The approach of a new year is often a good time to step back and take stock of the ediscovery industry, so that we can be better prepared to move forward. One of the most dramatic changes over the past few years has been the seismic shift across the legal and corporate data landscapes. That shift has slowly been expanding the concept of ediscovery beyond a single-litigation focus, to encompass data governance, data privacy and security, and an overall more holistic, strategic approach to review and analysis.
As we prepare to move forward in this brave new world, it’s important to understand how those industry changes affect the traditional framework of the ediscovery process: the Electronic Discovery Reference Model (EDRM). Recently, I was lucky enough to join a panel of industry experts, including Microsoft’s EJ Bastien, TracyAnn Eggen from CommonSpirit Health, and Lighthouse’s Sarah Barsky-Harlan, to dive deeper into that specific issue. Together, we tackled questions like: Does the EDRM still apply in today’s more complex ediscovery environment? If so, how is the evolving data and ediscovery landscape reshaping how organizations and law firms think about the EDRM? How can the EDRM be used to meet today’s more complex communication, data, and business challenges?
Below are some of the key themes and ideas that emanated from that discussion:
A Brave New Data World: Dynamic Changes in eDiscovery
Since its inception, the EDRM has been the industry’s standard approach to the ediscovery process (i.e., identification, collection, processing, review, analysis, and production of electronically stored information (ESI)). However, what we’re seeing today is that organizations and law firms now must think about ediscovery in much broader terms than that traditionally very linear method. There are three primary reasons for this change:
- New cloud-based and Software as a Service (SaaS) systems: Enterprise systems are not nearly as controlled by the underlying organization as they used to be. Even five years ago, IT departments could more closely manage what software was installed, as well as when, how, and what upgrades were rolled out. Now those updates and installations are managed by cloud providers, with upgrades rolling out on an almost weekly basis – often with no notice to the organization. All those changes have downstream ediscovery impacts, which must be dealt with at each stage of the EDRM process.
- New data formats: Data is no longer structured in the traditional document “family” of an email parent with attachment children. The shift to chat and collaboration platforms within organizations means that communications and workflows generate more data across multiple data sources and are much more fluid and informal. For instance, instead of an employee working on a static document saved on a desktop and then passing that document back and forth to co-workers via email, those employees may work on that document together while it’s saved on a cloud-based collaboration platform, chat about it via an in-office chat application, post updates on it via the collaboration tool channel, as well as email copies back and forth to each other. This means counsel must analyze how relevant data ties together and analyze the relationships between data sources in order to understand the full story of a communication during an investigation or litigation.
- New capabilities with ediscovery technology: There are many new types of capabilities that are native to enterprise systems, as well as new types of analytics and artificial intelligence (AI) that can handle more data at scale. These new capabilities are allowing case teams to leverage past data on new cases and get to key data more quickly in the EDRM process.
The Impact: How Those Changes Affect the EDRM Framework
Thinking of the EDRM as a monolithic linear process that flows straight from beginning (collection) to end (production) does not fit the way ediscovery takes place in practice anymore. There is a world of complexity within each step of the EDRM – one that is highly dependent on the data source. And the decisions made along the way for each data source at each new step will impact what happens next – often in a non-linear fashion: Sometimes that next step will send practitioners back to collection again, because they found another data source during review. Sometimes review takes place simultaneously with collection or processing phases, depending on the data source and those newer capabilities discussed above. In short, the old model of collecting all data, exporting it all, and then reviewing it all, in large chunks, one step at a time, is no longer applicable nor practical.
Instead, a “mini-EDRM” framework might make more sense, where organizations prepare workflows for the preservation, collection, processing, and review of each particular data source. Thinking of the EDRM in this way also helps the framework stay relevant and future-proof as practitioners deal with the sea-change happening across our data landscape. Practitioners need to be agile enough to handle new data sources as they pop up, for each step of the EDRM process, and then be prepared to do it all over again when someone in a deposition mentions another new data source, and to adapt it when something changes in the data source. A mini-EDRM framework would help organizations and practitioners better meet those challenges.
The EDRM and Data-in-Place
As noted above, the ediscovery process is now much broader and has much more of an impact on organizational information governance and data-in-place than ever before. This presents an opportunity to use learnings from across the EDRM to more effectively manage data “to the left” of that traditional process. For example, if a particular data source was problematic during review, that information can be disseminated at the organizational level and help inform how that source is used within the organization moving forward. Or if practitioners notice a large volume of irrelevant data during review that shouldn’t exist in the system at all, that information can be used to redraft document retention policies. In this way, ediscovery (and the EDRM framework) can now be a force for change over the entire organization.
Thinking Beyond a Single Matter
In today’s more dynamic and voluminous data landscape, the work we did in the past is more valuable than ever before and it can be used to inform and impact current processes across the EDRM.
This can come in the form of people and institutional knowledge: experienced and consistent staff and outside partners are an invaluable resource. These organizational experts can use their understanding and experience with an organization’s past matters, system architecture, data sources, workflows etc. to improve ediscovery efficiency and solve current problems more effectively.
It can also come in the form of technology: when the EDRM first evolved, data analytics were a much heavier lift. The process and tools were expensive and the amount of data that they could be applied to was much smaller than today. Advancements in AI capabilities now allow us to analyze much larger volumes of data with much more accurate results. Thus, this newer, advanced AI technology is now capable of leveraging the goldmine of millions of previous decisions made by attorneys on an organization’s past matters. That work product is baked into the data, and advanced AI can use it to make more accurate decisions on current data at a much larger scale than ever before.
Tips to Keep the EDRM Applicable in an Evolving Data Landscape
- Strive to retain institutional knowledge across matters: The constantly evolving ediscovery landscape makes continuity and retaining institutional knowledge incredibly important. Starting from scratch each time you confront a new data source or problem along the EDRM is no longer practical with today’s diversified and larger data volumes. Work to cultivate valuable partners and staff who will work to understand your organization’s data architecture, as well as the ediscovery workflows that are effective within your environment.
- Lean on your peers: Chances are, if you’re facing a problem with a challenging data source at one stage of the EDRM, someone in your peer group has also faced the same or a similar problem. Don’t be afraid to reach out and ask folks to benchmark. Peer experience can help each practitioner learn and move forward, solving challenging industry problems along the way.
- Open the lines of communication: Because the EDRM process is much more iterative and each step impacts other steps, it is incredibly important that the people working on those steps do not work in silos. Everyone should know the downstream impacts of their decisions and workflows.
- Test… and test again: Employ a testing framework to test the impact of ediscovery workflows on the underlying platforms, and then have a feedback loop to apply changes. This will ensure your ediscovery program is forward-thinking, as opposed to reactive.
- Automate where possible: When striving for repeatable, defensible ediscovery processes, predictability is key. And automation, when feasible, is a great way to achieve that predictability. Automating workflows across the EDRM will not only help improve efficiency and lower costs, it will also help minimize risk and keep your ediscovery program defensible.
If you are interested in this topic, feel free to reach out to me at firstname.lastname@example.org.