In our first post in this series, we discussed how eDiscovery technology and workflows are being applied to several different use cases today. According to eDiscovery Today’s 2023 State of the Industry Report, seven use cases are being applied by at least 40% of 410 survey respondents.
It’s no surprise that one of the use cases for eDiscovery technology and workflows is litigation – it’s the use case that eDiscovery was originally designed to support! It’s also no surprise that litigation was the top use case being applied by respondents. What may be surprising is that there were some respondents NOT using eDiscovery for litigation. 96.3% of respondents use eDiscovery technology and workflows for litigation, so that means 3.7% of respondents don’t use it for litigation – further illustrating that eDiscovery isn’t just for litigation anymore. For some, it’s not for litigation at all!
In many of these posts about different use cases, we’ve compared their application of eDiscovery technology and workflows to litigation, so for this post, we’ll discuss how eDiscovery has evolved in recent years to support litigation (and eventually other use cases) and is continuing to evolve today and into the future in terms of workflows, best practices and leveraging technology to support litigation and much more.
The Early Years: Pre 2000 to 2009Before “eDiscovery” was coined and during the early years when electronic information became in scope for discovery, the “eDiscovery” function was often lumped into the general category of “litigation support”. Management of information within organizations was known as “records management” because the information was originally located in hard copy documents; this evolved into “Information Governance”.
Software platforms and tools were designed to perform specific functions within what we know now as the eDiscovery lifecycle. For example, two of the earliest popular platforms – Summation and Concordance – were initially designed for recording and tracking of bibliographic data of hard copy documents. As imaging of hard copy documents became a standard, those two platforms evolved into review tools that could facilitate litigation productions.
As email became a common source of electronic evidence, processing tools like Discovery Cracker and LAW Pre-Discovery became popular for processing electronic documents and data for discovery. However, once the documents and data were processed, they needed to be manually extracted and loaded into a review platform. Every function – from collection to presentation – typically required a separate tool to get the job done with manual processes in between to move the data along and those tools were located primarily on-premise.
Two things happened in 2005 that established electronic discovery as its own discipline. First, the US Supreme Court amended the Federal Rules of Civil Procedure (FRCP) to include a category for electronic records. Additional rules changes in 2006 established parameters for cooperation (FRCP Rule 26(f) “meet and confer”), form of production (Rule 34(b)) and sanctions (Rule 37).
Additionally, the EDRM model was created in 2005, which reflected the phases of eDiscovery from Identification to Presentation and establish a widely adopted representation of a typical eDiscovery workflow.
The Middle Years: 2010-2019
As we moved into the second decade of the 21st century, eDiscovery teams began to increase their focus on automation and standardization and a move to cloud-based solutions. Phases like data identification, preservation, collection, processing, and review became more streamlined and the term “end-to-end” solution became a common selling point for eDiscovery providers who were looking to differentiate their offerings from other providers that still required several manual steps between eDiscovery phases.
As document and data volumes began to grow exponentially, text analytics and predictive coding/technology-assisted review (TAR) became popular for speeding up review processes, especially after now retired New York Magistrate Judge Andrew J. Peck issued the ruling in 2012 in the Da Silva Moore case that was the first court approval of TAR.
Because of that data growth (which began to expand to other sources like mobile devices and messaging apps), organizations also began to proactively manage their data to better support eDiscovery processes and the term “information governance” became its own discipline (evolving from records management) to represent the strategic, multi-disciplinary approach to managing and using information within an organization. InfoGov and eDiscovery became interdependent.
As cyberattacks on organizations rose, along with the passing of Europe’s General Data Protection Regulation (GDPR), data protection and data privacy increased in importance. eDiscovery workflows began to be developed for non-litigation use cases such as investigations, privacy requests, and incident response.
From a litigation rules standpoint, in 2015, proportionality factors were moved from FRCP 26(b)(2)(C) to Rule 26(b)(1) in order to emphasize their importance and changes to Rule 37(e) established the elevated “intent to deprive” standard to impose sanctions for spoliation of evidence.
The Current Years 2020-2023 and Beyond
As we moved into the second decade of the 21st century, the COVID-19 pandemic dramatically pushed the level of remote collaboration considerably forward, leading to many more organizations using cloud-based solutions for everything from email and office functions (e.g., M365 and G-Suite) to communication and collaboration (e.g., Slack, Zoom and MS Teams) and many other enterprise solutions.
These developments have dramatically increased the variety of discoverable data sources, which has led to a need to automate collection from a variety of enterprise platforms. It has also forced eDiscovery teams to adjust workflows in terms of how the data is managed during the discovery process, leading to challenges that include addressing linked files (i.e., “modern attachments”) and defining a “conversation” for messaging platforms.
The application of AI and machine learning to eDiscovery workflows has increased significantly in the last few years with the use of advanced algorithms capable of semantic understanding and data pattern recognition, including identifying personally identifiable information (PII). Now, the increased focus on the capabilities of large language models (LLMs) and generative AI technologies could completely revamp eDiscovery workflows once again! The pace of change is only increasing!
eDiscovery technology and workflows started out as being focused on litigation, but now it could literally apply to any use case involving the management and presentation of data, and the technology and workflows used to support eDiscovery are always changing. Working with experts to keep up with the changes in data sources, technology and workflows is essential to adapting your “swiss army knife” to support not only litigation but every other eDiscovery use case as well – today and tomorrow!
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In case you missed the other blogs in this series, Why Use a Hammer When You Can Use a Swiss Army Knife?: Use Cases for eDiscovery Today, you can find them here: