From a business perspective, information should be handled like property. Like assets or supplies, information needs management.
Companies set policies to govern use, storage, and disposal of assets and office supplies. Companies also need to make and enforce rules governing electronically stored information, including how it is organized (who has access and how), stored (where and at what cost), retained (including backups and archives), and destroyed (deletion and non-deletion both have significant legal and cost consequences). These policies must balance the business, legal and technical needs of the company. Without them, a company opens itself up to losses from missed opportunities, employee theft, lawsuits, and numerous other risks.
Some information is analogous to company ASSETS. For example, let’s suppose a certain sales proposal took someone a week to write and required approval and edits from four other people plus 6 hours of graphics production time. An accountant isn’t going to list that proposal on the company books. But it is an asset. It can be edited and resubmitted to other potential customers in a fraction of the time it took to create the original. Like the machinery, furniture, or hand tools used to operate a business, company money was spent obtaining this information and it will retain some value for some time. It should be managed like an asset.
Some information is analogous to OFFICE SUPPLIES. For example, a manager spends a number of hours customizing a laptop with email account settings, browser bookmarks and passwords, ribbon and plugin preferences, nested document folders, security settings, etc. That customization information is crucial for the manager’s productivity in much the same way as having pens in the drawer, paper in the printer, staples in the stapler, and water in the water-cooler can be important for productivity. Productivity will be lost if it is lost. That information needs to be managed just as much as office supplies need to be manged.
From a business perspective, when company information is lost or damaged, or when users are under or over supplied, it is no different from mismanagement of company assets and office supplies.
Setting an information policy means:
identifying information use and control needs;
making choices and tradeoffs about how to meet those needs; and
taking responsibility for results and an ongoing process (setting goals / taking action / measuring progress / adjusting).
Information governance policy is an on-going process for managing valuable company information. All of the stakeholders – in particular, business units, IT, and Legal – must collaborate in order to draw a bullseye on company information management needs. The right people in the organization must be charged with responsibility for getting results or for making changes needed to get results.
Without a doubt, it takes time and money, and requires collaboration, to develop a “policy.” But we’re all accustomed to this type of preparation already. Let’s look at simple, familiar professional standards for just a moment:
Software developers test software on actual users and correct bugs and (hopefully) mistaken assumptions before releasing it. Avoiding these steps will undoubtedly lead to loss and possibly bankruptcy.
Attorneys meet with clients before going to trial to prepare both the client and the attorneys. If they don’t they risk losing their clients millions, or getting them locked up.
Advance preparation is as essential in information management as it is in software development and trial practice. Simply ignoring the issue, or dumping it on one person or a single department (like IT or Legal) can be very costly. Avoiding the planning component of information management is like putting in only 80% of the time and effort needed for the company to succeed. Avoiding 20% of the time and effort doesn’t yield a “savings” when the outcome is failure, as when an employee steals documents, essential information is lost when a building with no computer back-ups burns down, or old documents which would have been deleted under a proper information governance policy turn up in a lawsuit and cost millions.
Information policy does NOT flow from any of the following all-to-common realities:
The first meeting between the Legal Department and the new eDiscovery vendor is also the first meeting between Legal and IT (true story);
Ever since a certain person from the General Counsel’s office was made the head of corporate records management, no one in IT will talk to that person (true story);
Information management technology alone, without a company-specific understanding of the problems that the technology is meant to solve, is not a recipe for success. A recent article by Carol Sliwa, published by SearchStorage.com (April 22, 2009), offers a detailed look at issues surrounding efforts to reduce storage costs by assessing how information is being used and moving it to the least expensive storage tier possible.
The article has some powerful suggestions on developing information policy. First, Karthik Kannan, vice president of marketing and business development at Kazeon Systems Inc.:
“What we discovered over time is that customers need to be able to take some action on the data, not just find it…. Nobody wants to do data classification just for the sake of it. It has to be coupled with a strong business reason.”
“In order to really realize and get the benefit of data and storage classification, you have to start with a business process…. And it has to start from conversations with the business units and understanding the needs and requirements of the business. Only at the end, once you actually have everything in place, should you be looking at technology because then you’ll have a better set of requirements for that technology.”
It takes time, money and cooperation between departments that may have never worked together before to develop a working information governance policy. But that is not a reason to skip — or skimp on — the process. Companies need to protect their assets and productivity, and information governance has become an essential area for doing just that.
Only a few years ago certain courts had ruled that data stored on tape could be considered “inaccessible” because it was so expensive to review it, and thus data stored on tape did not always need to be reviewed when answering an eDiscovery request (for example, the Zublake decisions). More recently, however, the legal profession is becoming aware of advances which make tapes faster and cheaper to review, like technology for rapid disaster recovery.
There are still a number of fine distinctions being made in this area of law, and the specific tape handling practices of different companies can render their tapes more or less “accessible.” (Ironically, companies that archive backup tapes indefinitely, which sounds like a safe practice, may be exposing themselves to a greater burden in eDiscovery, not to mention the extra cost of storing outdated tapes.) But broadly speaking, few companies storing information on tape can categorically rely on “inaccessibility” to rule out the risk of being required to review their tapes during eDiscovery any more. For more about the law concerning inaccessibility, including California’s burden-shifting rules, I recommend this article by Winston & Strawn attorneys David M. Hickey and Veronica Harris.
Fortunately, two prongs of innovation are shrinking the issues surrounding eDiscovery and tape. The first prong, which happens to be the subject of this blog post, comes in the form of new tape indexing and document retrieval technology. The second solution, which involves substituting hard drives in place of tape, will be the subject of a future post.
To learn more about the current state of eDiscovery technology in the realm of tape, I recently spoke with Jim McGann, Vice President of Marketing at Index Engines. Index Engines’ solution comes in the form of an appliance (a hardware box pre-loaded with their software) that scans a broad variety of tapes and catalogs the content. The appliance indexes tape data and de-duplicates documents within the index using the hash values of the documents. At this point users can cull (selectively retrieve) potentially responsive documents from a batch of ingested tapes without first performing an expensive, resource-intensive full restoration of each tape. And because Index Engines can ingest all of the common tape storage formats, users don’t need to run or even possess the original software used to write to the tapes.
From a longer-term strategic perspective, Index Engines’ users can approach their tape stores incrementally, taking a first pass through their tapes in response to a particular discovery request, then add to their global tape index as new discovery requests are fielded. They can embark upon a proactive tape indexing campaign that will give them enhanced early case assessment capabilities. Users may also opt to extract important data that is not immediately needed but resides on old or degraded tape.
For companies with thousands or tens of thousands of tapes, indexing can allow significant numbers of tapes to be discarded since many individual tapes typically contain data which is almost entirely repeated on other tapes or has lasted past the end of its retention period – not to mention the corrupted or blank tapes which are being carefully stored nonetheless.
All of this makes Index Engines an extremely affordable (at least by Enterprise standards) alternative to restoring and reviewing tapes individually.
I asked Jim McGann whether Index Engines resembled dentists who teach patients good dental hygiene and, if successful, will wind up putting themselves out of a job. If Index Engines’ appliances succeed in indexing, de-duplicating, and extracting all of the stored tape in existence, while ever more affordable hard drive storage replaces tape storage, won’t the company be out of a job?
Jim pointed out that, for certain organizations which currently rely on tape storage, substituting hard drives for tape drives is simply not a viable option. Costs associated with re-routing system data and human work flows, as well as the risk of downtime during a transition, mean that many organizations won’t switch even after disk drives become less expensive. And Index Engines takes away much of the cost incentive for switching that would otherwise be driven by eDiscovery and compliance requirements. Finally, Jim says, Index Engines can be used to index almost all of the information customers have, not just tape data, which enables users to find non-tape information that must be reviewed for eDiscovery.
The other approach to the problem of tape storage (to be explored in more detail in a future blog post) involves near-line hard drive solutions. Leading hard drive storage vendors such as Isilon Systems claim that their “near line” solution is priced nearly as low as tape while offering higher performance and reliability. But advocates of tape, including the Boston-area based Clipper Group (in a whitepaper offered on tape drive vendor SpectraLogic’s web site) claim that the total costs of ownership of disk storage, taking into account factors such as floor space requirements and electricity, is still many, many times higher than tape.
So, as tapes look like they will be around for some time to come, companies with tapes will continue to need technologies like Index Engines’. And most will not be able to avoid discovery of tapes for much longer, if they are even still able to do so, thanks in part to the availability of these technologies.
I recently had the pleasure of speaking with Nicholas Croce, President of Inference Data, a provider of innovative analytics and review software for eDiscovery, following the company’s recent webinar, De-Mystifying Analytics. During our conversation I discovered that Nick is double-qualified as a legal technology visionary. He not only founded Inference, but has been involved with legal technologies for more than 12 years. Particularly focused on the intersection of technology and the law, Nick was directly involved in setting the standards for technology in the courtroom through working personally with the Federal Judicial Center and the Administrative Office of the US Courts.
I asked to speak with Nick because I wanted to pin him down on what I imagined I heard him say (between the words he actually spoke) during the live webinar he presented in mid-March. The hour-long interview and conversation ranged in topic, but was very specific in terms of where Nick sees the eDiscovery market going.
Sure enough, during our conversation Nick confirmed and further explained that he and his team, which includes CEO Lou Andreozzi, the former LexisNexis NA (North American Legal Markets) Chief Executive Officer, have designed Inference with not one, but two models of advanced eDiscovery analytics and legal review in mind.
In a nutshell, Inference is designed not only to deliver the current model of eDiscovery software analytics, which I have dubbed “Software Queued Review,” but the next generation analytics model as well, which I am currently calling “Statistically Validated Automated Review” (Nick calls it “auto-coding”).
I have a few specific questions to ask, but in general what I’d like to cover is:
1) where does Inference fit within the eDiscovery ecosystem,
2) how you think statistically validated discovery will ultimately be used, and
3) how you think the left side of the EDRM diagram (which is where document identification, collection, and preservation are situated) is going to evolve?
Nick: To first give some perspective on the genesis of Inference, it’s important to understand the environment in which it was developed. Prior to founding Inference I was President of DOAR Litigation Consulting. When I started at DOAR in 1997, the company was really more of a hardware company than anything else. I was privileged to be involved in the conversion of courtroom technology from wooden benches to the efficient digital displays of evidence we see today, Within a few years we became the predominant provider of courtroom technology, and it was amazing to see the legal system change and directly benefit from the introduction of technology. As people saw the dramatic benefits, and started saying “how do we use it?” we created a consulting arm around eDiscovery which provided the insight to see that this same type of evolution was needed within the discovery process.
This began around 2004-2005 when we started to see an avalanche of ESI (“Electronically Stored Information”) coming, and George Socha became a much needed voice in the field of eDiscovery. As a businessman I was reading about what was happening, and asking questions, and it seemed black and white to me – it had become impossible to review everything because of the tremendous volume of ESI with existing technology. As a result I started developing new technology for it, to not only manage the discovery of large data collections, but to improve and bring a new level of sophistication to the entire legal discovery process.
Inference was developed to help clients intelligently mine and review data, organize case workflow and strategy, and streamline and accelerate review. It’s the total process. But, today I still have to fight “the short term fix mentality” – lawyers who just care about “how do I get through this stuff faster”, which is the approach of some other providers, and which also relates to the transition I see in the EDRM model – I want to see the whole thing change.
Review is the highest dollar amount, the biggest pain, 70% of a corporation’s legal costs are within eDiscovery. People want to, and need to, speed up review. However, we also need to add intelligence back into the process.
Bruce: What differentiates Inference, where does it fit in?
Nick: I, and Inference, went further than just accelerating linear review and said: it has to be dynamic, not just coding documents as responsive / non-responsive. I know this is going to sound cheesy I guess, but – you have to put “discovery” back into Discovery. You need to be able to quickly find documents during a deposition when a deponent says something like “I never saw a document from Larry about our financial statements”, and not just search for “responsive: yes/no”, “privileged: yes/no.”
Inference was, and is, designed to be dynamic – providing suggestions to reviewers, opportunities to see relationships between documents and document sets not previously perceived, helping to guide attorneys – intuitively. Inference follows standard, accepted methodologies, including Boolean keyword search, field and parametric search, and incorporates all of the tools required for review – redaction, subjective coding, production, etc.
In addition to that overriding principle, we wanted the ability to get data in from anyone, anywhere and at any time. Regulators are requiring incredibly aggressive production timelines; serial litigants re-use the same data set over and over; CIOs are trying to get control over searching data more effectively, including video and audio. Inference is designed to take ownership of data once it leaves the corporation, whether it is structured, semi-structured or unstructured data.
Inside the firewall, the steps on the left side of the EDRM model are being combined. Autonomy, EMC, Clearwell, StoredIQ — the crawling technologies – these companies are within inches of extracting metadata during the crawling process, and may be there already. This is where Inference comes in since we can ingest this data directly. I call it the disintermediation of processing because at that point there is no more additional costs for processing.
In the past someone would use EnCase for preservation, then Applied Discovery for processing (using date ranges and Boolean search terms), and at some cost per custodian, and per drive, you’d then pay for processing. It used to be over $2,500 per gig, now it’s more like $600 to $1500 per gig, depending on multi-language use and such.
But once corporations automate the process with crawling and indexing solutions, all of the information goes right into Inference without the intermediary steps, which puts intelligence back in the process. You can ask the system to guide you whenever there’s a particular case, or an issue. If I know the issue is a conversation between Jeff and Michele during a certain date range, I can prime the system with that information, start finding stuff, and start looking at settlement of the dispute. But without automation it can take months to do, at much higher costs.
Inference also offers quality control aspects not previously available: after, say, one month, you can use the software to check review quality, find rogue reviewers, and fix the process. You can also ultimately do auto-coding.
Bruce: I think this is a good opening for segue to the next question: how will analytics ultimately be used in eDiscovery?
Nick: The two most basic components of review are “responsive” and “privileged.” I learned from the public testimony of Verizon’s director of eDiscovery, Patrick Oot, some very strong statistics from a major action they were involved in. The first level document review expense was astounding even before the issues were identified. The total cost of responsive and privileged review was something like $13.6 million.
The truth is that companies only do so many things. Pharma companies aren’t generally talking about real-estate transactions or baseball contracts.
Which brings us to auto-coding… sometimes I try to avoid calling it “auto-coding” versus “computer aided” or “computer recommended” coding. When someone says “the computer did it” attorneys tend to shut down, but if someone says “the computer recommended it” then they pay attention.
Basically auto-coding is applying issue tags to the population based on a sampling of documents. The way we do it is very accurate because it is iterative. It uses statistically sound sampling, recurrent models. It uses the same technology as concept clustering, but you cluster a much smaller percentage. Let’s say you create 10 clusters, tag those, then have the computer tag other documents consistently with the same concepts. Essentially, the computer makes recommendations which are then confirmed by attorney, and then repeated until the necessary accuracy level has been achieved. This enables you to only look at a small percentage of the total document population.
Bruce: I spoke with one of the statistical sampling gurus at Navigant Consulting last month, who suggests that software validated by statistical sampling can be more accurate than human reviewers, with fewer errors, for analyzing large quantities of documents.
Nick: It makes sense. Document review is very labor intensive and redundant. Think about the type of documents you’re tagging for issues – it doesn’t even need to be conscious: it is an extremely rote activity on many levels which just lends itself to human error.
Bruce: So let’s talk about what needs to happen before auto-coding becomes accepted, and becomes the rule rather than the exception. In your webinar presentation you danced around this a bit, saying, in effect, that we’re waiting for the right alignment of law firms, cases, and a judge’s decision. In my experience as an attorney, including some background in civil rights cases, the way to go about this is by deliberately seeking out best-case-scenario disputes that will become “test cases.” A party who has done its homework stands up and insists on using statistically validated auto-coding in an influential court, here we probably want the DC Circuit, the Second Circuit, or the Ninth Circuit, I suppose. When those disputes result in a ruling on statistical validity, the law will change and everything else will follow. Do you know of any companies in a position to do this, to set up test cases, and have you discussed it with anyone?
Nick: Test cases: who is going to commit to this — the general counsel? Who do they have to convince? Their outside counsel, who, ultimately, has to be comfortable with the potential outcome. But lawyers are trained to mitigate risk, and for now they see auto-coding or statistical sampling as a risk. I am working with a couple of counsel with scientific and/or mathematic backgrounds who “get” Bayesian methods- and the benefits of using them. Once the precedents are set, and determine the use of statistical analysis as reasonable, it will be a risk not to use these technologies. As with legal research, online research tools were initially considered a risk. Now, it can be considered malpractice not to use them.
It can be frustrating for technologists to wait, but that’s how it is. Sometimes when we are following up with new installs of Inference we find that 6 weeks later they’ve gone back to using simple search instead of the advanced analytics tools. But even for those people, after using the advanced features for a few months they finally discover they can no longer live without it.
Bruce: Would you care to offer a prediction as to when these precedents will be set?
Nick: I really believe it will happen, there’s no ambiguity. I just don’t know if it’s 6 months or a year. But general counsel are taking a more active role, because of the cost of litigation, because of the economy, and looking at expenses more closely. At some point there will be that GC and an outside counsel combination that will make it happen.
Bruce: After hearing my statistician friend from Navigant deliver a presentation on statistical sampling at LegalTech last month I found myself wondering why parties requesting documents wouldn’t want to insist that statistically validated coding be used by parties producing documents for the simple reason that this improves accuracy. What do you think?
Nick: Requesting parties are never going to say “I trust you.”
Bruce: But like they do now, the parties will still have to be able to discuss and will be expected to reach agreement about the search methods being used, right?
Nick: You can agree to the rules, but the producing party can choose a strategy that will be used to manage their own workflow – for example today they can do it linearly, offshore, or using analytics. The requesting party will leave the burden on the producing party.
Bruce: If they are only concerned with jointly defining responsiveness, in order to get a better-culled set of documents — that helps both sides?
Nick: That would be down the road… at that point my vision gets very cloudy, maybe opposing counsel gets access to concept searches – and they can negotiate over the concepts to be produced.
Bruce: There are many approaches to eDiscovery analytics. Will there have to be separate precedents set for each mathematical method used by analytics vendors, or even for each vendor-provided analytical solution?
Nick: I’d love to have Inference be the first case. But I don’t know how important the specific algorithm or methodology is going to be – that is a judicial issue. Right now we’re waiting for the perfect judge and the perfect case – so I’ll hope it’s Inference, rather then “generic” as to which analytics are used. I hope there’s a vendor shakeout – for example, ontology based analytics systems demo nicely, but “raptor” renders “birds” which is non-responsive, while “Raptor” is a critical responsive term in the Enron case.
Bruce: Perhaps vendors and other major stakeholders in the use of analytics in eDiscovery, for example, the National Archives, should be tracking ongoing discovery disputes and be prepared to file amicus briefs when possible to help support the development of good precedents.
This blog post is the first of two on the topic of advanced eDiscovery analytics models. My goal is to make the point that lawyers don’t trust or use analytics to the degree that they should, according to scientifically sound conventions commonly employed by other professions, and to speculate about how this is going to change.
In this first post I’ll explain why we arrived where we are today by describing the progression of analytics across three generations of Discovery technology.
The first generation, which I call “The Photocopier Era,” relies on labor intensive, pre-analytics processes. Some lawyers are still stuck in this era, which is extremely labor intensive.
The second generation is the current reigning model of analytics and review. I call it “software queued review.” Software queued review intelligently sorts and displays documents to enable attorneys to perform document review more efficiently. At the same time, software queued review allows – or should I say, requires? – attorneys to do more manual labor than is required to ensure review quality or to ensure that attorneys take personal responsibility for the discovery process.
The third, upcoming generation of analytics is only beginning to provoke widespread discussion in the legal community. I’ll call it “statistically validated automated review.” In it software is used to perform the majority of document review work, leaving attorneys to do the minimum amount of review work. In fact, certain advanced analytics and workflow software solutions can already be calibrated, by attorney reviewers, to be more accurate than human reviewers typically are capable of when reviewing vast quantities of documents.
Because it will radically reduce the amount of hands-on review, the third generation model is currently perceived by many lawyers as a risky break from legal tradition. But when this model is deployed outside of the legal profession it is not considered a giant step, technologically or conceptually. It is merely an application of scientifically grounded business processes.
In subsequent blog posts, including the second post in this series, I will look at what is being done to overcome the legal profession’s reluctance to adopt this more accurate, less expensive eDiscovery model.
The Pre-analytics Generation: Back to the Photocopier Era
Please return with me now to olden times of not-so-long-ago, the days before eDiscovery software. (Although even today, for smaller cases and cases that somehow don’t involve electronically stored information, the Photocopier Era is alive and well.)
In the beginning there were paper documents, usually stored within folders, file boxes, and file cabinets. Besides paper, staples, clips, folders, and boxes, photocopiers were the key document handling technology, with ever improving speed, sheet feeding, and collation options.
Gathering documents: When a lawsuit reached the discovery stage, clients following the instructions of their attorneys physically gathered their papers together. Photocopies were made. Some degree of effort was (usually) made to preserve “metadata” which in this era meant identifying where the pieces of paper had been stored, and how they had been labeled while stored.
Assessing documents: In this era every “document” was a physical sheet of paper, or multiple sheets clipped together in some manner. Each page was individually read by legal personnel (attorneys or paralegals supervised by attorneys) and sorted for responsiveness and privilege. Responsive, non-privileged documents were compiled into a complete set and then, individual page after individual page, each was numbered (more like impaled) with a hand-held, mechanical, auto-incrementing ink stamp (I can hear the “ka-chunk” of the Bates Stamp now… ah, those were the days).
Privileged documents were set to one side, and summarized in a typed list called a privilege log. Some documents containing privileged information were “redacted” using black markers (there was an art to doing this in a way so that the words couldn’t be read anyway – an art which even the FBI on one occasion in my experience failed to master).
Finally, the completed document set was photocopied, boxed, and delivered to opposing counsel, who in turn reviewed each sheet of paper, page by page.
The Present Generation of Analytics: Software Queued Review
Fast forward to today, the era of eDiscovery and software-queued review. In the present generation software is used to streamline, and thus reduce, the cost of reviewing documents for responsiveness and privilege.
Gathering documents: Nowadays, still relying on instructions from their attorneys, clients designate likely sources of responsive documents from a variety of electronic sources, including email, databases, document repositories, etc. Other media such as printed documents and audio recordings may also be designated when indicated.
After appropriate conversions are made (for example, laptop hard drives may need to be transferred, printed documents may need to be OCR scanned, audio recordings may need to be transcribed, adapters for certain types of data sources may need to be bought or built) all designated sources are ingested into a system which indexes the data, including all metadata, for review.
Some organizations already possess aggressive records management / email management solutions which provide the equivalent of real time ingestion and indexing of significant portions of their documents. Such systems are particularly valuable in a legal context because they enable more meaningful early case assessment (sometimes called “early data assessment”).
Assessing documents: In the current era attorneys can use tools such as Inference which use a variety of analytical methods and workflow schemas to streamline and thus speed up review. (Another such tool is Clustify, which I described in some detail in a previous blog entry.) Such advanced tools typically combine document analytics and summarization with document clustering, tagging, and support for human reviewer workflows. In other words, tools like Inference start with a jumble of all of the documents gathered from a client, documents which most likely contain a broad spectrum of pertinent and random, off-topic information, and sort them into neat, easy to handle, virtual piles of documents arranged by topic. The beauty of such systems is that all of the virtual piles can be displayed — and the documents within them browsed and marked — from one screen, and any number of people in any number of geographic locations can share the same documents organized the same way. Software can also help the people managing the discovery process to assign groups of documents to particular review attorneys, and help them track reviewer progress and accuracy in marking documents as responsive or not, and privileged or not.
The key benefit of this generation of analytics is speed and cost savings. Similar documents, including documents that contain similar ideas as well as exact duplicates and partial duplicates of documents, can be quickly identified and grouped together. When a group of documents contains similar documents, and all of the documents in that group are assigned to the same person or persons, they can work more quickly because they know more of what to expect as they see each new document. Studies have shown that review can be performed perhaps 70-80% faster, and thus at a fraction of the cost, using these mechanisms.
Once review is complete, documents can be automatically prepared for transfer to opposing counsel, and privilege logs can be automatically generated. Opposing counsel can be sent electronic copies of responsive, non-privileged documents, which they in turn can review using analytical tools. (Inference is among the tools that are sometimes used by attorneys receiving such document sets, Nick tells me.)
The Coming Generation of Analytics: Statistically Validated Automated Review
The next software analytics model will be a giant leap forward when it is adopted. In this model software analytics intelligence is calibrated by human intelligence to automatically and definitively categorize the majority of documents collected as responsive or not, and as privileged or not, without document-by-document review by humans. In actuality, some of the analytical engines already in existence – such as Inference — can be “trained” through a relatively brief iterative process to be more accurate in making content-based distinctions than human reviewers can.
To adopt this mechanism as standard, and preferred, in eDiscovery would be merely to apply the same best practices statistical sampling standards currently relied upon to safeguard quality in life-or-death situations such as product manufacturing (think cars and airplanes) and medicine (think pharmaceuticals). The higher level of efficiency and accuracy that this represents is well within the scope of existing software. But while statistically validated automated review has been widely alluded to in legal technology circles, so far as I know it has not been used as a default by anyone when responding to document requests. Not yet. Reasons for this will be discussed in subsequent posts, including the next one.
Gathering documents: The Statistically Validated Automated Review model relies on document designation, ingestion, and indexing in much the same manner as described above with respect to Software Queued Review.
Assessing documents: In this model, a statistically representative sample of documents is first extracted from the collected set. Human reviewers study the documents in this sample then agree upon how to code them as responsive / non-responsive, privileged / non-privileged. This coded sample becomes the “seed” for the analytics engine. Using pattern matching algorithms the analytics engine makes a first attempt to code more documents from the collected set in the same way the human coders did, to match the coding from the seed sample. But because the analytics engine won’t have learned enough from a single sample to become highly accurate, another sample is taken. The human coders correct miscoding by the analytics engine, and their corrections are re-seeded to the engine. The process repeats until the level of error generated by the analytics engine is extremely low from the standpoint of scientific and industrial standards, and more accurate than human reviewers are typically capable of sustaining when coding large volumes of documents.
By way of comparison this assessment process resembles the functioning of the current generation of email spam filters, which employ Bayesian mathematics and corrections by human readers (“spam” / “not spam”) that teach the filters to make better and better choices.
After the Next Generation: Real Time Automated Review
It’s not another generation of analytics, but another significant shift is gradually occurring that will have a significant impact on eDiscovery. The day is approaching when virtually all information that people touch while working will be available and indexed in real time. From the perspective of analytics engines it is “pre-ingested” information. This will largely negate the gathering phase still common in previous generations. Vendors such as Kazeon, Autonomy, CA, Symantec, and others are already on the verge – and in some cases, perhaps, past the verge – of making this a possibility for their customers.
(Full disclosure of possible personal bias: I’m working with a startup with a replication engine that can in real time securely duplicate documents’ full content, plus metadata information about documents, as they are created on out-of-network devices, like laptops, to document management engines….)
The era of Real Time Automated Review will be both exciting and alarming. It will be exciting because instant access to all relevant documents should mean that more lawsuits settle on the facts, in perhaps weeks, after a conflict erupts (see early case assessment, above), rather than waiting for the conclusion of a long, and sometimes murky, discovery process. It’s alarming because of the Orwellian “Big Brother” implications of systems that enable others to know every detail of the information you touch the moment you touch it, and at any time thereafter.
In my next post you’ll hear about my conversation with Nick Croce, including how Inference has prepared for the coming generation of automated discovery and where Nick thinks things are going next.