Essays

One of the things that I love about publishing is the chance to be wrong and learn something new. Our industry and the technology that drives it is changing at an amazing pace. In the closing of my recent post about using Exchange Journaling for ongoing preservation, I mentioned the potential advantages of Nearpoint’s granular capture over Journaling as well as the monkey wrench Microsoft threw at them a couple years back. Indeed, Microsoft does not support parsing or extracting email from the Exchange database and transaction logs, except through a specific set of protocols. They phased out support for one of the generic protocols, ESE or Extensible Storage Engine API. This forced 3rd party vendors to either adapt or try to take full responsibility to their customer’s Exchange systems.

A recent post to the Yahoo! LitSupport group asked whether there were any published standards covering email deduplication hashing. The problem is more complicated that it appears on the surface. As several other search experts commented, the definition of a duplicate email and the actions that you take will vary based on the jurisdiction, matter issues and party demands. Under FRCP Rule 34(b), the requesting party may define the format of production within the scope of the Rule 26(b) and the terms of your Rule 26(f) negotiations. Both rules provide some exemption for duplicative productions, but there are arguments that can be made about deduplication within a custodian’s email or across all custodians.

The Electronic Discovery Reference Model (EDRM) projects meet twice a year in St. Paul for a couple days of concentrated work. This year’s gathering feels a bit smaller than year’s past, but all the projects seem to be making good progress. There were a good number of new, first time participants from law firms and corporations, reflecting the growing number of dedicated eDiscovery managers and specialized counsel. After a short status report from the project leaders, everyone adjourned to their chosen projects to work.

As always, there were some interesting tidbits in the Fulbright & Jaworski Annual Litigation Trends Survey (this one was the 7th annual report). The report covers a lot of ground including trends for fee arrangement, the tactics for handling international disputes, the types of matters organizations face the most (and the different ways each type of matter impacts litigation costs), and eDiscovery. At eDJ, we looked for the nuggets of data about eDiscovery that are most interesting and compelling – there were severa

I was recently asked if I had a top ten list of guidelines are questions for a corporate legal department who is just starting to look at investing in some kind of information management, archiving or discovery solution. Although I do a lot of requirements and RFP engagements, I am usually brought in after the internal committee already has some idea of what they want. However, I frequently encounter clients that have latched onto a solution label without a true understanding of their own key pain points or what it will take to realize the ROI promised by this new technology. First and foremost, these corporations are already ahead of the curve because they have recognized the need and have made the decision to do something about it. Rather than trying to tackle all the different options and issues that span the different technology solutions, I want to limit this discussion to eDiscovery related technology, mostly because I believe in sticking to what I know best.

Recently, Recommind briefed eDiscoveryJournal on the software vendor’s predictive coding. In the Recommind context, predictive coding starts with a sub-set of data (derived by various techniques such as concept searching, phrase identification, keyword searching, metadata filters, etc) and users review and code the seed data set for factors such as responsiveness, issue, and privilege. Once that review is complete, the user can hit a “train” button that tells the Axcelerate application to identify conceptually similar documents based on the attributes of the first set of coding. Recommind refers to this as machine learning – the engine learns from the document coding conducted by humans; and vice-versa, with predictive coding the human reviewers also learn from the suggested relevant documents that are returned by the machine. Basically, it is a process where reviewers are presented with more relevant documents, more often and see much less non-relevant document that slow down the process of completing review. There are checks built in so that case managers can continue to review sets of potentially low relevance documents. If any of those documents are in fact responsive, they are re-coded and the system can apply this learning back to the rest of the corpus.

Corporations continue to acquire eDiscovery technology as they slowly convert from reactive fire drills to proactive business processes. The majority of early eDiscovery processing and hosting platforms available to service providers carried a relatively high per GB license cost. This model drove many service providers to develop their own software to remain competitive when prices suddenly dropped from $2,000 per GB down to the current $400-600 per GB. Now many of these providers have packaged their toolboxes into commercial software and are trying to convert service customers to software sales. All of this gives buyers an overabundance of choices when creating an RFP. It certainly keeps me busy with briefings and demos of new products every week. An offhand remark from a savvy CTO sent me digging into the potential pitfalls of some current open source General Public Licenses that work on a Copyleft or pay it forward model.

We all know that Office 2007 and later files are a different file format from your traditional DOC/XLS/PPT files, but I thought that it was worth exploring them with an eye on their potential impact in eDiscovery activities. First we need a simple explanation of what changed from Office 2003 to Office 2007 formats. Prior to 2007, Word, Excel and Powerpoint files were each proprietary binary file formats that required the application or a viewer to open. Office 2007 adopted an XML-based file format called Office Open XML that uses a common set of XML files within a compressed Zip container. These Extensible Markup Language (XML) files are simple text files that resemble HTML. The files now have an X or M added to their traditional file extensions to indicate whether they are flat XML or if they have embedded macro content. So DOC, XLS and PPT have become DOCX/DOCM, XLSX/XLSM and PPTX/PPTM. There are many advantages to the open formats, but we will focus on the potential discovery impact.

While reviewing the hundreds of new blogs and stories found by the eDJ search engine over the weekend, I realized that we had reached 5,000 stories in just 9 months. That translates to almost 600 unique news or opinion pieces per month after we screen out the thousands of reposts and odd environmental stories that happen to mention “Chain of Custody”. Looking back, it appears that the number of blogs and stories have been steadily growing since my May post on the new user customized RSS feeds and home page display. We were clearing about 20 stories per day back then and I know that we have gotten a lot tougher in the screening process. The eDiscovery market and the legal system that it supports has definitely been warming the recession chill of 2009.

Our earlier post on predictive coding potentially being the future of document review included a poll on whether or not predictive coding is defensible. The results so far are somewhat surprising in that the majority of folks believe predictive coding is not defensible. I suppose I shouldn’t be surprised; the legal industry is slow to change (and for good reason – it makes sense to proceed with caution when going into unchartered territory).