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The Files are Already Electronic, How Hard Can They Be to Load? – eDiscovery Best Practices

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!

Back when most discovery was paper based, the usefulness of the documents was understandably limited.  Documents were paper and they all required conversion to image to be viewed electronically, optical character recognition (OCR) to capture their text (though not 100% accurately) and coding (i.e., data entry) to capture key data elements (e.g., author, recipient, subject, document date, document type, names mentioned, etc.).  It was a problem, but it was a consistent problem – all documents needed the same treatment to make them searchable and usable electronically.

Though electronic files are already electronic, that doesn’t mean that they’re ready for review as is.  They don’t just represent one problem, they can represent a whole collection of problems.  For example:

These are just a few examples of why working with electronic files for review isn’t necessarily straightforward.  Of course, when processed correctly, electronic files include considerable metadata that provides useful information about how and when the files were created and used, and by whom.  They’re way more useful than paper documents.  So, it’s still preferable to work with electronic files instead of hard copy files whenever they are available.  But, despite what you might think, that doesn’t make them ready to review as is.

So, what do you think?  Have you encountered difficulties or challenges when processing electronic files?  Please share any comments you might have or if you’d like to know more about a particular topic.

Disclaimer: The views represented herein are exclusively the views of the author, and do not necessarily represent the views held by CloudNine Discovery. eDiscoveryDaily is made available by CloudNine Discovery solely for educational purposes to provide general information about general eDiscovery principles and not to provide specific legal advice applicable to any particular circumstance. eDiscoveryDaily should not be used as a substitute for competent legal advice from a lawyer you have retained and who has agreed to represent you.

Plaintiffs Take the Supreme Step in Da Silva Moore – eDiscovery Case Law

As mentioned in Law Technology News (‘Da Silva Moore’ Goes to Washington), attorneys representing lead plaintiff Monique Da Silva Moore and five other employees have filed a petition for certiorari filed with the Supreme Court arguing that New York Magistrate Judge Andrew Peck, who approved an eDiscovery protocol agreed to by the parties that included predictive coding technology, should have recused himself given his previous public statements expressing strong support of predictive coding.

Da Silva Moore and her co-plaintiffs argued in the petition that the Second Circuit Court of Appeals was too deferential to Peck when denying the plaintiff’s petition to recuse him, asking the Supreme Court to order the Second Circuit to use the less deferential “de novo” standard.  As noted in the LTN article:

“The employees also cited a circuit split in how appellate courts reviewed judicial recusals, pointing out that the Seventh Circuit reviews disqualification motions de novo. Besides resolving the circuit split, the employees asked the Supreme Court to find that the Second Circuit’s standard was incorrect under the law. Citing federal statute governing judicial recusals, the employees claimed that the law required motions for disqualification to be reviewed objectively and that a deferential standard flew in the face of statutory intent. “Rather than dispelling the appearance of a self-serving judiciary, deferential review exacerbates the appearance of impropriety that arises from judges deciding their own cases and thus undermines the purposes of [the statute],” wrote the employees in their cert petition.”

This battle over predictive coding and Judge Peck’s participation has continued for 15 months.  For a recap of the events during that time, click here.

So, what do you think?  Is this a “hail mary” for the plaintiffs and will it succeed?  Please share any comments you might have or if you’d like to know more about a particular topic.

Disclaimer: The views represented herein are exclusively the views of the author, and do not necessarily represent the views held by CloudNine Discovery. eDiscoveryDaily is made available by CloudNine Discovery solely for educational purposes to provide general information about general eDiscovery principles and not to provide specific legal advice applicable to any particular circumstance. eDiscoveryDaily should not be used as a substitute for competent legal advice from a lawyer you have retained and who has agreed to represent you.

“Not Me”, The Fallibility of Human Review – eDiscovery Best Practices

When I talk with attorneys about using technology to assist with review (whether via techniques such as predictive coding or merely advanced searching and culling mechanisms), most of them still seem to question whether these techniques can measure up to good, old-fashioned human attorney review.  Despite several studies that question the accuracy of human review, many attorneys still feel that their review capability is as good or better than technical approaches.  Here is perhaps the best explanation I’ve seen yet why that may not be the case.

In Craig Ball’s latest blog post on his Ball in Your Court blog (The ‘Not Me’ Factor), Craig provides a terrific explanation as to why predictive coding is “every bit as good (and actually much, much better) at dealing with the overwhelming majority of documents that don’t require careful judgment—the very ones where keyword search and human reviewers fail miserably.”

“It turns out that well-designed and –trained software also has little difficulty distinguishing the obviously relevant from the obviously irrelevant.  And, again, there are many, many more of these clear cut cases in a collection than ones requiring judgment calls.

So, for the vast majority of documents in a collection, the machines are every bit as capable as human reviewers.  A tie.  But giving the extra point to humans as better at the judgment call documents, HUMANS WIN!  Yeah!  GO HUMANS!   Except….

Except, the machines work much faster and much cheaper than humans, and it turns out that there really is something humans do much, much better than machines:  they screw up.

The biggest problem with human reviewers isn’t that they can’t tell the difference between relevant and irrelevant documents; it’s that they often don’t.  Human reviewers make inexplicable choices and transient, unwarranted assumptions.  Their minds wander.  Brains go on autopilot.  They lose their place.  They check the wrong box.  There are many ways for human reviewers to err and just one way to perform correctly.

The incidence of error and inconsistent assessments among human reviewers is mind boggling.  It’s unbelievable.  And therein lays the problem: it’s unbelievable.    People I talk to about reviewer error might accept that some nameless, faceless contract reviewer blows the call with regularity, but they can’t accept that potential in themselves.  ‘Not me,’ they think, ‘If I were doing the review, I’d be as good as or better than the machines.’  It’s the ‘Not Me’ Factor.”

While Craig acknowledges that “there is some cause to believe that the best trained reviewers on the best managed review teams get very close to the performance of technology-assisted review”, he notes that they “can only achieve the same result by reviewing all of the documents in the collection, instead of the 2%-5% of the collection needed to be reviewed using predictive coding”.  He asks “[i]f human review isn’t better (and it appears to generally be far worse) and predictive coding costs much less and takes less time, where’s the rational argument for human review?”

Good question.  Having worked with some large review teams with experienced and proficient document reviewers at an eDiscovery provider that employed a follow-up QC check of reviewed documents, I can still recall how often those well-trained reviewers were surprised at some of the classification mistakes they made.  And, I worked on one project with over a hundred reviewers working several months, so you can imagine how expensive that was.

BTW, Craig is no stranger to this blog – in addition to several of his articles we’ve referenced, we’ve also conducted thought leader interviews with him at LegalTech New York the past three years.  Here’s a link if you want to check those out.

So, what do you think?  Do you think human review is better than technology assisted review?  If so, why?  Please share any comments you might have or if you’d like to know more about a particular topic.

Disclaimer: The views represented herein are exclusively the views of the author, and do not necessarily represent the views held by CloudNine Discovery. eDiscoveryDaily is made available by CloudNine Discovery solely for educational purposes to provide general information about general eDiscovery principles and not to provide specific legal advice applicable to any particular circumstance. eDiscoveryDaily should not be used as a substitute for competent legal advice from a lawyer you have retained and who has agreed to represent you.

Motion to Compel Dismissed after Defendant Agrees to Conditional Meet and Confer – eDiscovery Case Law

In Gordon v. Kaleida Health, No. 08-CV-378S(F) (W.D.N.Y. May 21, 2013), New York Magistrate Judge Leslie G. Foschio dismissed (without prejudice) the plaintiffs’ motion to compel the defendant to meet and confer to establish an agreed protocol for implementing the use of predictive coding software after the defendants stated that they were prepared to meet and confer with the plaintiffs and their non-disqualified ESI consultants regarding the defendants’ predictive coding process.

For over a year, the parties unsuccessfully attempted to agree on how to achieve a cost-effective review of the defendants’ 200,000 to 300,000 emails using a keyword search methodology.  Eventually, in June 2012, the court expressed dissatisfaction with the parties’ lack of progress toward resolving the issues and pointed to the availability of predictive coding, citing its approval in Da Silva Moore v. Publicis Groupe & MSL Group, No. 11 Civ. 1279 (ALC) (AJP) (S.D.N.Y. Feb. 24, 2012) (much more on that case here).

In a September 2012 email, after informing the plaintiffs that they intended to use predictive coding, the defendants objected to the plaintiffs’ ESI consultants participating in discussions with Defendants relating to the use of predictive coding and establishing a protocol.  Later that month, despite the plaintiffs’ requests for discussion of numerous search issues to ensure a successful predictive coding outcome, the defendants sent their ESI protocol to the plaintiffs and indicated they would also send a list of their email custodians to the plaintiffs.  In October 2012, the plaintiffs objected to the defendants’ proposed ESI protocol and filed the motion to compel, also citing Da Silva Moore and noting several technical issues “which should be discussed with the assistance of Plaintiffs’ ESI consultants and cooperatively resolved by the parties”.

Complaining that the defendants refused to discuss issues other than the defendants’ custodians, the plaintiffs claimed that “the defendants’ position excludes Plaintiffs’ access to important information regarding Defendants’ selection of so-called ‘seed set documents’ which are used to ‘train the computer’ in the predictive coding search method.  The defendants responded, indicating they had no objection to a meet and confer with the plaintiffs and their consultants, except for those consultants that were the subject of the defendants’ motion to disqualify (because they had previously provided services to the defendants in the case). With regard to sharing seed set document information, the defendants stated that “courts do not order parties in ESI discovery disputes to agree to specific protocols to facilitate a computer-based review of ESI based on the general rule that ESI production is within the ‘sound discretion’ of the producing party” and noted that the defendants in Da Silva Moore weren’t required to provide the plaintiffs with their seed set documents, but volunteered to do so.

Because the defendants stated that “they are prepared to meet and confer with Plaintiffs and Plaintiffs’ ESI consultants, who are not disqualified”, Judge Foschio ruled that “it is not necessary for the court to further address the merits of Plaintiffs’ motion at this time” and dismissed the motion without prejudice.  It will be interesting to see if the parties can ultimately agree on sharing the protocol or if the question regarding sharing information about seed set documents will come back before the court.

So, what do you think?  Should producing parties be required to share information regarding selection of seed set documents?  Please share any comments you might have or if you’d like to know more about a particular topic.

Disclaimer: The views represented herein are exclusively the views of the author, and do not necessarily represent the views held by CloudNine Discovery. eDiscoveryDaily is made available by CloudNine Discovery solely for educational purposes to provide general information about general eDiscovery principles and not to provide specific legal advice applicable to any particular circumstance. eDiscoveryDaily should not be used as a substitute for competent legal advice from a lawyer you have retained and who has agreed to represent you.

Never Mind! Plaintiffs Not Required to Use Predictive Coding After All – eDiscovery Case Law

Remember EORHB v. HOA Holdings, where, in a surprise ruling, both parties were instructed to use predictive coding by the judge?  Well, the judge has changed his mind.

As reported by Robert Hilson in the Association of Certified E-Discovery Specialists® (ACEDS) web site (subscription required), Delaware Chancery Court Vice Chancellor J. Travis Laster has revised his decision in EORHB, Inc. v. HOA Holdings, LLC, No. 7409-VCL (Del. Ch. May 6, 2013).  The new order enables the defendants to continue to utilize computer assisted review with their chosen vendor but no longer requires both parties to use the same vendor and enables the plaintiffs, “based on the low volume of relevant documents expected to be produced” to perform document review “using traditional methods.”

Here is the text of this very short order:

WHEREAS, on October 15, 2012, the Court entered an Order providing that, “[a]bsent a modification of this order for good cause shown, the parties shall (i) retain a single discovery vendor to be used by both sides, and (ii) conduct document review with the assistance of predictive coding;”

WHEREAS, the parties have proposed that HOA Holdings LLC and HOA Restaurant Group LLC (collectively, “Defendants”) retain ediscovery vendor Kroll OnTrack for electronic discovery;

WHEREAS, the parties have agreed that, based on the low volume of relevant documents expected to be produced in discovery by EORHB, Inc., Coby G. Brooks, Edward J. Greene, James P. Creel, Carter B. Wrenn and Glenn G. Brooks (collectively, “Plaintiffs”), the cost of using predictive coding assistance would likely be outweighed by any practical benefit of its use;

WHEREAS, the parties have agreed that there is no need for the parties to use the same discovery review platform;

WHEREAS, the requested modification of the Order will not prejudice any of the parties;

NOW THEREFORE, this –––– day of May 2013, for good cause shown, it is hereby ORDERED that:

(i) Defendants may retain ediscovery vendor Kroll OnTrack and employ Kroll OnTrack and its computer assisted review tools to conduct document review;

(ii) Plaintiffs and Defendants shall not be required to retain a single discovery vendor to be used by both sides; and

(iii) Plaintiffs may conduct document review using traditional methods.

Here is a link to the order from the article by Hilson.

So, what do you think?  Should a party ever be ordered to use predictive coding?  Please share any comments you might have or if you’d like to know more about a particular topic.

Disclaimer: The views represented herein are exclusively the views of the author, and do not necessarily represent the views held by CloudNine Discovery. eDiscoveryDaily is made available by CloudNine Discovery solely for educational purposes to provide general information about general eDiscovery principles and not to provide specific legal advice applicable to any particular circumstance. eDiscoveryDaily should not be used as a substitute for competent legal advice from a lawyer you have retained and who has agreed to represent you.

More Updates from the EDRM Annual Meeting – eDiscovery Trends

Yesterday, we discussed some general observations from the Annual Meeting for the Electronic Discovery Reference Model (EDRM) group and discussed some significant efforts and accomplishments by the (suddenly heavily talked about) EDRM Data Set project.  Here are some updates from other projects within EDRM.

It should be noted these are summary updates and that most of the focus on these updates is on accomplishments for the past year and deliverables that are imminent.  Over the next few weeks, eDiscovery Daily will cover each project in more depth with more details regarding planned activities for the coming year.

Model Code of Conduct (MCoC)

The MCoC was introduced in 2011 and became available for organizations to subscribe last year.  To learn more about the MCoC, you can read the code online here, or download it as a 22 page PDF file here.  Subscribing is easy!  To voluntarily subscribe to the MCoC, you can register on the EDRM website here.  Identify your organization, provide information for an authorized representative and answer four verification questions (truthfully, of course) to affirm your organization’s commitment to the spirit of the MCoC, and your organization is in!  You can also provide a logo for EDRM to include when adding you to the list of subscribing organizations.  Pending a survey of EDRM members to determine if any changes are needed, this project has been completed.  Team leaders include Eric Mandel of Zelle Hofmann, Kevin Esposito of Rivulex and Nancy Wallrich.

Information Governance Reference Model (IGRM)

The IGRM team has continued to make strides and improvements on an already terrific model.  Last October, they unveiled the release of version 3.0 of the IGRM.  As their press release noted, “The updated model now includes privacy and security as primary functions and stakeholders in the effective governance of information.”  IGRM continues to be one of the most active and well participated EDRM projects.  This year, the early focus – as quoted from Judge Andrew Peck’s keynote speech at Legal Tech this past year – is “getting rid of the junk”.  Project leaders are Aliye Ergulen from IBM, Reed Irvin from Viewpointe and Marcus Ledergerber from Morgan Lewis.

Search

One of the best examples of the new, more agile process for creating deliverables within EDRM comes from the Search team, which released its new draft Computer Assisted Review Reference Model (CARRM), which depicts the flow for a successful Computer Assisted Review project. The entire model was created in only a matter of weeks.  Early focus for the Search project for the coming year includes adjustments to CARRM (based on feedback at the annual meeting).  You can also still send your comments regarding the model to mail@edrm.net or post them on the EDRM site here.  A webinar regarding CARRM is also planned for late July.  Kudos to the Search team, including project leaders Dominic Brown of Autonomy and also Jay Lieb of kCura, who got unmerciful ribbing for insisting (jokingly, I think) that TIFF files, unlike Generalissimo Francisco Franco, are still alive.  🙂

Jobs

In late January, the Jobs Project announced the release of the EDRM Talent Task Matrix diagram and spreadsheet, which is available in XLSX or PDF format. As noted in their press release, the Matrix is a tool designed to help hiring managers better understand the responsibilities associated with common eDiscovery roles. The Matrix maps responsibilities to the EDRM framework, so eDiscovery duties associated can be assigned to the appropriate parties.  Project leader Keith Tom noted that next steps include surveying EDRM members regarding the Matrix, requesting and co-authoring case-studies and white papers, and creating a short video on how to use the Matrix.

Metrics

In today’s session, the Metrics project team unveiled the first draft of the new Metrics model to EDRM participants!  Feedback was provided during the session and the team will make the model available for additional comments from EDRM members over the next week or so, with a goal of publishing for public comments in the next two to three weeks.  The team is also working to create a page to collect Metrics measurement tools from eDiscovery professionals that can benefit the eDiscovery community as a whole.  Project leaders Dera Nevin of TD Bank and Kevin Clark noted that June is “budget calculator month”.

Other Initiatives

As noted yesterday, there is a new project to address standards for working with native files in the different EDRM phases led by Eric Mandel from Zelle Hofmann and also a new initiative to establish collection guidelines, spearheaded by Julie Brown from Vorys.  There is also an effort underway to refocus the XML project, as it works to complete the 2.0 version of the EDRM XML model.  In addition, there was quite a spirited discussion as to where EDRM is heading as it approaches ten years of existence and it will be interesting to see how the EDRM group continues to evolve over the next year or so.  As you can see, a lot is happening within the EDRM group – there’s a lot more to it than just the base Electronic Discovery Reference Model.

So, what do you think?  Are you a member of EDRM?  If not, why not?  Please share any comments you might have or if you’d like to know more about a particular topic.

Disclaimer: The views represented herein are exclusively the views of the author, and do not necessarily represent the views held by CloudNine Discovery. eDiscoveryDaily is made available by CloudNine Discovery solely for educational purposes to provide general information about general eDiscovery principles and not to provide specific legal advice applicable to any particular circumstance. eDiscoveryDaily should not be used as a substitute for competent legal advice from a lawyer you have retained and who has agreed to represent you.

Reporting from the EDRM Annual Meeting and a Data Set Update – eDiscovery Trends

The Electronic Discovery Reference Model (EDRM) Project was created in May 2005 by George Socha of Socha Consulting LLC and Tom Gelbmann of Gelbmann & Associates to address the lack of standards and guidelines in the electronic discovery market.  Now, beginning its ninth year of operation with its annual meeting in St. Paul, MN, EDRM is accomplishing more than ever to address those needs.  Here are some highlights from the meeting, and an update regarding the (suddenly heavily talked about) EDRM Data Set project.

Annual Meeting

Twice a year, in May and October, eDiscovery professionals who are EDRM members meet to continue the process of working together on various standards projects.  This will be my eighth year participating in EDRM at some level and, oddly enough, I’m assisting with PR and promotion (how am I doing so far?).  eDiscovery Daily has referenced EDRM and its phases many times in the 2 1/2 years plus history of the blog – this is our 144th post that relates to EDRM!

Some notable observations about today’s meeting:

  • New Participants: More than half the attendees at this year’s annual meeting are attending for the first time.  EDRM is not just a core group of “die-hards”, it continues to find appeal with eDiscovery professionals throughout the industry.
  • Agile Approach: EDRM has adopted an Agile approach to shorten the time to complete and publish deliverables, a change in philosophy that facilitated several notable accomplishments from working groups over the past year including the Model Code of Conduct (MCoC), Information Governance Reference Model (IGRM), Search and Jobs (among others).  More on that tomorrow.
  • Educational Alliances: For the first time, EDRM has formed some interesting and unique educational alliances.  In April, EDRM teamed with the University of Florida Levin College of Law to present a day and a half conference entitled E-Discovery for the Small and Medium Case.  And, this June, EDRM will team with Bryan University to provide an in-depth, four-week E-Discovery Software & Applied Skills Summer Immersion Program for Law School Students.
  • New Working Group: A new working group to be lead by Eric Mandel of Zelle Hoffman was formed to address standards for working with native files in the different EDRM phases.

Tomorrow, we’ll discuss the highlights for most of the individual working groups.  Given the recent amount of discussion about the EDRM Data Set group, we’ll start with that one today!

Data Set

The EDRM Enron Data Set has been around for several years and has been a valuable resource for eDiscovery software demonstration and testing (we covered it here back in January 2011).  The data in the EDRM Enron PST Data Set files is sourced from the FERC Enron Investigation release made available by Lockheed Martin Corporation.  It was reconstituted as PST files with attachments for the EDRM Data Set Project.  So, in essence EDRM took already public domain available data and made the data much more usable.  Initially, the data was made available for download on the EDRM site, then subsequently moved to Amazon Web Services (AWS).

In the past several days, there has been much discussion about the personally-identifiable information (“PII”) available within the FERC (and consequently the EDRM Data Set), including social security numbers, credit card numbers, dates of birth, home addresses and phone numbers.  Consequently, the EDRM Data Set has been taken down from the AWS site.

The Data Set team led by Michael Lappin of Nuix and Eric Robi of Elluma Discovery has been working on a process (using predictive coding technology) to identify and remove the PII data from the EDRM Data Set.  Discussions about this process began months ago, prior to the recent discussions about the PII data contained within the set.  The team has completed this iterative process for V1 of the data set (which contains 1,317,158 items), identifying and removing 10,568 items with PII, HIPAA and other sensitive information.  This version of the data set will be made available within the EDRM community shortly for peer review testing.  The data set team will then repeat the process for the larger V2 version of the data set (2,287,984 items).  A timetable for republishing both sets should be available soon and the efforts of the Data Set team on this project should pay dividends in developing and standardizing processes for identifying and eliminating sensitive data that eDiscovery professionals can use in their own data sets.

The team has also implemented a Forensic Files Testing Project site where users can upload their own “modern”, non-copyrighted file samples that are typically encountered during electronic discovery processing to provide a more diverse set of data than is currently available within the Enron data set.

So, what do you think?  How has EDRM impacted how you manage eDiscovery?  Please share any comments you might have or if you’d like to know more about a particular topic.

Disclaimer: The views represented herein are exclusively the views of the author, and do not necessarily represent the views held by CloudNine Discovery. eDiscoveryDaily is made available by CloudNine Discovery solely for educational purposes to provide general information about general eDiscovery principles and not to provide specific legal advice applicable to any particular circumstance. eDiscoveryDaily should not be used as a substitute for competent legal advice from a lawyer you have retained and who has agreed to represent you.

Plaintiffs’ Objections to Defendant’s Use of Keyword Search before Predictive Coding Rejected – eDiscovery Case Law

Is it possible to produce documents for discovery too early?  At least one plaintiff’s group says yes.

In the case In Re: Biomet M2a Magnum Hip Implant Products Liability Litigation (MDL 2391), thhttps://cloudnine.com/ediscoverydaily/ralph-losey-of-jackson-lewis-llp-ediscovery-trends-part-1/e Plaintiffs’ Steering Committee in a Multi District Litigation objected to the defendant’s use of keyword searching prior to performing predictive coding and requested that the defendant go back to its original set of 19.5 million documents and repeat the predictive coding without performing keyword searching.  Indiana District Judge Robert L. Miller, Jr. denied the request.

Defendant’s Discovery Efforts to Date

In this dispute over hip implant products, the defendant began producing documents in cases that were eventually centralized, despite (sometimes forceful) requests by plaintiffs’ counsel not to begin document production until the decision whether to centralize was made.  The defendant used keyword culling to reduce the universe of documents and attachments from 19.5 million documents to 3.9 million documents, and removing duplicates left 2.5 million documents and attachments. The defendant performed statistical sampling tests, with a 99 percent confidence rate, to determine that between .55% and 1.33% of the unselected documents would be responsive and (with the same confidence level) that between 1.37% and 2.47% of the original 19.5 million documents were responsive.  The defendant’s approach actually retrieved 16% of the original 19.5 million.  The defendant then performed predictive coding to identify responsive documents to be produced from the set of 2.5 million documents.

According to the order, the defendant’s eDiscovery costs “are about $1.07 million and will total between $2 million and $3.25 million.” {emphasis added}  The defendant “invited the Plaintiffs’ Steering Committee to suggest additional search terms and offered to produce the rest of the non-privileged documents from the post-keyword 2.5 million”, but they declined, “believing they are too little to assure proper document production”.

Plaintiffs’ Objections

The plaintiffs’ Steering Committee objected, claiming that the defendant’s use of keyword searching “has tainted the process”, pointing to an article which “mentioned unidentified ‘literature stating that linear review would generate a responsive rate of 60 percent and key word searches only 20 percent, and [the defendants in the case being discussed] proposed that predictive coding at a 75 percent responsive rate would be sufficient.’” {emphasis added}  They requested that the defendant “go back to its 19.5 million documents and employ predictive coding, with plaintiffs and defendants jointly entering the ‘find more like this’ commands.”  In response to the defendant’s objections that virtually starting over would cost additional millions, the Steering Committee blamed the defendant for spending millions on document production despite being warned not to begin until the cases had been centralized.

Judge’s Ruling

Noting that “[w]hat Biomet has done complies fully with the requirements of Federal Rules of Civil Procedure 26(b) and 34(b)(2)”, Judge Miller noted that “the Steering Committee’s request that Biomet go back to Square One…and institute predictive coding at that earlier stage sits uneasily with the proportionality standard in Rule 26(b)(2)(C).”  Continuing, Judge Miller stated:

“Even in light of the needs of the hundreds of plaintiffs in this case, the very large amount in controversy, the parties’ resources, the importance of the issues at stake, and the importance of this discovery in resolving the issues, I can’t find that the likely benefits of the discovery proposed by the Steering Committee equals or outweighs its additional burden on, and additional expense to, Biomet.”

Judge Miller also rejected the Steering Committee’s position that the defendant can’t rely on proportionality arguments because they proceeded with document production while the centralization decision was pending: “The Steering Committee hasn’t argued (and I assume it can’t argue) that Biomet had no disclosure or document identification obligation in any of the cases that were awaiting a ruling on (or even the filing of) the centralization petition.”  As a result, he ruled that the Steering Committee would have to bear the expense for “production of documents that can be identified only through re-commenced processing, predictive coding, review, and production”.

So, what do you think?  Was the judge correct to accept the defendant’s multimodal approach to discovery?  Please share any comments you might have or if you’d like to know more about a particular topic.

Disclaimer: The views represented herein are exclusively the views of the author, and do not necessarily represent the views held by CloudNine Discovery. eDiscoveryDaily is made available by CloudNine Discovery solely for educational purposes to provide general information about general eDiscovery principles and not to provide specific legal advice applicable to any particular circumstance. eDiscoveryDaily should not be used as a substitute for competent legal advice from a lawyer you have retained and who has agreed to represent you.

Appeals Court Upholds Decision Not to Recuse Judge Peck in Da Silva Moore – eDiscovery Case Law

As reported by IT-Lex, the Second Circuit of the US Court of Appeals rejected the Plaintiff’s request for a writ of mandamus recusing Magistrate Judge Andrew J. Peck from Da Silva Moore v. Publicis Groupe SA.

The entire opinion is stated as follows:

“Petitioners, through counsel, petition this Court for a writ of mandamus compelling the recusal of Magistrate Judge Andrew J. Peck. Upon due consideration, it is hereby ORDERED that the mandamus petition is DENIED because Petitioners have not ‘clearly and indisputably demonstrate[d] that [Magistrate Judge Peck] abused [his] discretion’ in denying their district court recusal motion, In re Basciano, 542 F. 3d 950, 956 (2d Cir. 2008) (internal quotation marks omitted) (quoting In re Drexel Burnham Lambert Inc., 861 F.2d 1307, 1312-13 (2d Cir. 1988)), or that the district court erred in overruling their objection to that decision.”

Now, the plaintiffs have been denied in their recusal efforts in three courts.

Since it has been a while, let’s recap the case for those who may have not been following it and may be new to the blog.

Last year, back in February, Judge Peck issued an opinion making this case likely the first case to accept the use of computer-assisted review of electronically stored information (“ESI”) for this case.  However, on March 13, District Court Judge Andrew L. Carter, Jr. granted the plaintiffs’ request to submit additional briefing on their February 22 objections to the ruling.  In that briefing (filed on March 26), the plaintiffs claimed that the protocol approved for predictive coding “risks failing to capture a staggering 65% of the relevant documents in this case” and questioned Judge Peck’s relationship with defense counsel and with the selected vendor for the case, Recommind.

Then, on April 5, Judge Peck issued an order in response to Plaintiffs’ letter requesting his recusal, directing plaintiffs to indicate whether they would file a formal motion for recusal or ask the Court to consider the letter as the motion.  On April 13, (Friday the 13th, that is), the plaintiffs did just that, by formally requesting the recusal of Judge Peck (the defendants issued a response in opposition on April 30).  But, on April 25, Judge Carter issued an opinion and order in the case, upholding Judge Peck’s opinion approving computer-assisted review.

Not done, the plaintiffs filed an objection on May 9 to Judge Peck’s rejection of their request to stay discovery pending the resolution of outstanding motions and objections (including the recusal motion, which has yet to be ruled on.  Then, on May 14, Judge Peck issued a stay, stopping defendant MSLGroup’s production of electronically stored information.  On June 15, in a 56 page opinion and order, Judge Peck denied the plaintiffs’ motion for recusal.  Judge Carter ruled on the plaintiff’s recusal request on November 7, denying the request and stating that “Judge Peck’s decision accepting computer-assisted review … was not influenced by bias, nor did it create any appearance of bias”.

So, what do you think?  Will this finally end the recusal question in this case?  Please share any comments you might have or if you’d like to know more about a particular topic.

Disclaimer: The views represented herein are exclusively the views of the author, and do not necessarily represent the views held by CloudNine Discovery. eDiscoveryDaily is made available by CloudNine Discovery solely for educational purposes to provide general information about general eDiscovery principles and not to provide specific legal advice applicable to any particular circumstance. eDiscoveryDaily should not be used as a substitute for competent legal advice from a lawyer you have retained and who has agreed to represent you.

Fulbright’s Litigation Trends Survey Shows Increased Litigation, Mobile Device Collection – eDiscovery Trends

According to Fulbright’s 9th Annual Litigation Trends Survey released last month, companies in the United States and United Kingdom continue to deal with, and spend more on litigation.  From an eDiscovery standpoint, the survey showed an increase in requirements to preserve and collect data from employee mobile devices, a high reliance on self-preservation to fulfill preservation obligations and a decent percentage of organizations using technology assisted review.

Here are some interesting statistics from the report:

PARTICIPANTS

Here is a breakdown of the participants in the survey.

  • There were 392 total participants from the US and UK, 96% of which were either General Counsel (82%) or Head of Litigation (14%).
  • About half (49%) of the companies surveyed, were billion dollar companies with $1 billion or more in gross revenue.  36% of the total companies have revenues of $10 billion or more.

LITIGATION TRENDS

The report showed increases in both the number of cases being encountered by organizations, as well as the total expenditures for litigation.

Increasing Litigation Cases

  • This year, 92% of respondents anticipate either the same amount or more litigation, up from 89% last year.  26% of respondents expect litigation to increase, while 66% expect litigation to stay the same.  Among the larger companies, 33% of respondents expect more disputes, and 94% expect either the same number or an increase.
  • The number of respondents reporting that they had received a lawsuit rose this year to 86% estimating at least one matter, compared with 73% last year. Those estimating at least 21 lawsuits or more rose to 33% from 22% last year.
  • Companies facing at least one $20 million lawsuit rose to 31% this year, from 23% the previous year.

Increasing Litigation Costs

  • The percentage of companies spending $1 million or more on litigation has increased for the third year in a row to 54%, up from 51% in 2011 and 46% in 2010, primarily due to a sharp rise in $1 million+ cases in the UK (rising from 38% in 2010 up to 53% in 2012).
  • In the US, 53% of organizations spend $1 million or more on litigation and 17% spend $10 million or more.
  • 33% of larger companies spent $10 million on litigation, way up from 19% the year before (and 22% in 2010).

EDISCOVERY TRENDS

The report showed an increase in requirements to preserve and collect data from employee mobile devices, a high reliance on self-preservation to fulfill preservation obligations and a decent percentage of organizations using technology assisted review.

Mobile Device Preservation and Collection

  • 41% of companies had to preserve and/or collect data from an employee mobile device because of litigation or an investigation in 2012, up from 32% in 2011.
  • Similar increases were reported by respondents from larger companies (38% in 2011, up to 54% in 2012) and midsized companies (26% in 2011, up to 40% in 2012).  Only respondents from smaller companies reported a drop (from 26% to 14%).

Self-Preservation

  • 69% of companies rely on individuals preserving their own data (i.e., self-preservation) in any of their disputes or investigations.  Larger and mid-sized companies are more likely to utilize self-preservation (73% and 72% respectively) than smaller companies (52%).
  • 41% of companies use self-preservation in all of their matters, and 73% use it for half or more of all matters.
  • When not relying on self-preservation, 72% of respondents say they depend on the IT function to collect all data sources of pertinent custodians.
  • Reasons that respondents gave for not relying on self-preservation included: More cost effective and efficient not to rely on custodian 29%; Lack of compliance by custodians 24%; High profile matter 23%; High monetary or other exposure 22%; Need to conduct forensics 20%; Some or all custodians may have an incentive to improperly delete potentially relevant information; 18%; Case law does not support self-preservation 14% and High profile custodian 11%.

Technology Assisted Review

  • 35% of all respondents are using technology assisted review for at least some of their matters.  U.S. companies are more likely to employ technology-assisted review than their U.K. counterparts (40% versus 23%).
  • 43% of larger companies surveyed use technology assisted review, compared with 32% of mid-sized companies and 23% of the smaller companies.
  • Of those companies utilizing technology assisted review, 21% use it in all of their matters and 51% use it for half or more of their matters.

There are plenty more interesting stats and trends in the report, which is free(!).  To download your own copy of the report, click here.

So, what do you think?  Do any of those trends surprise you?  Please share any comments you might have or if you’d like to know more about a particular topic.

Disclaimer: The views represented herein are exclusively the views of the author, and do not necessarily represent the views held by CloudNine Discovery. eDiscoveryDaily is made available by CloudNine Discovery solely for educational purposes to provide general information about general eDiscovery principles and not to provide specific legal advice applicable to any particular circumstance. eDiscoveryDaily should not be used as a substitute for competent legal advice from a lawyer you have retained and who has agreed to represent you.