As we noted yesterday and the day before, eDiscoveryDaily published 93 posts related to eDiscovery case decisions and activities over the past year, covering 68 unique cases! Yesterday, we looked back at cases related to eDiscovery cost sharing and reimbursement, fee disputes and production format disputes. Today, let’s take a look back at cases related to privilege and inadvertent disclosures, requests for social media, cases involving technology assisted review and the case of the year – the ubiquitous Apple v. Samsung dispute.
In Good v. American Water Works, West Virginia District Judge John T. Copenhaver, Jr. granted the defendants' motion for a Rule 502(d) order that merely encouraged the incorporation and employment of time-saving computer-assisted privilege review over the plaintiffs’ proposal disallowing linear privilege review altogether.
A couple of months ago, we had a laugh at Ralph Losey’s post that took a humorous look at the scenario where it’s Friday at 5 and you need data processed to be reviewed over the weekend. It was a funny take on a real problem that most of us have experienced from time to time. But, there may be a solution to this problem that’s automated, easy and inexpensive.
Today is Halloween. Every year at this time, because (after all) we’re an eDiscovery blog, we try to “scare” you with tales of eDiscovery horrors. This is our fifth year of doing so, let’s see how we do this year. Be afraid, be very afraid!
In Price Waicukauski & Riley v. Murray, Indiana District Judge William T. Lawrence granted the plaintiff’s request for summary judgment for failure to pay attorney’s fees of over $125,000, and refused to issue summary judgment for either party related to a legal malpractice claim for the plaintiff’s admitted failure to review documents produced in the defendants’ case against another party because of a factual dispute regarding the plaintiff’s knowledge of the documents produced.
A new self-assessment resource from EDRM helps you answer that question. A few days ago, EDRM announced the release of the EDRM eDiscovery Maturity Self-Assessment Test (eMSAT-1), the “first self-assessment resource to help organizations measure their eDiscovery maturity”. Find out more about it here.
In Dynamo Holdings v. Commissioner of Internal Revenue, Texas Tax Court Judge Ronald Buch ruled that the petitioners “may use predictive coding in responding to respondent's discovery request” and if “after reviewing the results, respondent believes that the response to the discovery request is incomplete, he may file a motion to compel at that time”.
As we discussed Wednesday, working with electronic files in a review tool is NOT just simply a matter of loading the files and getting started. Electronic files are diverse and can represent a whole collection of issues to address in order to process them for loading. To address those issues effectively, processing requires a sound process.
Since hard copy discovery became electronic discovery, I’ve worked with a number of clients who expect that working with electronic files in a review tool is simply a matter of loading the files and getting started. Unfortunately, it’s not that simple!
I frequently get asked how big does an ESI collection need to be to benefit from eDiscovery technology. In a recent case with one of my clients, the client had a fairly small collection – only about 4 GB. But, when a judge ruled that they had to start conducting depositions in a week, they needed to review that data in a weekend. But, if you’re not facing a tight deadline, how large does your collection need to be for the use of eDiscovery technology to provide benefits?
A lot of people consider Technology Assisted Review (TAR) and Predictive Coding (PC) to be new technology. We attempted to debunk that as myth last year after our third annual thought leader interview series by summarizing comments from some of the thought leaders that noted that TAR and PC really just apply artificial intelligence to the review process. But, the foundation for TAR may go way farther back than you might think.
In Bridgestone Americas Inc. v. Int'l Bus. Mach. Corp., Tennessee Magistrate Judge Joe B. Brown, acknowledging that he was “allowing Plaintiff to switch horses in midstream”, nonetheless ruled that that the plaintiff could use predictive coding to search documents for discovery, even though keyword search had already been performed.
When we launched nearly four years ago on September 20, 2010, our goal was to be a daily resource for eDiscovery news and analysis. Now, after doing so each business day, I’m happy to announce that today is our 1,000th post on eDiscovery Daily! Check out what we've covered over 1,000 posts!
In the case In re Bridgepoint Educ., Inc., Securities Litigation, California Magistrate Judge Jill L. Burkhardt ruled that expanding the scope of discovery by nine months was unduly burdensome, despite the plaintiff’s request for the defendant to use predictive coding to fulfill its discovery obligation and also approved the defendants' method of using search terms to identify responsive documents for the already reviewed three individual defendants, directing the parties to meet and confer regarding the additional search terms the plaintiffs requested.
I love Paul Simon’s music. One of my favorite songs of his is Mother and Child Reunion. Of course, I’m such an eDiscovery nerd that every time I think of that song, I think of keeping email and attachment families together. If you don’t remember the Mother and Child Reunion, you might provide an incomplete production to opposing counsel.
In a dispute over ESI protocols in FDIC v. Bowden, Georgia Magistrate Judge G. R. Smith approved the ESI protocol from the FDIC and suggested the parties consider the use of predictive coding.
In In Progressive Cas. Ins. Co. v. Delaney, Nevada Magistrate Judge Peggy A. Leen determined that the plaintiff’s unannounced shift from the agreed upon discovery methodology, to a predictive coding methodology for privilege review was not cooperative. Therefore, the plaintiff was ordered to produce documents that met agreed-upon search terms without conducting a privilege review first.
Scanning may no longer be cool, but it’s still necessary. Electronic discovery still typically includes a paper component. When it comes to paper, how documents are identified is critical to how useful they will be. Here’s an example.
One of the most frequently discussed trends in this year’s annual thought leader interviews that we conducted was the application of analytics (including predictive analytics) to Information Governance. A recent report published in the Richmond Journal of Law & Technology addresses how analytics can be used to optimize Information Governance.
I’ve recently worked with a couple of clients who proposed search terms for key individuals that were a bit limited, so I thought this was an appropriate topic to revisit. When looking for documents in your collection that mention key individuals, conducting a name search for those individuals isn’t always as straightforward as you might think. There are potentially a number of different ways names could be represented and if you don’t account for each one of them, you might fail to retrieve key responsive documents – OR retrieve way too many non-responsive documents. Here are some considerations for conducting name searches.
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