The e-discovery world is currently undergoing a new cycle of technology upgrades. This trend is driven by several key factors. First, the harsh realities of rapid data growth and risk concerns have propelled many organizations to rethink their spending on e-discovery practices and technology. Second, the conversion of industry leader Relativity to the cloud is driving a large number of users to re-evaluate their legacy e-discovery platforms, which may have seemed state-of-the-art just a few years ago but now look increasingly obsolete. Third, there is a growing consensus in law departments that technology is the most important factor in driving operational efficiency. The most recent Altman Weil survey of chief legal officers makes this crystal clear, revealing that “[o]f ten options to improve law department efficiency, the most common response…is a greater use of technology tools.”

However, the promise of technology can easily dim if the tools you select are unable to keep up with growing data volumes, increasing data complexity, and evolving business requirements. Organizations evaluating new e-discovery practices and technology investments must carefully analyze the total cost of ownership (TCO)—including both direct and indirect costs—when evaluating their current practices and technologies, and then identify the process and technology changes that have the most potential to reduce those costs.

Here, in my view, are the recent technology developments most likely to present organizations with promising opportunities to reduce TCO related to e-discovery.

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1, Built-in Artificial Intelligence

Some of the best e-discovery technology now includes full-strength review with artificial intelligence (AI), cloud analytics and collections, and robust data processing capabilities. In high-stakes litigation, the fear of missing critical pieces of evidence hidden in the document population or inadvertently disclosing privileged content is so high that organizations are often willing to pay expensive legal professionals to conduct legal review. Legal technology experts like Maura R. Grossman and Gordon V. Cormack have exposed the limitations of that kind of thinking and have demonstrated that parties “can quickly and easily identify substantially all of the relevant documents in a collection” using technology assisted review (TAR) with a continuous active learning (CAL) protocol. Many legal professionals, including a growing number of judges, support the use of TAR/predictive coding as the only way to ensure the right to a speedy and fair trial in the era of big data.

AI technologies like analytics, machine learning and TAR have been largely concentrated in the review phase, in part because more than 70 percent of e-discovery spending still goes to cover hourly rates for lawyers sifting through data and tagging it. But a few of the more far-sighted vendors now have AI running in their early case assessment (ECA) and collection processes to dramatically reduce the size of the document set before review.

The most advanced platforms also feature a technology stack that is designed for maximum extensibility, meaning they can be readily modified and built upon to create custom workflows and accommodate a continuous, iterative process of technology adoption, thereby avoiding the trap of rapid obsolescence.

Built-in, pre-installed artificial intelligence dramatically reduces review time and data size in litigation matters, which means more efficient workflows and greater cost savings.

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2. Integration of E-discovery Platforms Across the EDRM

Plenty of vendors market their solutions as end-to-end e-discovery solutions; however, some of these offerings are not actually continuous. Instead, they have “bolted on” additional functionality to an existing application and lack cohesive platform design. There are collection and ECA providers who try to move rightward on the EDRM spectrum into review, and review pioneers who are trying to move leftward into collection. What can result is an inconsistent—and expensive—patchwork of products and data types that must be moved from application to application, and in and out of the cloud storage, thus exposing the data to cybersecurity threats. Organizations are left struggling to manage a set of fragmented and overlapping technologies that must be constantly orchestrated to serve the requirements of each new case or investigation.

Smart automation in a contiguous, unified platform has huge untapped potential to reduce the complexity of responding to or initiating legal actions by optimizing and integrating workflows, reducing the number of platforms and providers you need to manage, and eliminating duplicate processes. Integration also makes it easier for organizations to develop and implement long-term information management and cost-control strategies. A comprehensive platform with built-in analytics, for example, can be leveraged for customized reporting that could give CLO's unprecedented insight into spending patterns and help identify operational bottlenecks in e-discovery and other legal workflows.

A truly integrated e-discovery platform can reduce your software license costs and provide a more efficient workflow, significantly improving the TCO.

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3. The Cost Benefits of the Cloud

Supporting this AI/TAR/predictive coding push is the cloud. It is now beyond dispute that true cloud architecture brings major benefits: it keeps data in one place and protected with enterprise-grade security, it serves the front-end technology and reduces burdens on internal IT staff, it enables on-demand self-service, it enables location-independent resource pooling and it provides rapid elasticity and scalability. All of these factors have a bearing on long-term costs.

Properly implemented with the right vendor, cloud architecture can reduce the up-front costs (installation, hardware/software, training) associated with legacy e-discovery platforms, reduce recurring fees (annual maintenance, support fees, user seats), minimize ongoing operational expenses (power, cooling, real-estate space, redundancy, staff) and enable cost-effective scaling of technology to specific projects.

Enterprises everywhere continue to move their data and applications to the cloud. Important emerging data sources for discovery like social media content and mobile applications originate from the cloud. Infrastructure vendors that own most enterprise data and can support the data sovereignty, privacy and security requirements are helping to consolidate the market for “left-hand side” EDRM capabilities such as identification, collection, and preservation. From an e-discovery perspective, the ability to securely analyze and collect data directly from cloud-based data sources like Office 365 and Microsoft OneDrive, Gmail and Google Drive, and Dropbox and Box represents a powerful new efficiency that simplifies and speeds up workflows, and eliminates the risk of shuttling data between on-premises off-site locations.

In short, moving to the cloud removes infrastructure costs and greatly increases efficiency and that directly impacts the bottom line.

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Total Cost of Ownership, Improved

Reducing review times, minimizing software licenses, and removing infrastructure costs are all key for a better total cost of ownership in e-discovery. Other factors—like a simple, transparent pricing model—will influence your ability to minimize TCO when upgrading your e-discovery toolset. The key is to focus on built-in artificial intelligence, a fully integrated platform, and true cloud infrastructure should ensure the total economic value of your investment is in line with your long-term business objectives.

David Carns is the Chief Strategy Officer of Casepoint LLC. He joined Casepoint as a Director of Client Services in 2010, rose the ranks to Executive Vice President until his most recent promotion in 2017. In addition to being a recovering attorney, David possesses a lifelong passion for technology and its advancements. His career has always found him at the intersection of technology and the legal field given his intimate knowledge of both.