Over the last decade-plus, data creation and usage have skyrocketed. Today, companies across all industries are accelerating digitalization and producing massive volumes of information. This trend doesn’t appear to be slowing down any time soon.
From a business and operations standpoint, data is a wonderful thing. But from a legal perspective, it can be a tremendous liability. This is doubly true for companies that can’t quickly search, discover, and analyze the information they are collecting.
As a result, a growing number of companies are using predictive coding to streamline the eDiscovery process for electronically stored information (ESI).
Read on to learn how predictive coding works and how it helps legal teams move faster and more efficiently during audits, investigations, and legal proceedings.
What Is Predictive Coding?
Predictive coding—or technology-assisted review—is a technology that uses machine learning and artificial intelligence to quickly access and analyze ESI. Predictive coding helps teams discover information across multiple computer systems and databases.
Companies, law firms, and government agencies use predictive coding to quickly analyze large volumes of information and make decisions at a faster clip than manual analysis. As a result, there’s a growing demand for predictive coding among organizations that manage large data sets and need to comply with internal and external protocols.
To use predictive coding, you need to train software with data. After that’s done, reviewers code each document as relevant or irrelevant. This also requires the help of an attorney with technical and industry experience.
Over time, predictive coding software becomes better—or smarter—at reading documents and making decisions. Eventually, minimal human oversight is needed during the process.
How Does Predictive Coding Expedite eDiscovery?
Organizations must move quickly during eDiscovery to respond efficiently to ESI requests and keep workflows moving. For this reason, traditional information discovery methods no longer apply in today’s fast-moving, data-driven world. And organizations that continue using outdated eDiscovery methods risk falling behind and missing deadlines.
With this in mind, let’s take a look at some of the ways that predictive coding helps expedite the eDiscovery process.
Reduce Manual Data Processing
Over time, predictive coding reduces manual data processing and enables automation, eliminating mundane and time-consuming workflows. It also lowers eDiscovery costs.
What’s more, eDiscovery firms can use automation as a selling point when working with clients. This can demonstrate that a firm is leveraging the latest technology and implementing efficient business practices.
Free eDiscovery Analysts to Focus on Other Work
eDiscovery analysts often waste hours combing through data sets and extracting information. This limits the amount of work they can take on and pulls them away from other projects.
By using predictive coding, eDiscovery analysts can spend more time analyzing information and moving cases forward. This leads to massive efficiency gains for legal departments and eDiscovery teams.
Accuracy is imperative during audits, investigations, and legal cases. Missing important files during eDiscovery creates delays and leads to conflicts and penalties. It also extends project timelines and leads to higher eDiscovery costs.
But with a robust predictive coding service in place, eDiscovery is much more accurate. Teams can reduce data collection errors and produce reports and data sets that are free of errors and duplicate data. This reduces rework and also helps cases move faster.
Reduce Data Volume
One of the most important parts of eDiscovery is trimming down large datasets and eliminating excess information that isn’t useful during a case. But without the right tools, this is very difficult to accomplish in a timely manner.
Predictive coding makes it easy to sort through and cull data sets. For this reason, it’s an excellent tool for enterprises that collect data at scale.
eDiscovery professionals are in high demand due to rising data volumes and increasing regulatory compliance. As a result, there tends to be high turnover in eDiscovery.
Unfortunately, turnover is very expensive, bad for morale, and ultimately slows down workflows and makes it harder for eDiscovery teams to complete projects on time.
Predictive coding enables eDiscovery teams to work quickly and efficiently. This makes for a more pleasant, productive, and effective environment that is attractive for eDiscovery analysts, managers, and executives.
To illustrate, imagine a rising eDiscovery star at the top of their game. That individual would most likely rather work in a fast-moving environment with predictive coding and automation instead of one that uses outdated and ineffective eDiscovery tools.
Predictive Coding Best Practices
Predictive coding is undeniably important for eDiscovery—and something that all firms should know about. However, eDiscovery teams often rush in and deploy predictive coding without fully understanding what it entails. This leads to suboptimal results and skepticism.
When integrating predictive coding into your eDiscovery model, keep the following best practices in mind to avoid complications and achieve maximum results.
Educate Workers About Predictive Coding
Predictive coding is an amazing technology with transformative potential. But it’s important to make sure eDiscovery workers understand how it works and why it’s important.
Consider hosting a lunch-and-learn or group training session to explain predictive coding, ask questions, and gauge your team’s overall understanding of the technology.
Training Is Part of the Process
It’s important to realize that predictive coding gets better over time. The quality of predictive coding increases with specific and concise training. That being the case, it’s important to keep up with training and using the platform. Over time, it will get better at understanding incoming data sets and making decisions.
Be Selective When Purchasing a Platform
Not all eDiscovery platforms offer the same level of predictive coding quality. As such, it’s necessary to be selective when finding a platform and integrating it into your environment.
As you begin your search, it’s a good idea to look for a flexible platform that can take on small and simple cases or scale to large and more complex ones. Another vital feature is continuous active learning (CAL), where the software continuously improves its results by analyzing user feedback.
Predictive coding can be a potential security threat due to the vast amount of information that the system must collect and process. It’s critical to integrate a predictive coding platform that prioritizes security and comes with the latest updates and regulatory protocols—like HIPAA and GDPR compliance.
In addition, you should consult with your company’s security advisors before using predictive analytics to bring them in the loop about the platform that you are using. This way, the security team can adjust their strategy, explain potential concerns, answer questions, and green light the project. This can go a long way toward protecting your sensitive data and avoiding problems.
How Venio Helps With eDiscovery
Here at Venio Systems, we’re on the cutting-edge of predictive coding. We offer VenioOne eDiscovery Assisted Review, which enables predictive coding for every data set and case.
With the help of our predictive coding solution, eDiscovery becomes much faster, easier, and more accurate. This, in turn, leads to cost savings, better client relations, and better case management. The platform is also user-friendly and comes with the full support of our dedicated team of experts.
At the end of the day, predictive coding is one of the most powerful tools you can introduce into your eDiscovery environment. Thanks to Venio, you can enjoy next-generation eDiscovery support with blazing-fast speeds and unmatched accuracy.
To see this powerful platform in action, schedule a demo today.
About the Author
Justin Reynolds, Akshita Singhal & Lianna Vaughan
This post was written by Justin Reynolds. Justin is a freelance writer who enjoys telling stories about how technology, science, and creativity can help workers be more productive. In his spare time, he likes seeing or playing live music, hiking, and traveling. This post was reviewed and published by Akshita and Lianna, the in-house team of Venio.