e-discovery 2.0

2012 is the Year of the Dragon – which is fitting, since no other Chinese Zodiac sign represents the promise, challenge, and evolution of predictive coding technology more than the Dragon.  The few who have embraced predictive coding technology exemplify symbolic traits of the Dragon that include being unafraid of challenges and willing to take risks.  In the legal profession, taking risks typically isn’t in a lawyer’s DNA, which might explain why predictive coding technology has seen lackluster adoption among lawyers despite the hype.  This blog explores the promise of predictive coding technology, why predictive coding has not been widely adopted in eDiscovery, and explains why 2012 is likely to be remembered as the year of predictive coding.

What is predictive coding?

Predictive coding refers to machine learning technology that can be used to automatically predict how documents should be classified based on limited human input.  In litigation, predictive coding technology can be used to rank and then “code” or “tag” electronic documents based on criteria such as “relevance” and “privilege” so organizations can reduce the amount of time and money spent on traditional page by page attorney document review during discovery.

Generally, the technology works by prioritizing the most important documents for review by ranking them.  In addition to helping attorneys find important documents faster, this prioritization and ranking of documents can even eliminate the need to review documents with the lowest rankings in certain situations. Additionally, since computers don’t get tired or day dream, many believe computers can even predict document relevance better than their human counterparts.

Why hasn’t predictive coding gone mainstream yet?

Given the promise of faster and less expensive document review, combined with higher accuracy rates, many are perplexed as to why predictive coding technology hasn’t been widely adopted in eDiscovery.  The answer really boils down to one simple concept – a lack of transparency.

Difficult to Use

First, early predictive coding tools attempt to apply a complicated new technological approach to a document review process that has traditionally been very simple.  Instead of relying on attorneys to read each and every document to determine relevance, the success of today’s predictive coding technology typically depends on review decisions input into a computer by one or more experienced senior attorneys.  The process commonly involves a complex series of steps that include sampling, testing, reviewing, and measuring results in order to fine tune an algorithm that will eventually be used to predict the relevancy of the remaining documents.

The problem with early predictive coding technologies is that the majority of these complex steps are done in a ‘black box’.  In other words, the methodology and results are not always clear, which increases the risk of human error and makes the integrity of the electronic discovery process difficult to defend.  For example, the methodology for selecting a statistically relevant sample is not always intuitive to the end user.  This fundamental problem could result in improper sampling techniques that could taint the accuracy of the entire process.  Similarly, the process must often be repeated several times in order to improve accuracy rates.  Even if accuracy is improved, it may be difficult or impossible to explain how accuracy thresholds were determined or to explain why coding decisions were applied to some documents and not others.

Accuracy Concerns

Early predictive coding tools also tend to lack transparency in the way the technology evaluates the language contained in each document.  Instead of evaluating both the text and metadata fields within a document, some technologies actually ignore document metadata.  This omission means a privileged email sent by a client to her attorney, Larry Lawyer, might be overlooked by the computer if the name “Larry Lawyer” is only part of the “recipient” metadata field of the document and isn’t part of the document text.  The obvious risk is that this situation could lead to privilege waiver if it is inadvertently produced to the opposing party.

Another practical concern is that some technologies do not allow reviewers to make a distinction between relevant and non-relevant language contained within individual documents.  For example, early predictive coding technologies are not intelligent enough to know that only the second paragraph on page 95 of a 100-page document contains relevant language.  The inability to discern what language  led to the determination that the document is relevant could skew results when the computer tries to identify other documents with the same characteristics.  This lack of precision increases the likelihood that the computer will retrieve an over-inclusive number of irrelevant documents.  This problem is generally referred to as ‘excessive recall,’ and it is important because this lack of precision increases the number of documents requiring manual review which directly impacts eDiscovery cost.

Waiver & Defensibility

Perhaps the biggest concern with early predictive coding technology is the risk of waiver and concerns about defensibility.  Notably, there have been no known judicial decisions that specifically address the defensibility of these new technology tools even though some in the judiciary, including U.S. Magistrate Judge Andrew Peck, have opined that this kind of technology should be used in certain cases.

The problem is that today’s predictive coding tools are difficult to use, complicated for the average attorney, and the way they work simply isn’t transparent.  All these limitations increase the risk of human error.  Introducing human error increases the risk of overlooking important documents or unwittingly producing privileged documents.  Similarly, it is difficult to defend a technological process that isn’t always clear in an era where many lawyers are still uncomfortable with keyword searches.  In short, using black box technology that is difficult to use and understand is perceived as risky, and many attorneys have taken a wait-and-see approach because they are unwilling to be the guinea pig.

Why is 2012 likely to be the year of predictive coding?

The word transparency may seem like a vague term, but it is the critical element missing from today’s predictive coding technology offerings.  2012 is likely to be the year of predictive coding because improvements in transparency will shine a light into the black box of predictive coding technology that hasn’t existed until now.  In simple terms, increasing transparency will simplify the user experience and improve accuracy which will reduce longstanding concerns about defensibility and privilege waiver.

Ease of Use

First, transparent predictive coding technology will help minimize the risk of human error by incorporating an intuitive user interface into a complicated solution.  New interfaces will include easy-to-use workflow management consoles to guide the reviewer through a step-by-step process for selecting, reviewing, and testing data samples in a way that minimizes guesswork and confusion.  By automating the sampling and testing process, the risk of human error can be minimized which decreases the risk of waiver or discovery sanctions that could result if documents are improperly coded.  Similarly, automated reporting capabilities make it easier for producing parties to evaluate and understand how key decisions were made throughout the process, thereby making it easier for them to defend the reasonableness of their approach.

Intuitive reports also help the producing party measure and evaluate confidence levels throughout the testing process until appropriate confidence levels are achieved.  Since confidence levels can actually be measured as a percentage, attorneys and judges are in a position to negotiate and debate the desired level of confidence for a production set rather than relying exclusively on the representations or decisions of a single party.  This added transparency allows the type of cooperation between parties called for in the Sedona Cooperation Proclamation and gives judges an objective tool for evaluating each party’s behavior.

Accuracy & Efficiency

2012 is also likely to be the year of transparent predictive coding technology because technical limitations that have impacted the accuracy and efficiency of earlier tools will be addressed.  For example, new technology will analyze both document text and metadata to avoid the risk that responsive or privileged documents are overlooked.  Similarly, smart tagging features will enable reviewers to highlight specific language in documents to determine a document’s relevance or non-relevance so that coding predictions will be more accurate and fewer non-relevant documents will be recalled for review.

Conclusion - Transparency Provides Defensibility

The bottom line is that predictive coding technology has not enjoyed widespread adoption in the eDiscovery process due to concerns about simplicity and accuracy that breed larger concerns about defensibility.  Defending the use of black box technology that is difficult to use and understand is a risk that many attorneys simply are not willing to take, and these concerns have deterred widespread adoption of early predictive coding technology tools.  In 2012, next generation transparent predictive coding technology will usher in a new era of computer-assisted document review that is easy to use, more accurate, and easier to defend. Given these exciting technological advancements, I predict that 2012 will not only be the year of the dragon, it will also be the year of predictive coding.

As 2011 comes quickly to a close we’ve attempted, as in years past, to do our best Carnac impersonation and divine the future of eDiscovery.  Some of these predictions may happen more quickly than others, but it’s our sense that all will come to pass in the near future – it’s just a matter of timing.

1. Technology Assisted Review (TAR) Gains Speed.  The area of Technology Assisted Review is very exciting since there are a host of emerging technologies that can help make the review process more efficient, ranging from email threading, concept search, clustering, predictive coding and the like.  There are two fundamental challenges however.  First, the technology doesn’t work in a vacuum, meaning that the workflows need to be properly designed and the users need to make accurate decisions because those judgment calls often are then magnified by the application.  Next, the defensibility of the given approach needs to be well vetted.  While it’s likely not necessary (or practical) to expect a judge to mandate the use of a specific technological approach, it is important for the applied technologies to be reasonable, transparent and auditable since the worst possible outcome would be to have a technology challenged and then find the producing party unable to adequately explain their methodology.
2. The Custodian-Based Collection Model Comes Under Stress. Ever since the days of Zubulake, litigants have focused on “key players” as a proxy for finding relevant information during the eDiscovery process.  Early on, this model worked particularly well in an email-centric environment.  But, as discovery from cloud sources, collaborative worksites (like SharePoint) and other unstructured data repositories continues to become increasingly mainstream, the custodian-oriented collection model will become rapidly outmoded because it will fail to take into account topically-oriented searches.  This trend will be further amplified by the bench’s increasing distrust of manual, custodian-based data collection practices and the presence of better automated search methods, which are particularly valuable for certain types of litigation (e.g., patent disputes, product liability cases).
3. The FRCP Amendment Debate Will Rage On – Unfortunately Without Much Near Term Progress. While it is clear that the eDiscovery preservation duty has become a more complex and risk laden process, it’s not clear that this “pain” is causally related to the FRCP.  In the notes from the Dallas mini-conference, a pending Sedona survey was quoted referencing the fact that preservation challenges were increasing dramatically.  Yet, there isn’t a consensus viewpoint regarding which changes, if any, would help improve the murky problem.  In the near term this means that organizations with significant preservation pains will need to better utilize the rules that are on the books and deploy enabling technologies where possible.
4. Data Hoarding Increasingly Goes Out of Fashion. The war cry of many IT professionals that “storage is cheap” is starting to fall on deaf ears.  Organizations are realizing that the cost of storing information is just the tip of the iceberg when it comes to the litigation risk of having terabytes (and conceivably petabytes) of unstructured, uncategorized and unmanaged electronically stored information (ESI).  This tsunami of information will increasingly become an information liability for organizations that have never deleted a byte of information.  In 2012, more corporations will see the need to clean out their digital houses and will realize that such cleansing (where permitted) is a best practice moving forward.  This applies with equal force to the US government, which has recently mandated such an effort at President Obama’s behest.
5. Information Governance Becomes a Viable Reality.  For several years there’s been an effort to combine the reactive (far right) side of the EDRM with the logically connected proactive (far left) side of the EDRM.  But now, a number of surveys have linked good information governance hygiene with better response times to eDiscovery requests and governmental inquires, as well as a corresponding lower chance of being sanctioned and the ability to turn over less responsive information.  In 2012, enterprises will realize that the litigation use case is just one way to leverage archival and eDiscovery tools, further accelerating adoption.
6. Backup Tapes Will Be Increasingly Seen as a Liability.  Using backup tapes for disaster recovery/business continuity purposes remains a viable business strategy, although backing up to tape will become less prevalent as cloud backup increases.  However, if tapes are kept around longer than necessary (days versus months) then they become a ticking time bomb when a litigation or inquiry event crops up.
7. International eDiscovery/eDisclosure Processes Will Continue to Mature. It’s easy to think of the US as dominating the eDiscovery landscape. While this is gospel for us here in the States, international markets are developing quickly and in many ways are ahead of the US, particularly with regulatory compliance-driven use cases, like the UK Bribery Act 2010.  This fact, coupled with the menagerie of international privacy laws, means we’ll be less Balkanized in our eDiscovery efforts moving forward since we do really need to be thinking and practicing globally.
8. Email Becomes “So 2009” As Social Media Gains Traction. While email has been the eDiscovery darling for the past decade, it’s getting a little long in the tooth.  In the next year, new types of ESI (social media, structured data, loose files, cloud context, mobile device messages, etc.) will cause headaches for a number of enterprises that have been overly email-centric.  Already in 2011, organizations are finding that other sources of ESI like documents/files and structured data are rivaling email in importance for eDiscovery requests, and this trend shows no signs of abating, particularly for regulated industries. This heterogeneous mix of ESI will certainly result in challenges for many companies, with some unlucky ones getting sanctioned because they ignored these emerging data types.
9. Cost Shifting Will Become More Prevalent – Impacting the “American Rule.” For ages, the American Rule held that producing parties had to pay for their production costs, with a few narrow exceptions.  Next year we’ll see even more courts award winning parties their eDiscovery costs under 28 U.S.C. §1920(4) and Rule 54(d)(1) FRCP. Courts are now beginning to consider the services of an eDiscovery vendor as “the 21st Century equivalent of making copies.”
10. Risk Assessment Becomes a Critical Component of eDiscovery. Managing risk is a foundational underpinning for litigators generally, but its role in eDiscovery has been a bit obscure.  Now, with the tremendous statistical insights that are made possible by enabling software technologies, it will become increasingly important for counsel to manage risk by deciding what types of error/precision rates are possible.  This risk analysis is particularly critical for conducting any variety of technology assisted review process since precision, recall and f-measure statistics all require a delicate balance of risk and reward.

Picking five out of the sea of electronic discovery cases isn’t as easy as it sounds.  Sure, a few, like our “Case of the Year” will be no-brainers, but others aren’t as clear cut.  And, they’re certainly open to debate.  But, in my humble opinion here’s THE list, counting down David Letterman style:

5) Mancia v. Mayflower Textile Servs. Co., 2008 WL 4595175 (D. Md. Oct. 15, 2008)

If there ever was an opinion written by a judge to make a larger societal point, Mancia was certainly it.  Judge Paul Grimm, who’ll appear on this list in another slot as well, has clearly taken the mantle from Judge Scheindlin as the leading electronic discovery jurist.  He’d heretofore authored a number of significant opinions in this area, including Hobson and Thompson. Now, in Mancia he used a garden variety discovery dispute, which was typically rife with boilerplate objections and other obstreperous tactics, to highlight the Sedona Conference’s Cooperation Proclamation.

The lasting takeaway from the opinion is the notion that “[c]ourts repeatedly have noted the need for attorneys to work cooperatively to conduct electronic discovery, and sanctioned lawyers and parties for failing to do so.” To support this notion he cites the Sedona Conference Proclamation and the little used FRCP 26(g).  This opinion is noteworthy because it gives precedent to bolster the Sedona initiative and should provide a ready citation for all those counsel who aren’t getting the level of cooperation they need from the opposition.  It remains to be seen if other judges will follow suit, but this could be the beachhead for a more cooperative electronic discovery process in 2009 and beyond.

4) Flagg v. City of Detroit, 252 F.R.D. 346 (E.D. Mich. 2008)

Flagg highlights the growing need to reconcile the electronic discovery landscape, which typically focuses somewhat myopically on email, with the larger informational trends which are now categorized by the use of blogs, social networking sites, instant messaging, and text messaging.  Flagg was one of the first to determine text messages (e.g., messages exchanged among certain officials and employees of the City of Detroit via city-issued text messaging devices) were discoverable under the standards of FRCP 26(b)(1).  The holding further demonstrated the challenges of conducting electronic discovery across information systems that mix personal information with business communications.  This type of information commingling will continue to escalate, causing significant long term electronic discovery challenges due to thorny privacy, privilege and policy implications.

3) Rhoads Indus., Inc. v. Bldg. Materials Corp. of Am., 2008 WL 4916026 (E.D. Pa. Nov. 14, 2008)

Rhoads is one of the first cases post Federal Rule of Evidence (FRE) 502, which recently created a national standard (versus the previous split in jurisdictions) and now states a “middle ground” for the determining of inadvertent disclosure during electronic discovery.  The key provision is (b)(2) which provides protection only if “the holder of the privilege or protection took reasonable steps to prevent disclosure.”  So, Rhoads took that “reasonableness” question head on in a scenario where the plaintiff Rhoads admittedly (yet inadvertently) produced over eight hundred privileged, electronic documents.  The decision is significant because it used the five-factor test stated in Fidelity, but put an undue weighting on the final test which was: “whether the overriding interests of justice would be served by relieving the party of its errors.”   This approach potentially threatens the development of sound case law that will be necessary to help the deployment of FRE 502 into practice because it casts too much uncertainty with its weighting of “fairness” (a problematically vague notion) in the analysis.  It will be interesting to see if/how this approach is subsequently adopted as we enter the New Year.

2) Qualcomm Inc. v. Broadcom Corp., 2008 WL 66932 (S.D. Cal. Jan. 7, 2008)

This for many was the case of the year given it’s far reaching implications for the legal community.  Some have argued that this isn’t an e-discovery abuse case per se, but more of an example of discovery abuses that just so happened to be centered around ESI.  In either case, the fraud, resulting cover-up, sanctions, ethical issues and privilege discussions made for insightful and thought provoking reading throughout 2008.  The lasting takeaway from Qualcomm appears to be the implications of not just committing discovery abuses, but the failure of having a well thought out e-discovery plan that is actively executed/monitored by outside counsel.  The resulting tension between outside counsel, inside counsel and the internal IT department may continue to escalate if more cases like this make the headlines in 2009.

1)  E-Discovery Case of the Year: Victor Stanley, Inc. v. Creative Pipe, Inc., 2008 WL 2221841 (D. Md. May 29, 2008)

Judge Grimm’s hallmark opinion has had the legal community buzzing over the past several months and the reason appears pretty straight forward.  In Victor Stanley Grimm builds on the holdings in Seroquel, O’Keefe and Equity Analytics, to boldly cast doubt on a practice so routine that it’s literally shocked the legal community into reevaluation:

(“[D]etermining whether a particular search methodology, such as keywords, will or will not be effective certainly requires knowledge beyond the ken of a lay person (and a lay lawyer) . . . .”

The notion that electronic discovery search is beyond the ability of most attorneys has caused tremors within the litigation support community who had a long history of blindly receiving keywords from counsel, running them and turning back over the results – often blissfully unaware of the extent to which those keyword searches actually located relevant information.  Victor Stanley‘s analysis of the “reasonableness” of search protocols also has impact on the FRE 502 and therefore cements its place alongside other e-discovery “must reads” such as Zubulake and Morgan Stanley.

The cases above are my Top 5.  What additional cases do you think were important?  Please let me know by commenting on the cases you think shaped electronic discovery in 2008 and why.

Learn More On: Frcp Electronic discovery.

In my last post, I started a discussion on the myths surrounding concept search.  The first myth I dispelled was the “concept search is concept search” myth.  The myth is that there is an agreed upon definition of concept search.  In actuality, when people in electronic discovery use the term concept search, they don’t always mean the same thing.  Frequently they are not actually talking about concept search technology at all and are actually talking about concept or content categorization technology, which is very different.  The second myth that needs dispelling is that concept search is better than keyword search.

The thinking behind this myth goes something like this:

Keyword search has a lot of problems.  It is prone to being over-inclusive, i.e., finding some non-relevant documents, and under-inclusive, i.e., not finding some relevant documents.  Concept search technologies are new and interesting and using these technologies you can find documents that keyword search can’t find.  Therefore, concept search must be better than keyword search.

Let’s examine this thinking.  The first two statements are accurate.  Keyword search is not perfect and can produce over- and under-inclusive results.  And concept search and content categorization technologies can both help identify documents that keyword search technologies might not find.  However, the conclusion that concept search is better than keyword search is not valid and doesn’t follow from these two statements.  Why?

In order to answer this question, we first need to go back to the difference between concept search and content categorization. Because these are different technologies, we really need to separately compare concept search versus keyword search and content categorization versus keyword search.  Let’s start with content categorization and keyword search.

The issue with this comparison is that keyword search and content categorization do different things.  Keyword search can be used in many ways in e-discovery.  The two most common are: (1) analysis or case assessment: finding the hot documents and understanding the matter by determining who knew what, when, how and why, etc., and (2) culling: removing non-responsive documents and/or identifying potentially privileged documents in order to reduce a large, starting set of documents to a smaller set before review.

Content categorization, on the other hand, has historically been used within the review phase of e-discovery.  Categorization can help reviewers to better understand the documents they are reviewing and thus potentially increase the speed of review.  Practitioners with whom I have worked also find that categorization can be useful during analysis by helping to understand a matter and identify potentially important keywords.

However, content categorization has not been used as part of culling.  First, culling needs to be transparent.  You need to be able to get agreement with or at least explain to the opposing side and the court exactly how you have culled the data set.  If you cull based on categories of documents that have been generated by a proprietary, black-box algorithm, it’s going to be difficult to gain agreement on or explain your culling methodology.  This is why the typical method of culling is still to use keyword search and either agree on the set of search terms with the opposing side or to use e-discovery search best practices to perform keyword searches on your own.

Second, content categorization has its own issues when it comes to being over- and under-inclusive.  There is no guarantee that your group of documents that have been categorized as being related to, for example, a company’s hiring policies include all of the documents in your matter related to hiring policies or that they do not include some documents that may not really be related to hiring policies.  Content categorization, like keyword search and virtually every information retrieval technology, is not perfect.

So what about concept search technology?  Surely, concept search technology is better than old, boring keyword search.  Well, actually it’s not that clear-cut.  The problem with concept search technology is that while it might find more relevant documents than plain keyword search, it will also likely find more false positives.  Imagine searching for documents containing “terminate” in an employment matter and your concept search technology automatically searching for “fire”, “dismiss”, etc. as well.  You’ll find more documents related to the termination of employees, but you’ll also find a lot more non-relevant documents concerning house fires, the fire department, etc.

So concept search can help address the under-inclusive problem with keyword search, (though it won’t solve it) and can be helpful during analysis.  But it can often increase the over-inclusive problem.  In addition, today’s concept search technologies share the transparency problem with concept categorization.  These technologies have largely been designed as “black boxes”, which as I have discussed in the past, makes sense for Enterprise search but not for e-discovery search, and, as a result, could also be potentially difficult to explain and defend.   For these reasons, concept search technology isn’t used very much in e-discovery today.  In order for its use to become widespread, it will need to become more transparent.  But that’s a topic for another day.

The bottom line here is that despite all the hype, concept search and content categorization technologies do not solve all the challenges of e-discovery search.  Both of these technologies can be very useful and the technology behind them is always improving.  However, as most of the experienced practitioners I work with already know, these technologies are generally better thought of as supplements to keyword search, not replacements.  The important question is not whether to use one technology over the other but which technology is best suited to your objectives and how best to use all the available technologies to achieve the desired goal.

Judge Paul Grimm’s recent opinion in Victor Stanley, Inc. v. Creative Pipe, Inc., 2008 WL 2221841 (D. Md. May 29, 2008) provides valuable guidance on one of the most important issues in e-discovery: how to conduct keyword searches in a defensible manner given that keyword searches are prone to produce over- and under-inclusive results.  The ruling suggests one of two approaches: either producing parties should adopt a “collaborative” approach to conducting keyword searches, whereby each party agrees on a search methodology; or, they should use a “best practices” approach, such as the one suggested by Sedona, where the producing party tests, samples, and iteratively refines searches so that they can demonstrate they have taken reasonable measures to reduce over- and under-inclusive results.

While the guidance is clear, following the guidance in practice is very difficult.  The primary reason for this is that the search technology being used in e-discovery today is not up to the task.  Specifically, today’s search technology suffers from three problems:

1. The over- and under-inclusive tradeoff. Many technologies have been developed to address the tendency of keyword searches to miss relevant documents and produce under-inclusive results.  Wildcard and stemming technology has been developed in order to address the issue of finding common word variations in specified keywords.  Concept search has been designed to find documents containing words with similar meanings to the keywords in a search.  And fuzzy search technologies have been put in place to find misspellings of words. However, all of these suffer from the same problem: they produce too many non-relevant or “false positive” documents thus driving up the cost of review. For example, if someone runs the wildcard search “divers*”, then he or she not only gets the desired documents containing “diverse” and “diversity”, but also gets a large number of false positive documents containing “diversion”, “diversification”, and so on.  In the case of concept and fuzzy search, the problem is so great that these technologies to date have rarely been used in e-discovery.
2. Too expensive to test, sample and refine searches. Today’s search technologies are largely designed to run one search at a time, not the dozens of searches that are typical in e-discovery. As a result, anyone trying to follow the best practices of testing, sampling, and refining each search will find themselves missing deadlines and running over budget because it takes so long. This also makes collaboration with the opposing party close to impossible, since there’s little time to iterate on – and agree upon – a set of keyword searches.
3. Manual documentation. It’s not enough for producing parties to use best practices, they have to document them so that they can “show their work” to the court. Currently, documenting the search refinement process is mostly manual, with the result that it is either done inadequately or not at all.

The reason why the search technology used for e-discovery has these problems is surprisingly simple: it’s because the technology was not designed for e-discovery in the first place. Rather, it was built for enterprise search, and was only later repurposed towards e-discovery.

The “Black Box” Of Enterprise Search

The core issue is that enterprise search technology has been designed to be a “black box”. Users enter a single search query into one end, and get results at the other, with no visibility into what happens in between. Going back to our previous example, when a user searches for “divers*” intending to find documents related to “diversity” or “diverse”, enterprise search engines give the user no visibility into the crucial step of query expansion and how it expands the search query into relevant and non-relevant terms like “diversion” and “diversification”. As a result, the user has no ability to minimize the false positives.

In the same vein, when a user enters multiple queries into a “black box” enterprise search engine, all of the queries run as a single search, and the user has no visibility into which results are associated with which query. For example, a user that searches for “hiring OR interview” will get the results for the combination of the queries “hiring” and “interview”. He or she won’t know that only 5 of documents contained “hiring” while 100 documents contained “interview.”  This limitation makes analyzing, sampling and refining searches costly and time consuming.

That’s not say that enterprise search products like Autonomy or Endeca are flawed. Far from it.  Their “black box” design works exceedingly well for the simple and quick queries that people want to run across the enterprise for general business purposes. If a sales manager is looking for a single proposal for her meeting the following day, then she doesn’t care how the search was performed or if it’s over-inclusive.  She’s only interested in the first page of relevant results, and for that use case enterprise search engines do a great job.

But e-discovery is a whole different world.  In e-discovery, users typically must review every single document in the search results, not just the most relevant ones.  As a result, over-inclusive searches can dramatically increase the costs of downstream production and review.  And under-inclusive searches raise the issue of defensibility.  Finally, e-discovery users have to run a lot of search queries and understand which documents are associated with each of those queries.

So, going back to the original problem, if current search technologies cannot help lawyers and litigation support professionals follow Judge Grimm’s guidance and address the “well-known limitations” of keyword search, what can? That will be the subject of my next post.

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