Since its inception in 2005, eDiscovery has taken on many forms—evolving with us to keep up with the colossal data demands of today. Even so, many law firms, corporations, service providers, and government agencies have not moved on from using the time-consuming and labor-intensive traditional, legacy eDiscovery tools of yesterday.
Some of these tools include spreadsheets, database management systems, cloud storage platforms, and paper management systems.
Let’s take a closer look at how these four traditional tools measure up to modern eDiscovery solutions.
Spreadsheets are one of the most common ways organizations manage their eDiscovery. This comes as no surprise, as spreadsheets are very affordable, easy to learn, can be exported for simple data visualization, and easily shared for rapid analysis.
However, all information—including electronically stored information (ESI)—has to be manually entered and updated cell by cell. This is an incredibly tedious process that is very prone to data entry errors and is quickly becoming an impractical practice with the current, vast growth of data volumes.
- Database Management Systems
Database management systems are most often used when organizations need to ingest large volumes of data from IT endpoints, end users, or customers. Because of this, database management systems have some serious advantages. They are scalable to fit nearly any data set size and are excellent for long-term storage. Additionally, database management systems are helpful with processing and analyzing large datasets very quickly.
Though, the scalability makes it difficult for litigators to manage and deploy database management systems—potentially costing organizations their cases, investigations, and/or audits. These systems also require a subject matter expert that can be expensive and hard to find in addition to the high cost of software due to the cloud storage costs.
Organizations also use cloud storage platforms, such as Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure to aid in their eDiscovery process. Cloud storage allows you to save on computing equipment costs, like physical consoles, and utilizes fast and efficient computing infrastructure to get your eDiscovery processed quickly.
Similar to database management systems, cloud storage platforms are scalable, allowing you to fit the software to your needs. But the hidden costs of cloud platforms, such as changing monthly bandwidth costs, can be higher than expected and the software sometimes requires additional training for your IT department.
Though we are moving further and further into a paperless world, organizations still use this analog method for their discovery. While there are absolutely no cybersecurity threats (because there is no cyberspace to threat), paper management systems are more time-consuming and labor-intensive than spreadsheets. Any ESI must be printed before you can even begin the discovery process and the added expense of mailing can quickly add up.
How the EDRM and AI Shaped the Current State of the Legal Industry
The creation of the Electronic Discovery Reference Model (EDRM) after eDiscovery was added to the Federal Rules of Civil Procedure revolutionized our understanding of eDiscovery processes. The EDRM outlines the eDiscovery process so that legal professionals can ensure they stay in compliance with federal regulations and avoid sanctions and other penalties for missing court deadlines or data spoliation.
The EDRM contains nine stages organizations can use as a reference to build their own eDiscovery frameworks, including
- Information Governance: implementing a plan to reduce risk and lower costs
- Identification: identifying sources for relevant ESI, such as mobile and personal devices, and cloud and on-premise systems
- Preservation: includes the legal hold of any relevant ESI and its protection against spoliation
- Collection: collects all relevant ESI for further use, including through early case assessment
- Processing: culling of ESI and formatting it into more suitable forms for review and analysis
- Review: evaluating ESI for relevance and privilege to develop facts for cases and investigations
- Analysis: evaluating ESI for content, specifically looking at patterns in topic, people and discussion
- Production: delivering ESI to stakeholders
- Presentation: displaying ESI to an audience, such as during trials, depositions, and hearings, to validate claims and persuade the audience
The EDRM is the most widely regarded eDiscovery reference tool, and thus, plays a crucial role in the current state of the legal landscape.
Addition of AI
The introduction of artificial intelligence (AI) only furthered the use of the EDRM and allowed newer, better versions to manifest that combine various stages into a single step. The use of AI in eDiscovery automated different stages that were previously manual so legal teams could process more data faster without compromising their security or accuracy.
When it comes to AI, there are two main types: supervised and unsupervised learning. Supervised learning requires a human to train the technology assisted review (TAR 1.0) software to recognize patterns in ESI until the software can do it itself.
Unsupervised learning actively learns to recognize patterns in topics and keywords, and automatically tags ESI as relevant without human intervention. An example of this is TAR 2.o, commonly known as CAL—continuous active learning.
The implementation of the EDRM and AI has led eDiscovery to be what it is today and caused traditional tools to become outdated. Unfortunately, organizations that rely on legacy practices are being left behind and missing out on bigger cases and investigations due to their lack of storage, processing, and/or review and analysis capabilities.
eDiscovery software today is purpose-built cloud and on-premise eDiscovery systems and applications that aid law firms, corporations, service providers, and government agencies in managing their ESI in any stage from ingestion to production while staying in compliance with FRCP regulations.
One of the best eDiscovery practices is the utilization of fully integrated, end-to-end solutions—like Venio Systems’ Venio Cloud and VenioOne. These solutions often directly correlate with the stages of the EDRM and streamline the eDiscovery process to be better, faster, stronger, and more secure.
However, eDiscovery software itself is still evolving to keep up with the demands of the legal industry and thus, has its own shortcomings. As we look to the future of eDiscovery, we hope to see solutions that answer the questions of today.
What Does the Future of eDiscovery Hold?
The exponential growth of data volumes is a strong driving force for changes to eDiscovery. Not only is there so much more data today than ever before, but there are more and new data types and sources. One of the biggest proponents for this growth is messaging data.
Messaging data contains an abundance of metadata that tells the story of who, what, when, and where—making it crucial to produce for legal cases and investigations. However, because messaging data is a newer facet of eDiscovery, there are not many solutions that can easily collect, process, review, analyze, or produce it.
Fortunately, legal technologists are developing software that can handle multiple messaging platforms and convert raw data into reviewable formats.
Another area technologists are exploring is the use of natural language processing (NLP). With advanced NLP, solutions can refine search results during eDiscovery to more efficiently identify relevant ESI. With these enhancements to the eDiscovery process, many solutions will shorten the EDRM so multiple stages can occur within the time it currently takes a single stage to occur.
Though much of the future of eDiscovery is unknown, one thing for certain is Venio Systems has got your back no matter what challenges arise. With Venio, you can discover it all and experience 10x faster processing for up to 10 terabytes of data per day, reducing your data to review by 90% and increasing your team’s productivity tenfold.