetDiligence 2014 Cyber Claims Study relies on data voluntarily provided by insurers about amounts paid out on cyber claims occurring from 2011 through 2013. Since the Study only accounts cyber claims reported to larger insurers, NetDiligence believes its study only accounts for 5-10% of the total number of all cyber claims handled in those years.
“The ability to earn trust must be part of any plan to implement artificial intelligence (AI) or smart machines, and will be an important selling point when marketing this technology. CIOs must be able to monitor smart machine technology for unintended consequences of public use and respond immediately, embracing unforeseen positive outcomes and countering undesirable ones.”
As we have reported in the past, the eDiscovery industry is still growing at an impressive rate. One recent market report estimated that the global eDiscovery market is forecast to reach $15.65 billion by 2020 . So, who is investing in the eDiscovery industry?
At this year’s Legal Tech, I once again had the honor of moderating the Judges Panel, on which Judge John Facciola (D.D.C., retired), Judge Andrew Peck (S.D.N.Y), Judge Frank Maas (S.D.N.Y), and Judge Elizabeth Laporte (N.D.Cal.) presented. This time, we had a provocative topic (or, perhaps –as Judge Peck put it–, a depressing one): “What’s Wrong with Discovery?” The judges had plenty of insight into why discovery has become risky and expensive, what causes attorney misconduct in discovery, and implications for access to justice. Below are ten highlights of that discussion.
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.
There is a growing number of government and industry regulations and standards designed to help protect the confidentiality of data that companies have in their care. The need for guidance in this area is obvious, as 2014 saw a record number of software vulnerabilities and actual data breaches. Unfortunately, many of the companies that experienced those breaches are now facing lawsuits filed by individuals, investors and other entities that claim they were harmed by the exposure of their information.
When dealing with electronic data, some attorneys think that since the files are already electronic, how hard can they be to load? Unfortunately, it’s not as simple as that. To be useable in discovery, electronic files need to be processed and good processing requires a sound process.
Regularly we read, see and hear more and more about mergers, acquisitions, investments and investors in the business of electronic discovery. This week our cartoon and clip features an abstract look at investing in eDiscovery (cartoon) and two quick reference links that highlight merger, acquisition and investment activities from both an activity level and an investor level (clip).
The 2015 Big Data and Analytics study highlights data-driven initiatives and strategies driving data investments within IT organizations. In order to gain a deeper understanding of organizations’ big data goals and tactics, the research shows data deployment trends, future investment growth and opportunities for vendors.
One of the most challenging aspects to identifying and protecting PII is how to deal with “unstructured” content, i.e., with documents or files on file shares, personal computing devices, and content management systems. These files can be generated within and outside the organization using many applications, can be converted to multiple file formats (most commonly to PDF), and seemingly have unlimited form and content.
83% of organizations are prioritizing structured data initiatives as critical or high priority in 2015, and 36% planning to increase their budgets for data-driven initiatives in 2015.
Secretary Clinton will likely do unintentionally for the duty to preserve electronic records in controversy what Edward Snowden did intentionally for the very right to privacy from government surveillance that Secretary Clinton now claims: She may put it on the map—into the global, public tag cloud—in a big way.
Provided is a short list of 30+ investment organizations that have funded eDiscovery-related companies between 2009 and today. The list is non-comprehensive and is based on industry mergers, acquisitions and investment tracking by ComplexDiscovery.
In 2010, “predictive coding” or “computer-assisted review” was considered the next big thing in ediscovery, destined to replace linear review and keyword searching as the predominant methodology during document review. Fast forward 5 years and where are we? Has the “next big thing” arrived?
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.
Daily we read, see and hear more and more about the challenge and cost of managing email. This week our cartoon and clip features a metric highlighting one cost of email (cartoon) and four quick reference links to recent mentions of the potential impact of organizational email practices (clip).
Many who consider Magistrate Judge Peck’s recent opinion and order in Rio Tinto PLC v. Vale S.A., which he titled “Predictive Coding a.k.a. Computer Assisted Review a.k.a. Technology Assisted Review (TAR) – Da Silva Moore Revisited,” will focus on his declaration “that it is now black letter law that where the producing party wants to utilize TAR for document review, courts will permit it.” We’ll revisit that statement in a moment, but first note that it is also black letter law that important discovery decisions get revisited. See, e.g., The Pension Committee of the University of Montreal Pension Plan v. Banc of America Securities, subtitled “Zubulake Revisited: Six Years Later.”
Provided below is a short list of 20+ publicly traded companies that offer eDiscovery-related offerings as part of their overall offering portfolio. The purpose of this short list is to simply highlight those companies with a public status that allows for deeper investigations into the health and wealth of their eDiscovery business.