How to Cut Ediscovery Costs by Optimizing Review Processes
Law firms, corporations, government agencies, and non-profits have been leveraging ediscovery technology to effectively and efficiently manage litigation, conduct investigations, perform due diligence, or respond to regulatory requests. As a result, ediscovery is now a critical component of business operations, forcing legal professionals to adopt cost-saving tactics and digital tools to cut ediscovery costs and avoid significant financial strain.
Maximizing efficiency is vital to avoid a manual, error-prone, time-consuming, and, most importantly, costly process — especially after decreasing the volume of documents during document review.
Three Steps to Cutting Ediscovery Costs and Optimizing Review
Even after reducing the overall volume of potentially relevant data, there are still several subsequent tasks that modern technology can optimize within the actual review process. In order to further cut costs and increase the efficiency of per-document review, administrators and their teams can do the following:
1. Utilize Predictive Coding to Avoid Manual Review of Some Documents
Reviewing each document individually throughout the ediscovery process is increasingly less viable in the age of electronic information. In order to remain even minimally productive, legal teams often need to adopt concrete methods to avoid performing manual review whenever possible. Predictive coding or technology assisted review (TAR) is one of the most intuitive and straightforward approaches to speeding up review times without the risk of overlooking potentially critical information.
What Is Predictive Coding?
Predictive coding is a form of machine learning, a technology that most of us are quite familiar with, whether we realize it or not. In fact, if you have an account with Netflix, Spotify, YouTube, or any other major streaming service, then you probably interact with machine learning systems on a fairly regular basis. And in very much the same way Netflix can identify the kind of movies and TV shows you prefer, Everlaw’s technology can identify the information that legal teams believe will be most relevant to win their case.
How Predictive Coding Works
Predictive coding works by using a categorization process. After utilizing the above methods to learn as much as possible about which information is likely to be most relevant, legal teams can feed what they’ve learned into a predictive coding algorithm. Once the legal team establishes an accurate model of preferences, the algorithm can identify documents based on preferred categories and even flag documents with similar characteristics that might otherwise have gone overlooked. Predictive coding can radically reduce the number of documents necessary to review manually by automatically prioritizing the information likely to be the most relevant.
Importantly, not all predictive coding technologies have the same functionality. There are still a number of legacy systems on the market that rely on an outdated, rigid methodology consisting only of training the model before review. With these systems, the algorithm will identify documents based on manually established categories, but it won’t have the capacity to continuously learn and adapt throughout the review process. For example, with Everlaw, not only is the algorithm continuously learning, but it is also constantly assessing its own performance through a process of recall and precision. Over a short period of time, the number of documents correctly predicted to be relevant (recall) and the number of relevant documents returned (precision) will become sufficiently reliable.
Overall, predictive coding is one of the most efficient ways to properly assign documents to batches based on categories and relevancy, greatly simplifying whatever manual review efforts legal teams can’t avoid. And in the most successful cases, predictive coding can significantly expedite the entire review process by identifying the most critical information early on, eliminating the need to manually review hundreds of irrelevant documents and ensuring that teams have the most important documents on hand to support their case.
2. Auto-Code Related Documents
After using predictive coding to organize documents into batches based on categories and relevancy, legal teams can set up the actual review process for further efficiencies with the addition of auto-code rules. Because documents rarely exist in isolation and are almost always directly related to other documents (e.g., messages in an email thread, file attachments, or duplicates), it’s important to clearly understand which documents contain duplicate information to not waste time or resources.
For example, hundreds of employees may have received the same PDF attachment of a corporate memo but not all within the same email thread. By applying an auto-code to the attachment, legal teams can avoid having to review the same memo over and over again in a series of maddening loops. Users can set up auto-code rules on a project level or during the normal course of review, and Everlaw allows administrators to determine and manage who can apply these codes through an intuitive permissions interface.
Moreover, Everlaw’s auto-code review speed clocks in at 140 documents per hour, or 180% faster than the industry standard. When utilized correctly and with full consideration of a legal team’s risk and cost balance, eliminating multiple duplicates from the review process can lead to improved efficiencies and less ediscovery costs.
3. Decrease Doc-to-Doc and Page-to-Page Load Times
Of course, if the above processes aren’t operating from a foundation of responsive technology, then legal teams won’t be able to utilize them to their full potential. And the unfortunate truth is that far too many legal professionals today are still left groaning at their computer screens as they wait for a document to load, an experience that is as frequently frustrating as it is unnecessary.
For example, Everlaw utilizes modern, cloud-native technology that scales incredibly well, dynamically allocating the resources needed to keep review moving along swiftly and without interruption. To introduce a useful analogy, scaling in the cloud is somewhat akin to the functionality of newer combustion engines, which produce efficient horsepower by automatically turning cylinders on and off as they work. The only flaw in this example is that the amount of horsepower an engine can produce has a hard limit, whereas the cloud has the equivalent of infinite horsepower, allocating as many resources as necessary at no additional cost. This can save legal teams hundreds of hours in review compared to traditional technologies, some of which still suffer from multi-second load times.
Technical analogies aside, legal professionals don’t actually need to understand how cloud-native scaling works to realize that waiting for a document or page to load for even more than a second is unacceptable by today’s standards. As a rule, it should take well under 350 milliseconds for any page or document to load, or in other words, much faster than anyone can blink. Everlaw’s use of cloud-native technology, in addition to our intuitive interface, has allowed us to achieve a baseline review speed of 86 documents per hour, or 72% faster than the industry standard.
Interested in learning more about other tactics and strategies for cutting ediscovery costs? Download your free copy of our recent eBook, “11 Ways to Reduce Ediscovery Expenses,” today!
