In the beginning, there was paper. And lots of it.
Discovery and production requests were designed with paper in mind. Now, there’s far less paper, yet discovery (or rather, “ediscovery”) has not evolved to catch up. How does a Slack channel with 20 people talking about a specific topic over a two-year period translate to the concept of a document? Why are we trying to conform a Zoom meeting with video, chat, and polls to 8.5”x11” production format?
From Banker Boxes full of Redwelds to the deluge of email and now the mobile application invasion, uncovering evidence in discovery has been a wild ride over the last two decades. The amendment of the Federal Rules of Civil Procedure (FRCP) in 2006 ushered in an era of electronically stored information (ESI) and ediscovery, but the process and key concepts were still anchored in a very paper document-centric perspective.
The way we communicate and conduct business has fundamentally shifted and the digital fingerprint we leave behind is often quite far from traditional notions of a document. Times are changing, and to quickly surface evidence and advocate for clients in a new digital era our views on ediscovery need to shift as well.
Discovery is not a new concept, in fact early iterations of the concept date back to the court of chancery in the 16th century. From inception through as recently as 2006, discovery was reliant on oral depositions and physical documents. Despite the extension of the FRCP to include ESI as a relevant discoverable source of information, as recently as a decade ago people were still calculating ediscovery reviews in terms of Banker Boxes of paper documents for review.
In the early 2000s, I worked on a massive matter that was estimating review size based on the number of train cars the Banker Boxes would fill. Ten million documents came out to nine train cars filled with Banker Boxes each holding around 3,000 pages if you were wondering.
The transition from folders and boxes of paper to drives and servers of data dramatically changed how attorneys practice law and created the ten billion dollar ediscovery industry almost overnight. Despite the massive shift to ESI, most legal practitioners still evaluated and performed discovery using the concept of a physical document as a cornerstone. While this concept was not a stretch for things like a word document or angle email, it became more challenging as new sources of information entered the equation.
Ceci n’est pas un doc (This is not a doc)
Today, evidence may reside in text messages, Twitter feeds, Slack channels, or any of a myriad of other app-based methods of human interaction. These new evidence sources do not fit the traditional document mold. If 10 team members have a discussion on a Slack channel and move it to text and the topic extends across more than one day, where should the evidence be broken up to denote a “doc” during ediscovery review? How do GIFs and emojis and other short form and abbreviated communication across multiple custodians fit into the equation? Times have changed and something has to give.
While it was simple enough to ascribe document characteristics to email (as self-contained conversations between clearly defined players on a specific topic or set of topics), new application-based communication and collaboration tools are a fundamentally different animal. So, how exactly do these new communication tools differ from traditional documents?
Data, data everywhere
Key info can reside concurrently in multiple locations because modern conversations frequently traverse from email to text to collaboration tools and back again. A single topic thread may traverse internal and external hardware and cloud storage and reside in different platforms and data formats depending on what tools are leveraged. A single subject may have relevant information in a variety of locations and be subject to differing possession, custody, and control.
Square peg, round hole
Platforms like Teams, Slack, text, and other instant messaging tools do not have the same boundaries as a traditional document. Parties can jump in or out of a conversation at any time, topics may extend across multiple channels and varied time periods and attachments, and emojis, videos or GIFs may all be relevant. Breaking up a Slack channel discussion just to fit on a page or even something as seemingly simple as determining if portions of a discussion are privileged becomes very challenging when conceiving of the communication like a document.
Data has become like Baskin-Robbins
Before you make a quip about Tiger King and Carol Baskin, think Baskin-Robbins and 31 flavors. Legal practitioners face new methods of communication and generation of ESI complete with new data formats, storage locations, and limitations. From .JSON to .PST, .XML to .MSG, legal practitioners need to be versed in the many file types these new communication methods rely upon and understand which partners can best support extracting and parsing the data type they are conducting ediscovery on.
Connecting the dots
Because a conversation may spread across multiple communication streams, involve attachments or emojis, and defy current date parameters, it is important to adjust scope and scale accordingly. To find key evidence, it is important to use all the tools at your disposal to triangulate and connect the dots across multiple platforms to paint a complete picture. Advanced AI-powered tech is especially important to make these connections which are beyond the capability of human cognition when dealing with so much data across so many sources.
The doc is dead. Now what?
What does this mean for legal practitioners? First and foremost, where you look for data has to expand to include these weird not-doc types of data. From revising ESI protocols to refining custodial interviews ensure that new data sources are covered. Once you have access to these new data types, making sense of the formats, linguistic nuances, and fluidity of topics is the next hurdle to face.
Because key concepts in this post-doc universe often traverse a myriad of data types in a given conversation, AI becomes an increasingly important resource. Advanced algorithms can make correlations across disparate data types in a way the human mind is not equipped to. In next-gen tools, AI surfaces cross-data-type concepts or identifies “similar” pieces of ESI regardless of data type. If a custodian discussed a key topic in text, then moved to Slack, sent an email about it, and then typed a short Slack message, AI can locate and correlate the similar concepts despite the differing formats.
Collaboration tools, instant messaging, and SMS differ from a traditional document format, ensuring that you have a tool that takes into account this difference is key to accelerating review and reducing time to insight. Some tech renders Slack channels in a manner that arbitrarily separates threads into separate files that are hard to efficiently review — and you will want to avoid that if at all possible. Ensure that when partnering with a provider you have an ability to review examples of key data types likely to be in-scope.
Social network analysis
Leverage social network analysis to prioritize custodians based upon the frequency of communication with other key custodians across data sources. As you have more and more sources of data to review, leveraging tools to effectively prioritize and triangulate limited resources is extremely important.
AI model sharing
As the universe of data continues its explosive growth in variety and velocity, relying on tools like AI model sharing to help you start with more insights even at the outset of a review is increasingly important. Whether applied to a previously reviewed set of data or custodians or just a conceptually similar type of matter, the reduction of time to insight afforded by using AI model sharing is significant.
The business world is adapting to meet the rapidly evolving way that humans connect, communicate, and do business — and so must ediscovery. The first step is to acknowledge the fundamental differences in the tools we use to stay engaged in the digital world. Using the insights and skills we have developed over the last decade and a half of ediscovery evolutions, paired with next generation technology, practitioners can move beyond the doc and accelerate speed to evidence. We just can’t be afraid to say, “This is not a doc!”