How to choose the best eDiscovery software in 2018

Three out of four corporate legal operations professionals say their entire legal departments would benefit from reducing their eDiscovery costs, according to the OpenText Corporate…

Adam Kuhn profile picture
Adam Kuhn

June 5, 20186 minute read

Three out of four corporate legal operations professionals say their entire legal departments would benefit from reducing their eDiscovery costs, according to the OpenText Corporate Legal Ops Survey. Around half of the respondents said they have either consolidated their eDiscovery solutions or are in the process of doing so. In addition, many enterprises are looking to in-source eDiscovery software to conquer litigation and due diligence challenges. But not all litigation profiles are the same and each legal department must evaluate what platforms will best serve their needs. So, what should you look for in the best eDiscovery software?

What is eDiscovery?

First, the basics. Gartner defines electronic discovery (“eDiscovery”) as “the identification, collection, preservation, processing, review, analysis and production of electronically stored information (ESI) to meet the mandates imposed by common-law requirements for discovery.” Historically, we’d do this manually—a lot of lawyers reading a lot of documents, linearly. In the big data era, that’s not much of an option, so most organizations use eDiscovery tools.

Contemporary eDiscovery scenarios frequently involve both structured (e.g., databases) and unstructured information (e.g., emails). These collections may be dozens, hundreds, or thousands of gigabytes, all of which must be analyzed with only a small volume (perhaps just a couple of megabytes) produced. It’s a lot like looking for a needle in a stack of needles. Data mining these sources with text and content analytics is therefore key to success. It also means that machine learning and predictive analytics software are crucial tools in the eDiscovery toolbox.

The first published court opinion to endorse the use of AI for eDiscovery was issued in 2012, and since then technology-assisted review (TAR) has become an important element of eDiscovery processes.

Finally, the responsive data is screened for privilege, redacted, and produced to the court, opposing counsel, regulatory agency, or board of directors as appropriate. This process can be streamlined with automated redaction technology that identifies patterns like credit card or social security numbers. Productions are often complex and high-stakes, which makes it important to have customizable workflows that look for potential mistakes before documents are packaged up and sent outside.

Reporting is generally required throughout the entire eDiscovery process. Key metrics on the eDiscovery process—for instance how much data was collected, culled and produced—can substantiate legal arguments around document productions. Other metrics on the process—for instance how many documents reviewed were ultimately relevant—can help optimize business workflows and standards for the next case.

eDiscovery features

Some (but certainly not all) of the key aspects of the best eDiscovery systems you should consider include:

  • Robust search capabilities
    The most fundamental function in eDiscovery is search (The California State Bar even makes search skills an ethical obligation) and any eDiscovery tool must have a powerful search query editor that can accommodate multiple lines of Boolean logic. The best eDiscovery solutions also include more sophisticated search technologies like the ability to identify keywords in context with phrase analysis and natural language concepts.
  • Leverage metadata
    Inclusive and exclusive ESI filtering on a full range of metadata variables—including system location, custodian, access time, size, and file type—is an absolute must. And the best eDiscovery software will go the extra step by visualizing metadata to help identify patterns and anomalies across large datasets.
eDiscovery Communication Patterns Hypergraph - OpenText
OpenText Axcelerate visualizes communication patterns by domain or sender/recipient in the hypergraph.
  • Machine learning tools
    Supervised and unsupervised machine learning algorithms should both be included in your eDiscovery tool. Concept groups automatically organize content into related clusters with topic labels, enabling analysts to get a big-picture view of their data landscape and target a specific theme of interest for priority analysis. Predictive coding learns from human relevancy decisions and builds a data model to “find more like this.” Savvy eDiscovery professionals use this technology on every project, at least as a form of quality control to identify anomalies where human and machine disagree.
  • Integration with existing systems
    Discovery projects involve data from multiple sources. A lawsuit request for production can include emails, chat, fax, and many other file formats. The best eDiscovery solutions will connect to the most common document stores—like ECM and email servers—to streamline the process of collecting data for legal purposes.
  • Minimize data collection’s impact on staff
    In the “old days,” organizations had to physically take an employee’s laptop or phone to collect evidence. This not only disrupts work, but also tips the corporate investigators hand. Your eDiscovery solution should have as little impact on your employees’ normal working day as possible. It should be able to collect data from a target remotely, without disruption as endpoints log-in and out of the network.
  • Rolling data loads
    Even while review is underway, your eDiscovery solution must be able to incorporate additional information without disrupting project progress. Investigations are fluid and dynamic, the best eDiscovery tools are able to seamlessly roll new data into an active project without disrupting the machine learning process.
  • Defensible data
    It’s essential to preserve the integrity of existing data, so your eDiscovery software shouldn’t alter document properties when copying or moving it. If ESI is modified it not only risks legal penalties but also calls the entire process into doubt. A forensically sound collections process is a critical component of your eDiscovery solution. For many organizations, this means using a trusted format that has been examined by courts already, like the LEF.
  • Audit trails and reporting
    Your chosen eDiscovery software should provide comprehensive audit logs showing where data originally resided, what search terms were applied to collect it, and how it was handled. During the review, it’s also necessary to maintain records of decisions and review workflows. Not only for accountability and transparency, but for process optimization as well.
  • Comprehensive eDiscovery capabilities
    Not every organization needs the same grade of litigation support software. Some organizations only need a forensic collection and processing application. Others need a full spectrum solution with redaction and production capabilities. But regardless of the process you are looking to solve, you should choose an eDiscovery solution that integrates a comprehensive set of components to cover each function with as few plugins and third-party reliance as possible.

Why choose OpenText for eDiscovery?

OpenText is a global, tier-one eDiscovery vendor, offering a suite of solutions—including text and content analytics, data mining software and litigation support software—to achieve litigation readiness through a comprehensive records, retention, disposition, and legal hold management strategy. Our solutions cover the entirety of the eDiscovery industry’s Electronic Discovery Reference Model (EDRM), starting with information governance and enterprise content management components through eDiscovery. New OpenText AI solutions bring even more functionality to the post-discovery debrief phase, enabling legal teams to analyze a project’s success.

OpenText Discovery Suite is an enterprise eDiscovery platform that offers industry gold standard forensics and unstructured data analytics for searching, collecting, and investigating enterprise data to manage legal obligations and risk.

Share this post

Share this post to x. Share to linkedin. Mail to
Adam Kuhn avatar image

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.

See all posts

More from the author

What’s new in Axcelerate, EnCase eDiscovery, and Decisiv Release 16 EP7

What’s new in Axcelerate, EnCase eDiscovery, and Decisiv Release 16 EP7

Automation, analytics, and accessibility are the key themes around the EP7 updates for OpenText™ Axcelerate™, OpenText™ EnCase™ eDiscovery and OpenText™ Decisiv™. New automation templates reduce…

5 minute read

LegalTech is no longer optional

LegalTech is no longer optional

“In the post-digital world, every company is an information company.” Mark Barrenechea’s quote at OpenText™ Enterprise World in Toronto ought to be a wake-up call…

3 minute read

What’s new in Axcelerate and EnCase eDiscovery Release 16

What’s new in Axcelerate and EnCase eDiscovery Release 16

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…

4 minute read

Stay in the loop!

Get our most popular content delivered monthly to your inbox.