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Doug Austin

eDiscovery Case Law: Conclusion of Case Does Not Preclude Later Sanctions

In Green v. Blitz U.S.A., Inc., (E.D. Tex. Mar. 1, 2011), the defendant in a product liability action that had been settled over a year earlier was sanctioned for “blatant discovery abuses” prior to the settlement. Defendant was ordered to add $250,000 to its settlement with plaintiff, to provide a copy of the court’s order to every plaintiff in every lawsuit against defendant for the past two years or else forfeit an additional $500,000 “purging” sanction, and to include the order in its first responsive pleading in every lawsuit for the next five years in which defendant became involved.

Defendant, a manufacturer of gasoline containers, was named in several product liability lawsuits, including this case in which plaintiff alleged that her husband’s death was caused in part by the lack of a flame arrestor on defendant’s gas cans. The jury in plaintiff’s case returned a verdict for defendant after counsel for defendant argued that “science shows” that flame arrestors did not work. The case was settled after the jury verdict for an undisclosed amount, but two years later, counsel for plaintiff sought sanctions and to have the case reopened after learning in another case against defendant that while the gas can lawsuits were underway, defendant had been instructing its employees to destroy email.

The court described defendant’s failure to implement a litigation hold as gas can cases were filed. A single employee met with other employees to ask them to look for documents, but he did not have any electronic searches made for documents and he did not consult with defendant’s information technology department on how to retrieve electronic documents.

The court held that defendant willfully violated the discovery order in the case by not producing key documents such as a handwritten note indicating a desire to install flame arrestors on gas cans and an email noting that the technology for flame arrestors existed given the common use of flame arrestors in the marine industry. “Any competent electronic discovery effort would have located this email,” according to the court, through a key word search. Defendant’s employee in charge of discovery did not conduct a key word search and, despite acknowledging that he was as computer “illiterate as they get,” did not seek help from defendant’s information technology department, which was routinely sending out instructions to employees to delete email and rotating backup tapes every two weeks while the litigation was underway.

The court declined to reopen the case since it had been closed for a year. However, based on its knowledge of the confidential settlement of the parties, the court ordered defendant to pay plaintiff an additional $250,000 as a civil contempt sanction to match the minimum amount that the settlement would have been if plaintiff had been provided documents withheld by defendant. The court also ordered a “civil purging sanction” of $500,000 which defendant could avoid upon showing proof that a copy of the court’s decision had been provided to every plaintiff in a lawsuit against defendant for the past two years. The court added a requirement that defendant include a copy of the court’s opinion in its first pleading in any lawsuit for the next five years in which defendant became a party.

As Yogi Berra would say, “It ain’t over ‘til it’s over”.

So, what do you think?  Should cases be re-opened after they’re concluded for discovery violations?  Please share any comments you might have or if you’d like to know more about a particular topic.

Case Summary Source: Applied Discovery (free subscription required).  For eDiscovery news and best practices, check out the Applied Discovery Blog here.

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.

eDiscovery Best Practices: Your ESI Collection May Be Larger Than You Think
 

Here’s a sample scenario: You identify custodians relevant to the case and collect files from each.  Roughly 100 gigabytes (GB) of Microsoft Outlook email PST files and loose “efiles” is collected in total from the custodians.  You identify a vendor to process the files to load into a review tool, so that you can perform first pass review and, eventually, linear review and produce the files to opposing counsel.  After processing, the vendor sends you a bill – and they’ve charged you to process over 200 GB!!  What happened?!?

Did the vendor accidentally “double-bill” you?  That would be great – but no.  There’s a much more logical explanation and, unfortunately, you may wind up paying a lot more to process these files that you expected.

Many of the files in most ESI collections are stored in what are known as “archive” or “container” files.  For example, as noted above, Outlook emails are typically saved for each custodian in a personal storage (.PST) file format, which is an expanding container file. For most custodians, all of their email (and the corresponding attachments, if present) resides in a few PST files.  The scanned size for the PST file is the size of the file on disk.

Did you ever see one of those vacuum bags that you store clothes in and then suck all the air out so that the clothes won’t take as much space?  The PST file is like one of those vacuum bags – it typically stores the emails and attachments in a compressed format to save space.  When the emails and attachments are processed into a review tool, they are expanded into their normal size.  This expanded size can be 1.5 to 2 times larger than the scanned size (or more).  And, that’s what many vendors will bill on – the expanded size.

There are other types of archive container files that compress the contents – .zip and .rar files are two examples of compressed container files.  These files are often used to not only to compress files for storage on hard drives, but they are also used to compact or group a set of files when transmitting them, usually in – you guessed it – email.  With email comprising a majority of most ESI collections and the popularity of other archive container files for compressing file collections, the expanded size of your collection may be considerably larger than it appears when stored on disk.  It’s important to be prepared for that and know your options when processing that data, so you can effectively anticipate those processing costs.

So, what do you think?  Have you ever been surprised by processing costs of your ESI?   Please share any comments you might have or if you’d like to know more about a particular topic.

eDiscovery Trends: Jurors and Social Media Don’t Mix

Discovery of social media is continuing to increase as a significant issue for organizations to address, with more and more cases addressing the topic, including this one and this one that have reached various conclusions regarding the discoverability of social media.  However, when it comes to social media, courts agree on one thing: jurors and social media don’t mix.  Courts have consistently rejected attempts by jurors to use social technology to research or to communicate about a case, and have increasingly provided pre-trial and post-closing jury instructions to jurors to dissuade them from engaging in this practice.

A recent example of juror misconduct related to social media is this case, where one of the jurors actually attempted to “Friend” one of the defendants on Facebook.  With so much information at our disposal these days and so many ways to communicate, some jurors can be easily tempted to ignore court instructions and behave badly.

At its December 2009 meeting, the Judicial Conference Committee on Court Administration and Case Management (CACM) endorsed a set of suggested jury instructions for district judges to consider using to help deter jurors from using electronic technologies to research or communicate about cases on which they serve.  These proposed instructions were published in thisMemorandum in late January.  These instructions were designed to prevent jurors from two activities:

  1. Independently researching a case, including through the internet or other electronic means,
  2. Communicating about the case, including by electronic means such as email or social media sites such as Facebook.

Several states, such as California and New York, have crafted and adopted their own instructions to regulate the use of social media and other electronic means to research a case.  It seems like a “no-brainer” that every state will eventually be forced to promote or adopt such instructions.  Of course, it also seems like a “no-brainer” for jurors to refrain from such activities anyway, but I guess this is the world we live in today, right?

So, what do you think?  Does your state have standard jury instructions prohibiting social media use?   Please share any comments you might have or if you’d like to know more about a particular topic.

eDiscovery Best Practices: Testing Your Search Using Sampling

Friday, we talked about how to determine an appropriate sample size to test your search results as well as the items NOT retrieved by the search, using a site that provides a sample size calculator.  Yesterday, we talked about how to make sure the sample size is randomly selected.

Today, we’ll walk through an example of how you can test and refine a search using sampling.

TEST #1: Let’s say in an oil company we’re looking for documents related to oil rights.  To try to be as inclusive as possible, we will search for “oil” AND “rights”.  Here is the result:

  • Files retrieved with “oil” AND “rights”: 200,000
  • Files NOT retrieved with “oil” AND “rights”: 1,000,000

Using the site to determine an appropriate sample size that we identified before, we determine a sample size of 662 for the retrieved files and 664 for the non-retrieved files to achieve a 99% confidence level with a margin of error of 5%.  We then use this site to generate random numbers and then proceed to review each item in the retrieved and NOT retrieved items sets to determine responsiveness to the case.  Here are the results:

  • Retrieved Items: 662 reviewed, 24 responsive, 3.6% responsive rate.
  • NOT Retrieved Items: 664 reviewed, 661 non-responsive, 99.5% non-responsive rate.

Nearly every item in the NOT retrieved category was non-responsive, which is good.  But, only 3.6% of the retrieved items were responsive, which means our search was WAY over-inclusive.  At that rate, 192,800 out of 200,000 files retrieved will be NOT responsive and will be a waste of time and resource to review.  Why?  Because, as we determined during the review, almost every published and copyrighted document in our oil company has the phrase “All Rights Reserved” in the document and will be retrieved.

TEST #2: Let’s try again.  This time, we’ll conduct a phrase search for “oil rights” (which requires those words as an exact phrase).  Here is the result:

  • Files retrieved with “oil rights”: 1,500
  • Files NOT retrieved with “oil rights”: 1,198,500

This time, we determine a sample size of 461 for the retrieved files and (again) 664 for the NOT retrieved files to achieve a 99% confidence level with a margin of error of 5%.  Even though, we still have a sample size of 664 for the NOT retrieved files, we generate a new list of random numbers to review those items, as well as the 461 randomly selected retrieved items.  Here are the results:

  • Retrieved Items: 461 reviewed, 435 responsive, 94.4% responsive rate.
  • NOT Retrieved Items: 664 reviewed, 523 non-responsive, 78.8% non-responsive rate.

Nearly every item in the retrieved category was responsive, which is good.  But, only 78.8% of the NOT retrieved items were responsive, which means over 20% of the NOT retrieved items were actually responsive to the case (we also failed to retrieve 8 of the items identified as responsive in the first iteration).  So, now what?

TEST #3: If you saw this previous post, you know that proximity searching is a good alternative for finding hits that are close to each other without requiring the exact phrase.  So, this time, we’ll conduct a proximity search for “oil within 5 words of rights”.  Here is the result:

  • Files retrieved with “oil within 5 words of rights”: 5,700
  • Files NOT retrieved with “oil within 5 words of rights”: 1,194,300

This time, we determine a sample size of 595 for the retrieved files and (once again) 664 for the NOT retrieved files, generating a new list of random numbers for both sets of items.  Here are the results:

  • Retrieved Items: 595 reviewed, 542 responsive, 91.1% responsive rate.
  • NOT Retrieved Items: 664 reviewed, 655 non-responsive, 98.6% non-responsive rate.

Over 90% of the items in the retrieved category were responsive AND nearly every item in the NOT retrieved category was non-responsive, which is GREAT.  Also, all but one of the items previously identified as responsive was retrieved.  So, this is a search that appears to maximize recall and precision.

Had we proceeded with the original search, we would have reviewed 200,000 files – 192,800 of which would have been NOT responsive to the case.  By testing and refining, we only had to review 8,815 files –  3,710 sample files reviewed plus the remaining retrieved items from the third search (5,700 – 595 = 5,105) – most of which ARE responsive to the case.  We saved tens of thousands in review costs while still retrieving most of the responsive files, using a defensible approach.

Keep in mind that this is a simple example — we’re not taking into account misspellings and other variations we may want to include in our criteria.

So, what do you think?  Do you use sampling to test your search results?   Please share any comments you might have or if you’d like to know more about a particular topic.

eDiscovery Best Practices: A “Random” Idea on Search Sampling
 

Friday, we talked about how to determine an appropriate sample size to test your search results as well as the items NOT retrieved by the search, using a site that provides a sample size calculator.  Today, we’ll talk about how to make sure the sample size is randomly selected.

A randomly selected sample gives each file an equal chance of being reviewed and eliminates the chance of bias being introduced into the sample which might skew the results.  Merely selecting the first or last x number of items (or any other group) in the set may not reflect the population as a whole – for example, all of those items could come from a single custodian.  To ensure a fair, defensible sample, it needs to be selected randomly.

So, how do you select the numbers randomly?  Once again, the Internet helps us out here.

One site, Random.org, has a random integer generator which will randomly generate whole numbers.  You simply need to supply the number of random integers that you need to be generated, the starting number and ending number of the range within which the randomly generated numbers should fall.  The site will then generate a list of numbers that you can copy and paste into a text file or even a spreadsheet.  The site also provides an Advanced mode, which provides options for the numbers (e.g., decimal, hexadecimal), output format and how the randomization is ‘seeded’ (to generate the numbers).

In the example from Friday, you would provide 660 as the number of random integers to be generated, with a starting number of 1 and an ending number of 100,000 to get a list of random numbers for testing your search that yielded 100,000 files with hits (664, 1 and 1,000,000 respectively to get a list of numbers to test the non-hits).  You could paste the numbers into a spreadsheet, sort them and then retrieve the files by position in the result set based on the random numbers retrieved and review each of them to determine whether they reflect the intent of the search.  You’ll then have a good sense of how effective your search was, based on the random sample.  And, probably more importantly, using that random sample to test your search results will be a highly defensible method to verify your approach in court.

Tomorrow, we'll walk through a sample iteration to show how the sampling will ultimately help us refine our search.

So, what do you think?  Do you use sampling to test your search results?   Please share any comments you might have or if you’d like to know more about a particular topic.

eDiscovery Best Practices: Determining Appropriate Sample Size to Test Your Search
 

We’ve talked about searching best practices quite a bit on this blog.  One part of searching best practices (as part of the “STARR” approach I described in an earlier post) is to test your search results (both the result set and the files not retrieved) to determine whether the search you performed is effective at maximizing both precision and recall to the extent possible, so that you retrieve as many responsive files as possible without having to review too many non-responsive files.  One question I often get is: how many files do you need to review to test the search?

If you remember from statistics class in high school or college, statistical sampling is choosing a percentage of the results population at random for inspection to gather information about the population as a whole.  This saves considerable time, effort and cost over reviewing every item in the results population and enables you to obtain a “confidence level” that the characteristics of the population reflect your sample.  Statistical sampling is a method used for everything from exit polls to predict elections to marketing surveys to poll customers on brand popularity and is a generally accepted method of drawing conclusions for an overall results population.  You can sample a small portion of a large set to obtain a 95% or 99% confidence level in your findings (with a margin of error, of course).

So, does that mean you have to find your old statistics book and dust off your calculator or (gasp!) slide rule?  Thankfully, no.

There are several sites that provide sample size calculators to help you determine an appropriate sample size, including this one.  You’ll simply need to identify a desired confidence level (typically 95% to 99%), an acceptable margin of error (typically 5% or less) and the population size.

So, if you perform a search that retrieves 100,000 files and you want a sample size that provides a 99% confidence level with a margin of error of 5%, you’ll need to review 660 of the retrieved files to achieve that level of confidence in your sample (only 383 files if a 95% confidence level will do).  If 1,000,000 files were not retrieved, you would only need to review 664 of the not retrieved files to achieve that same level of confidence (99%, with a 5% margin of error) in your sample.  As you can see, the sample size doesn’t need to increase much when the population gets really large and you can review a relatively small subset to understand your collection and defend your search methodology to the court.

On Monday, we will talk about how to randomly select the files to review for your sample.  Same bat time, same bat channel!

So, what do you think?  Do you use sampling to test your search results?   Please share any comments you might have or if you’d like to know more about a particular topic.

eDiscovery Case Law: Destroy Data, Pay $1 Million, Lose Case

A federal judge in Chicago has levied sanctions against Rosenthal Collins Group LLC and granted a default judgment to the defendant for misconduct in a patent infringement case, also ordering the Chicago-based futures broker’s counsel to pay “the costs and attorneys fees incurred in litigating this motion” where plaintiff’s agent modified metadata related to relevant source code and wiped several relevant disks and devices prior to their production and where the court found counsel participated in “presenting misleading, false information, materially altered evidence and willful non-compliance with the Court’s orders.”

In Rosenthal Collins Group, LLC v. Trading Techs. Int’l, No. 05 C 4088, 2011 WL 722467 (N.D. Ill. Feb. 23, 2011), U.S. District Judge Sharon Johnson Coleman assessed a sanction of $1 million to Rosenthal Collins (RCG) and granted defendant/counter-plaintiff Trading Technologies’ (TT) motion for evidentiary sanctions and default judgment.  Much of the reason was due to the actions of RCG’s agent, Walter Buist.  Here’s why:

  • During Buist’s deposition, he admitted to “turning back the clock” to change the “last modified” date on the previously modified source code to make it appear that the modifications had occurred much earlier.  Despite clear evidence of these facts, RCG continued to deny them, even calling the claims “libelous,” “audacious,” and “Oliver Stone-esque.”
  • Buist also later admitted “wiping” six of seven zip disks that originally contained the relevant source code.  While he did not admit wiping the seventh disk, it was also wiped, and the Court found that it was “impossible to believe that it is merely coincidence that the seventh disk happened to be wiped on May 2, 2006, which just happened to be the same day that TT was scheduled to inspect it.”
  • The Court found that here was evidence that “virtually every piece of media ordered produced by the Court in May 2007 and July 2008 was wiped, altered, or destroyed.”
  • Despite RCG’s (and its counsel’s) attempts to distance itself from “its own agent, employed for the purposes of pursuing this litigation” and disavowing any “actual knowledge” of wrongdoing, Buist was RCG’s agent and, therefore, RCG was bound by Buist’s behavior and actions.
  • Even if RCG and its counsel had no knowledge of the destruction of the evidence, the destruction might have been avoided if RCG had complied with the Court’s orders in a timely manner to produce the materials and/or preserved the evidence by taking custody of it.

So, what do you think?  Should parties and their counsel be liable for the actions of an agent on their behalf?  Please share any comments you might have or if you’d like to know more about a particular topic.

eDiscovery Trends: Forbes on the Rise of Predictive Coding
 

First the New York Times with an article about eDiscovery, now Forbes.  Who’s next, The Wall Street Journal?  😉

Forbes published a blog post entitled E-Discovery And the Rise of Predictive Coding a few days ago.  Written by Ben Kerschberg, Founder of Consero Group LLC, it gets into some legal issues and considerations regarding predictive coding that are interesting.  For some background on predictive coding, check out our December blog posts, here and here.

First, the author provides a very brief history of document review, starting with bankers boxes and WordPerfect and “[a]fter an interim phase best characterized by simple keyword searches and optical character recognition”, it evolved to predictive coding.  OK, that’s like saying that Gone with the Wind started with various suitors courting Scarlett O’Hara and after an interim phase best characterized by the Civil War, marriage and heartache, Rhett says to Scarlett, “Frankly, my dear, I don’t give a damn.”  A bit oversimplification of how review has evolved.

Nonetheless, the article gets into a couple of important legal issues raised by predictive coding.  They are:

  • Satisfying Reasonable Search Requirements: Whether counsel can utilize the benefits of predictive coding and still meet legal obligations to conduct a reasonable search for responsive documents under the federal rules.  The question is, what constitutes a reasonable search under Federal Rule 26(g)(1)(A), which requires that the responding attorney attest by signature that “with respect to a disclosure, it is complete and correct as of the time it is made”?
  • Protecting Privilege: Whether counsel can protect attorney-client privilege for their client when a privileged document is inadvertently disclosed.  Fed. Rule of. Evidence 502 provides that a court may order that a privilege or protection is not waived by disclosure if the disclosure was inadvertent and the holder of the privilege took reasonable steps to prevent disclosure.  Again, what’s reasonable?

The author concludes that the use of predictive coding is reasonable, because it a) makes document review more efficient by providing only those documents to the reviewer that have been selected by the algorithm; b) makes it more likely that responsive documents will be produced, saving time and resources; and c) refines relevant subsets for review, which can then be validated statistically.

So, what do you think?  Does predictive coding enable attorneys to satisfy these legal issues?   Is it reasonable?  Please share any comments you might have or if you’d like to know more about a particular topic.

eDiscovery Best Practices: Does Size Matter?
 

I admit it, with a title like “Does Size Matter?”, I’m looking for a few extra page views….  😉

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.  Without FirstPass™, powered by Venio FPR™ to cull the data and OnDemand® to manage the linear review, they would not have been able to make that deadline.  So, they clearly benefited from the use of eDiscovery technology in that case.

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?

I recently conducted a webinar regarding the benefits of First Pass Review – aka Early Case Assessment, or a more accurate term (as George Socha points out regularly), Early Data Assessment.  One of the topics discussed in that webinar was the cost of review for each gigabyte (GB).  Extrapolated from an analysis conducted by Anne Kershaw a few years ago (and published in the Gartner report E-Discovery: Project Planning and Budgeting 2008-2011), here is a breakdown:

Estimated Cost to Review All Documents in a GB:

  • Pages per GB:                75,000
  • Pages per Document:      4
  • Documents Per GB:        18,750
  • Review Rate:                 50 documents per hour
  • Total Review Hours:       375
  • Reviewer Billing Rate:     $50 per hour

Total Cost to Review Each GB:      $18,750

Notes: The number of pages per GB can vary widely.  Page per GB estimates tend to range from 50,000 to 100,000 pages per GB, so 75,000 pages (18,750 documents) seems an appropriate average.  50 documents reviewed per hour is considered to be a fast review rate and $50 per hour is considered to be a bargain price.  eDiscovery Daily provided an earlier estimate of $16,650 per GB based on assumptions of 20,000 documents per GB and 60 documents reviewed per hour – the assumptions may change somewhat, but, either way, the cost for attorney review of each GB could be expected to range from at least $16,000 to $18,000, possibly more.

Advanced culling and searching capabilities of First Pass Review tools like FirstPass can enable you to cull out 70-80% of most collections as clearly non-responsive without having to conduct attorney review on those files.  If you have merely a 2 GB collection and assume the lowest review cost above of $16,000 per GB, the use of a First Pass Review tool to cull out 70% of the collection can save $22,400 in attorney review costs.  Is that worth it?

So, what do you think?  Do you use eDiscovery technology for only the really large cases or ALL cases?   Please share any comments you might have or if you’d like to know more about a particular topic.

eDiscovery Best Practices: Is Disclosure of Search Terms Required?
 

I read a terrific article a couple of days ago from the New York Law Journal via Law Technology News entitled Search Terms Are More Than Mere Words, that had some interesting takes about the disclosure of search terms in eDiscovery.  The article was written by David J. Kessler, Robert D. Owen, and Emily Johnston of Fulbright & Jaworski.  The primary emphasis of the article was with regard to the forced disclosure of search terms by courts.

In the age of “meet and confer”, it has become much more common for parties to agree to exchange search terms in a case to limit costs and increase transparency.  However, as the authors correctly note, search terms reflect counsel’s strategy for the case and, therefore, work product.  Their position is that courts should not force disclosure of search terms and that disclosure of terms is “not appropriate under the Federal Rules of Civil Procedure”.  The article provides a compelling argument as to why forced disclosure is not appropriate and provides some good case cites where courts have accepted or rejected requests to compel provision of search terms.  I won’t try to recap them all here – check out the article for more information.

So, should disclosure of search terms be generally required?  If not, what does that mean in terms of utilizing a defensible approach to searching?

Personally, I agree with the authors that forced disclosure of search terms is generally not appropriate, as it does reflect strategy and work product.  However, there is an obligation for each party to preserve, collect, review and produce all relevant materials to the best of their ability (that are not privileged, of course).  Searching is an integral part of that process.  And, the article does note that “chosen terms may come under scrutiny if there is a defect in the production”, though “[m]ere speculation or unfounded accusations” should not lead to a requirement to disclose search terms.

With that said, the biggest component of most eDiscovery collections today is email, and that email often reflects discussions between parties in the case.  In these cases, it’s much easier for opposing counsel to identify legitimate defects in the production because they have some of the same correspondence and documents and can often easily spot discrepancies in the production set.  If they identify legitimate omissions from the production, those omissions could cause the court to call into question your search procedures.  Therefore, it’s important to conduct a defensible approach to searching (such as the “STARR” approach I described in an earlier post) to be able to defend yourself if those questions arise.  Demonstrating a defensible approach to searching will offer the best chance to preserve your rights to protect your work product of search terms that reflect your case strategy.

So, what do you think?  Do you think that forced disclosure of search terms is appropriate?   Please share any comments you might have or if you’d like to know more about a particular topic.