Manual Review is the process of human reviewers painstakingly reviewing documents collected as part...
The 2020 toolkit: Tech that will upgrade your eDiscovery efficiency
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Dramatically streamline your litigation operations and unlock new, company-wide efficiencies with these advanced eDiscovery tools and systems.
As we roll into 2020, the landscape of litigation looks set to go on as it has done unapologetically for the last fewyears, continuing to burgeon with an abundance of data in new and ever-changing formats. Over time as the market continues to grow in size, scale and complexity, it is safe to say that having access to a suite of sophisticated tools and systems will be practically essential to managing and dealing with any eDiscovery project…as linear approaches will be rendered obsolete.
But that day is yet to arrive. So for now,
market-leading eDiscovery tools and technology platforms can instead offer Associates and other key legal team decision makers the opportunity to dramatically streamline their operations, enabling them to do more in the hours they have, and deliver a level of client satisfaction that simply can’t be matched by the firm next door.
Without further ado, let’s look at the best tech features, systems and tools to have in your arsenal for 2020 if you want to unlock new operational efficiencies and deliver a higher-value service to your clients more swiftly than ever before.
Active Learning TAR
TAR (or Technology Assisted Review) is the linchpin of tech-enabled efficiency gains for discovery, automating and dramatically streamlining the formerly resource-heavy document review process.
While the initial iteration of TAR or predictive coding provided game-changing impact in terms of reducing the time and internal resource needed to review documents, it still demanded a base level of man hours and technical expertise from someone to program it and create training sets that allowed the technology to do its job effectively.
Active Learning TAR (or TAR 2.0) brings greater automated efficiency than its predecessor with the additional benefits of
machine learning and AI, meaning little to no administrative support is needed for set up, control and maintenance.
As more document coding decisions are made, the system will use them to refine its own understanding of what is relevant, growing faster and more effective as the project goes on.
The exponential gains in effectiveness and efficiency offered by Active Learning TAR can prove a big helping hand in the face of vast volumes of unstructured data and tight deadlines.
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