e-discovery 2.0

Over the past 4 years, I have had hundreds of conversations with corporate counsel and “legal IT”, meaning technical folks charged with supporting the legal team. More and more of them are looking to lower their costs by bringing e-discovery in-house. But as they work through that process, there’s one question that consistently comes up, even today – namely, “When [insert name of software company] says they “do” e-discovery, what exactly does that mean?”

There has been progress towards answering this question, thanks mainly to the analyst community. George Socha and Tom Gelbmann’s EDRM framework has been immensely helpful in breaking down electronic discovery into its component steps. Other analysts, like Debra Logan at Gartner, were quick to embrace the framework, prompting every software provider to follow suit. As a result, there is today a common language that everyone uses to describe the e-discovery process.

The Electronic Discovery Reference Model (EDRM) breaks down the e-discovery process into a series of steps. Companies looking to buy e-discovery software to lower costs typically map different software products to each of these steps, to make sure that they cover the entire process.

But having a universally-agreed framework is only half the answer. To eliminate customer confusion, there also needs to be agreement on how different software products fit into the framework. This is especially important since there is no single, end-to-end solution for e-discovery which covers all aspects of EDRM. So customers are forced to think about how different software solutions fit together. And that is where things begin to fall apart.

Many software vendors feel it is advantageous to claim that they do everything, even though they do not. Customers are rightly suspicious of those claims, and so press vendors to provide more detailed information – hence the question, “when you say you do e-discovery, what exactly does that mean?”

In light of that, how can litigation support teams, corporate counsel, or legal IT people figure out which e-discovery solution best meets their needs? From observing this decision-making process hundreds of times, I have found 3 simple steps are incredibly helpful.

Step 1: Read the analyst reports

Two reports in particular make for required reading. One is Gartner’s MarketScope Report, which is available for free at certain sites; the other is the 451Group’s recent e-discovery report, which is summarized in a publicly available presentation. The helpful thing about the 451 Group’s report is that it tells you which software companies do which parts of the EDRM process. You do have to buy the report to get the full picture (it’s well worth it!), but the publicly available presentation will give you a flavor for their analyis, and I have drawn from that presentation in the figure below:

Analyst firms like the 451 Group map software vendors to the EDRM framework according to what they actually do, which is often different from what software vendors claim they do.

The 451 Group’s analysis highlights several important points. First, it shows that there is no single end-to-end solution. Even the products of giants like EMC (SourceOne), HP (IAP), and IBM (CommonStore) only solve one piece of the puzzle, information management. Second, it shows that customers have choices at each stage of the EDRM process. For example, to solve the problem of identification, collection, and preservation of electronic information, customers can choose from solutions as diverse as Guidance EnCase (forensic collection), Index Engines (back-up tapes) and Mimosa NearPoint (email archive). Third, it provides an independent assessment of what vendors do, as opposed to what they may claim. For example, Kazeon claims analysis and review capabilities, whereas the report shows its product does identification, collection, and preservation; Recommind claims its Axcelerate eDiscovery and MindServer products do processing, whereas the report finds that they do not.

Step 2: Evaluate the products prior to purchase

Just as anyone would test-drive a car prior to purchase, it’s critical to test-drive e-discovery software. Any vendor should be willing to provide their software free of charge for an evaluation on-premise. The most effective evaluations are when the customer uses the product themselves, either on a live case or test data. This is far preferable to just sending the data to the vendor who then loads it into their system, as in that scenario there are too many opportunities for the vendor to hide their product’s shortcomings.

Step 3: Check references carefully

The trick with references is to insist on relevant references. It’s not good enough for the vendor to dredge up some random person who says nice things; or even a credible knowledgeable person who is using the product in a completely different way. For example, if a company is happy with Autonomy’s IDOL for enterprise search, that does not tell you much about what Autonomy might be like for e-discovery. What really counts are references from other customers who are using the product for the same application that you are.

All this can sound like a lot of work, but I have seen people go through the process in as little as a month, and be much happier for it. A little work up front can save a lot of time (and heart-ache!) later on.

In my last post, I discussed how the “black box” design of enterprise search engines makes it challenging to defensibly use keyword search in e-discovery and follow Judge Grimm’s guidance in Victor Stanley, Inc. v. Creative Pipe, Inc., 2008 WL 2221841 (D. Md. May 29, 2008).  In Victor Stanley, Judge Grimm notes that because keyword search technology is prone to producing over- and under-inclusive results, attorneys using keyword search should adopt one of two approaches: either collaborate with the opposing party to agree on keyword search methodology, or utilize best practices that demonstrate they have taken reasonable measures to reduce over- and under-inclusiveness.  However, the black box search technologies that are used in e-discovery today make following this guidance difficult.  They can’t reduce under-inclusiveness without increasing over-inclusiveness.  And they make it expensive to utilize collaborative or best practices methodologies including testing, sampling, refining and documenting searches.  All of which begs an obvious question: what can be done to improve search for e-discovery?

In my opinion, the answer is simple: e-discovery search needs to become more transparent.  Instead of being forced to feed one search query at a time into a “black box” search engine and then getting results  with no idea how those results were generated, lawyers and litigation support professionals need technology that provides them with greater visibility into the search process. They need to understand how the results were obtained, so they can reduce both the over- and under-inclusiveness of keyword search, and easily follow Judge Grimm’s advice to improve the defensibility of their search methodology.

A transparent search solution should have four key elements:

1. Transparent query expansion.  Query expansion is the process by which search engines take the query that the user submitted and expand or convert it into a new and improved form.  Wildcard, stemming, concept and fuzzy searches all follow this query expansion process.  For example, the search “divers*,” would be expanded to search for all the words that start with “divers” in the data set, such as “diverse,” “diversity,” “diversion,” “diversification,” etc.  In transparent search, query expansion would be exposed to users, allowing them to include or exclude expanded keywords. To continue with the previous example, a user that is searching for documents related to diversity would then have the ability to exclude false positive expanded terms, such as “divers”, “diversion,” and “diversification” from the search.  Making query expansion transparent can significantly reduce the over-inclusiveness of keyword search.  It also makes it practical to use technologies, such as concept and fuzzy search, that have not been used to date because of their complexity and tendency to produce massively over-inclusive results.
2. Multiple query support. When a search contains multiple keyword queries, such as “hiring” and “interview,” transparent search should provide visibility into the results for each individual query as well as the combination of all the queries. For example, with the search “hiring OR interview,” users should have separate visibility into the results for “hiring” and “interview” as well as “hiring OR interview.”  They should know that out of the 100 documents that match “hiring OR interview”, only 5 match interview and 95 match hiring.  This kind of visibility is critical if you want to either collaborate or follow search testing, sampling, and refinement best practices when there are a large number of queries.
3. Rapid sampling. Transparent search should support the ability to rapidly sample the results from all of the individual queries, such as “hiring” and “interview”, contained within a search. It should also be easy to take a random sample of non-matching documents in order to assess whether one or more searches have identified as many of the relevant documents as possible.  As Judge Grimm states in Victor Stanley when assessing keyword searches used to find privileged documents, “The only prudent way to test the reliability of the keyword search is to perform some appropriate sampling of the documents determined to be privileged and those determined not to be in order to arrive at a comfort level that the categories are neither over-inclusive nor under-inclusive.”
4. Automated documentation. Transparent search technology needs to document all aspects of the search process including (but not limited to) any keyword that has been excluded during transparent query expansion, the combined results of a search containing multiple individual queries, and the results for each of the individual queries within that search.  Automatically documenting the search methodology used and the results obtained is critical so that users can “show their work” if their search methodology is ever called into question.

Benefits of Transparent Search

By addressing the main technology challenges of keyword search, transparent search provides significant benefits to attorneys and litigation support professionals using search for e-discovery. First, parties that adopt transparent search can improve the defensibility of their e-discovery search practices. By enabling iterative testing, sampling and refinement, transparent search allows users to adopt the approaches recommended by Judge Grimm when it was previously impractical to do so.  At the end of the day, this means less risk.

Second, the use of transparent search can substantially reduce downstream production and review costs by removing false positives. For example, it is not uncommon for certain wildcard searches to generate results where 20-40% of the included documents are false positives that can be removed by transparent query expansion.  This can result in thousands of dollars of savings on a single search query.

Finally, transparent search can dramatically reduce the time and cost required to complete the search and culling stage of e-discovery. Currently, it can take hundreds of hours to run a significant number of searches one at a time, document the results of each search, and sample and refine each individual query. With transparent search, running multiple queries and documenting each of the individual results takes minutes. Sampling each of the individual queries takes seconds.

When it comes to e-discovery search, it’s important to recognize that there are no “silver bullets.”  Search will remain an imperfect science with the possibility of over- and under-inclusive results.  But equally, there is no doubt that search remains the best solution for reducing the vast quantities of electronic information that are a part of every e-discovery process down to a reasonable level for human review. While attorneys and litigation support professionals can’t completely remove the imperfections of keyword search, they can, with transparent search, take action to minimize the impact of these imperfections and defensibly meet the requirements of new case law.  In doing so, they will be able to turn their attention to where it should be: the substance of the case.

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.