The document review process plays a critical role in complying with discovery requirements, and as a result, review is the most expensive step. No technology eliminates the need for this essential process; however, it’s time to reshape our focus on how to approach this oftentimes daunting task. Current approaches are cost-prohibitive and stand in the way of uncovering critical merit documents and themes.
To get the full sense of how much data entering review is actually irrelevant, consider that a typical corporate employee receives 121 email messages daily, amounting to more than 41,000 emails sent and received per year. Using this example, let’s say review attorneys are hired at $60/hr to review three years of this typical corporate employee’s email. The document reviewers manage to review 60 documents per hour and ultimately produce 20% of the documents reviewed.
Understand and prioritize your data in order to move less but more relevant information into the hands of your review team. With a “less and more relevant” approach, law firms, and corporate legal departments can improve outcomes while reducing costs, staffing hours, and stress.
Law firms and companies tend to consider only the here and now — not what will provide lasting improvement. They’re concentrating on the pain they are experiencing rather than understanding the many compounding problems that create the pain.
There are several reasons why most attorneys can’t see beyond the moment. Many are not only uncomfortable with technology, they’re uncomfortable admitting that they’re uncomfortable. They can’t be expected to think deeply about changing a historically linear eDiscovery process, where the focus is inevitably on the area with which they have the most involvement: attorney review.
A large number of law firms and legal service providers continue to follow long-held practices. Even if they’ve considered changing their approach to eDiscovery, when it’s time to act, they defer to past practices and forward almost everything to review.
The cycle of repeating past performance can be difficult to break. With vendors and service providers handling the bulk of processing, the discovery process will continue to center on volume. There will be little incentive to change the process unless the company that pays the bill objects.
Consider integrating analytics and data consultants into case teams. These resources can work with counsel to apply workflow steps that assist the case team collectively to better understand the facts, or perceived facts, of a case, target the right data, and step through that data together. Collaboration can work, but only as long as the data analysts know what they’re looking for and the appropriate technologies are leveraged.
Without question, the urge to review every document is a mindset that needs to be changed. By recognizing the window of relevance is usually small, all parties involved should look to establish tighter and more iterative review workflows. The first step is to focus on substance, not on volume. It’s all about prioritizing the data set.
It’s difficult to stress relevance when it’s not always known just what is relevant, but the road getting there doesn’t have to be perfect. It only has to be reasonable. The key is finding enough dots to connect to create a picture, and that picture will tell you what needs to be reviewed, thus helping eliminate the noise.
Once you’ve established an effective workflow, it will become clear that some items must move forward, items that are more relevant than others, and items that shouldn’t move forward at all. In a legal document review with a wide scope, everything moves forward, but valuable resources are wasted.
Lastly, take advantage of various analytics and artificial intelligence (AI) capabilities. You can vastly improve targeting documents before review, by utilizing layered approaches like searches, clusters, and topics. Predictive coding AI can enhance review by acting as a force multiplier to prioritize review by scoring all of the documents in the review as decisions or new discovery requests are applied.
Historically, when discovery was a process based around paper documents, there was no intent that every document would be reviewed in its entirety. Now, the intent has changed with the use of electronically stored information (ESI), and almost everything is flagged for review.
The goal of data management and the early data intelligence process should instead center on identifying data sets that have more relevant characteristics. These should be moved to attorney review, and as more information is learned, the intelligence can be applied to prioritize the next set of documents to be reviewed in more detail.
Changing the way we define the review problem will reap the rewards in the long run. But a shift in focus demands an in-depth and honest study of the problem from the legal industry and not a quick fix through more automation and technology.
Reveal AI can help you mine vast quantities of data, find relationships between custodians and content, uncover additional keywords, and narrow the scope of eDiscovery. Leveraging these capabilities means significant time and cost-saving by reducing the volume of data for review and giving legal teams the information needed to set your case strategy early in the process.
Our cutting-edge AI and machine learning uses four key components:
If your organization is interested in eliminating time-consuming work, contact Reveal to learn how our AI-powered eDiscovery software can help. We’ll be happy to show you how we deliver less but more relevant document review information with our end-to-end document review platform.