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What’s new in Axcelerate and EnCase eDiscovery Release 16

OpenText Discovery Release 16 EP6 Introduces New AI tools

OpenText™ Discovery applications like OpenText™ Axcelerate™ and OpenText™ Insight Predict (now part of the OpenText family from the recent Catalyst acquisition) pioneered the use of AI for eDiscovery. In litigations and investigations involving millions of documents, our customers let AI do the heavy lifting and only manually review a small subset of the documents.

Now, with Release 16 EP6, Axcelerate not only suggests specific documents, it can suggest entire search categories, opening new avenues for technology-assisted investigations with new Predictive Filters.

Today we’re also announcing the EnCase team has released a new service line that combines proprietary technology and workflow to systematically identify and defensibly remediate legacy legal data held. And, the OpenText Discovery team is excited to expand the legal technology portfolio with the introduction of OpenText Insight for the Enterprise, an innovative cloud solution for dramatic efficiency gains at organizations with significant legal footprints (now available, among other new products, through the Catalyst acquisition).

A whole new way to search: Predictive Filtering in Axcelerate

OpenText Discovery users have one thing in common, they use Axcelerate, EnCase, and Insight to search for the facts across millions of documents. The new Predictive Filtering feature in Axcelerate is a game changer for fact finders – it delivers an AI assistant to suggest hidden search queries leading to potentially relevant information.

Searching for data is daunting for a human staring down a million different emails. Do I start with a date filter? Maybe a keyword? Perhaps a custodian or author? With the latest EP6 release, Axcelerate learns from human decisions and identifies the patterns that are most likely to yield results.

This new feature leverages our tried-and-true AI expertise and learns throughout the process. By default, Axcelerate will identify the most common 2-4 word phrases in the data set and sort them by occurrence. We’re using the Enron data set for this example and notice how the phrases are all generally oriented around standard business discussions for an energy company in Texas.

Default phrase analysis results

Now let’s say I want to find some documents about football. Simple enough, I can just run a standard keyword search on “football” and bulk code some documents to get us started.

Training the system with exemplars

Now, notice how the filter suggestions have changed. Phrases like “Fantasy Football,” “tight end,” “hamstring injury,” and player names like Steve McNair are being identified by the Predictive Filtering as potentially relevant. As the review project continues, the rankings will evolve and reflect the new issues that review attorneys are focusing on.

Updated phrase analysis results with predictive rankings (green numbers on right)

This is just the beginning. This functionality applies to any of Axcelerate’s Smart Filters—including all metadata fields and even customized work product like privilege tags. Plus, it’s directly integrated at no additional cost. The use cases are only limited by an investigator’s creativity. Learn more here.

EnCase reduces legacy data storage costs

Somewhere buried deep underneath a mountain, your organization is storing hundreds or perhaps thousands of boxes of hard drives. At one time, these hard drives were potential sources of evidence for a major litigation, regulatory response, or internal investigation. But how long ago was that? And how much of the data on those drives was relevant at the time? How much is still relevant today? Is any of it? For one of our first customers, an international insurance agency, the answer was about ten percent.

The EnCase Professional Services team recently went onsite at a client’s managed storage facility and sat down with hundreds of boxes full of hard drives that were on legal hold. Prepared with a litigation schedule from the GC, the EnCase team reviewed, labeled, organized, categorized, and ultimately disposed of thousands of hard drives. In theory, these drives were at one time relevant to potential or actual lawsuits. But most were no longer relevant, and the organization had no obligation to continue to preserve them. The engagement ROI was justified in reduced managed storage facility fees alone. Learn more here.

A look forward at EP7 and beyond

In the past year, the OpenText Discovery team has released several significant innovations like review-in-context, Predictive Research, and now Predictive Filtering. In addition, the product management team continually releases under-the-hood improvements to improve stability and efficiency across the suite. Beyond that, EP6 contains major code improvements that will lay the foundation for dramatic enhancements in EP7 later this year like Magellan sentiment and text mining integrations, machine translation APIs, native Excel redactions, and more. Learn more here.

Learn more:

Discover all the latest innovations in OpenText Discovery solutions here, and read more about OpenText Release 16, OpenText Cloud and OpenText OT2 here.

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Adam Kuhn

Adam is an eDiscovery attorney and the Director of Product Marketing at OpenText Discovery. He holds an advanced certification for the Axcelerate eDiscovery platform and is responsible for research, education and outreach programs. Adam also serves as a Senior Research Fellow at the McCarthy Institute for IP & Technology Law at the University of San Francisco School of Law.

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