After attending the many engaging and informative sessions at the 2019 Relativity Fest, it became evident to me there were two clearly developing technology trends in the industry:
Trend #1: Cloud-Based Workflows
Leveraging repository workspaces for ECA and multiple projects, cloud-based workflows are gaining prevalence. However, one of the newest and most impactful cloud-based workflows is Relativity Collect to Review. The application is built on the Relativity platform and collects data from cloud-based systems, most notably Microsoft Office 365.
This defensible, repeatable process uses Kubernetes–an MS preferred open-source container-orchestration system for automating application deployment, scaling, and management–to securely connect O365 to RelativityOne.
In conjunction with custodian questionnaires, Relativity collect facilitates legal holds, preserve to store, AND collect for review workflows. By preserving key data immediately, analysis can be performed to determine the best next-steps based on the data and the case needs. Future enhancement opportunities include collection from network locations not in office 0365, integration with mobile devices, and dropbox/box compatibility.
Bottom line: Relativity collect allows Relativity administrators to better utilize their skills to impact the speed of projects by maintaining fast, consistent workflows from collection through analysis and ultimately review (if needed).
Trend #2: Active Learning Adoption
The second recurring theme I noticed at 2019 Relativity Fest was an increased adoption of Relativity Active Learning for projects both large and small. Many firms and service providers I encountered are developing standard operating procedures for these workflows to use machine learning to get to the end goal faster. In the course of those encounters, I learned that Relativity active learning is most prevalently used for strategies including prioritized and coverage reviews, but can be leveraged for QC as well.
For prioritized review where no coding or seed documents exist, it’s best to perform a Richness estimate using a random sample of documents to identify the overall potential level of relevance among a dataset. Subject Matter experts code the random sampling and administrators use the initial results to assist in determining when the most potentially relevant documents have been reviewed in a dataset.
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