Anyone who has ever had to search for relevant information in a case understands that organizations are complex cultures with their own languages, and there is always more than one way to uncover the truth.
For the last decade, lawyers have used analytics technology to help them uncover patterns and insights from conversations and work product to better manage the complexity of this task. During the same period, technologists have refined the technology, no longer requiring the perfect document, hard cut-offs for case sizes, or even much case setup to see a big impact. This progress has made analytics and artificial intelligence much more accessible and performant than ever before.
Early on, the momentum around analytics in e-discovery centered in the United States, where it could help manage some of the largest and nastiest document populations in a highly litigious environment. Judicial acceptance of the technology opened the gates for analytics to be considered—and in some instances, mandated—for more and more cases. And the momentum took off from there.
Finally, we started to see analytics permeate the e-discovery process, adding tremendous value at each stage compared to a linear application for review of large data sets.
Now, the battle-tested technology is finding a new place in a region that previously didn’t feel the pain of large manual document review for litigation.
Evolving e-Discovery in Asia
The e-discovery landscape in Asia is evolving quickly—jump-started by a few drivers:
- Increases in U.S and EMEA inbound litigation. Countries in Asia haven't historically recognized the same discovery obligations in their legal systems as the United States and Europe, but are increasingly finding themselves beholden to litigation regulations in other areas of the world where their organizations conduct business. Adding to this new load is an increase in investigations, both global and local, driving a greater need for e-discovery software.
- New privacy and anti-corruption efforts around the world. With new and varied data protection regulations coming online every year, and stricter enforcement of the Foreign Corrupt Practices Act (FCPA), compliance has never been more important. Even without an obligation to reactive discovery requirements, data management expectations are higher than ever. Countries in Asia have a bigger appetite to proactively monitor and prevent non-compliant activities before they become a problem.
- Changes in working and communication habits during the pandemic. Prior to the COVID-19 pandemic, digital information was growing at an inconceivable rate. In 2020, as we all moved most of our work and personal conversations to digital channels with hundreds of different conversational and multi-modal message types—including streaming audio, video, and chat—we accelerated both the pace and complexity of data growth.
As data grows in both magnitude and variety and the use cases for e-discovery, investigations, and compliance increase, organizations in Asia have taken to analytics solutions that are already at their fingertips—technology that has been crafted and refined in other areas of the world that have experienced similar momentum—and the impact is exciting.
At Work in the Real World
Kim & Chang, the largest law firm in South Korea, has long been an adopter of the Analytics suite in Relativity to help their clients save time and costs. Their team believes that such technology is instrumental in navigating increasing data volumes and tight deadlines.
On a recent compliance monitoring project, Kim & Chang was given two weeks to complete a review of more than 56,000 documents. They decided to deploy Relativity’s active learning workflow so their reviewers could dig in immediately, putting eyes on only the most relevant information.
And they’re not alone: Active learning is used by 80 percent of Relativity customers, which has led to predictions on nearly two billion documents worldwide.
The technology works by using machine learning to continuously assess what is important and serve up relevant information faster. In an active learning project, the reviewer sends document coding decisions to the model, the model updates, and the latest set of prioritized documents are made available for the next batch to review. It can be an excellent support system that not only speeds up a long and complex process, but is great at quickly surfacing where coding errors or gaps in the data are happening and finding relevant documents that a human reviewer may have missed.
In their case, Kim & Chang’s decision to use active learning allowed them to complete the project in only five days—resulting in an estimated 20 percent reduction in cost for their client.
Throughout 2021, more than half of all Relativity’s active workspaces in Asia have been using analytics capabilities to their advantage. Much like the U.S. and Europe, early usage has been focused on structured analytics like email threading and language detection, which are small, but powerful ways for new users to get started. However, forward-thinking legal professionals, like the team at Kim & Chang, are rising to new data challenges with an innovative and proactive mindset. They’re using machine learning to make an even bigger impact on tough and ever-evolving data challenges.
Artwork for this article was created by Sarah Vachlon.