Proofpoint has introduced an eDiscovery Analytics platform to help organizations quickly find, identify and review documents to meet regulatory and legal requirements. Proofpoint eDiscovery Analytics™ will provide powerful search, rich data visualizations and predictive coding capabilities to strengthen the company’s offering across the Electronic Discovery Reference Model. “eDiscovery review can cost an average of $5 million per year[1] if information is inefficiently managed,” said Darren Lee, senior vice president of Archiving and Governance for Proofpoint. “Costs climb due to a lack of information management combined with an exponential increase in data from a variety of sources, time spent searching for relevant content and required legal review. Proofpoint eDiscovery Analytics™ empowers legal teams to conduct first pass reviews in-house, reducing the result sets exported to a third party to save significant costs.” Integrated with Proofpoint Enterprise Archive™, Proofpoint eDiscovery Analytics™ will provide comprehensive collection, review and export capabilities from a single platform. Built with an architecture explicitly designed to leverage the cloud, Proofpoint Enterprise Archive preserves, discovers and supervises business critical information. Customers are offered guaranteed search performance, unmatched customer satisfaction and the industry’s most sophisticated encryption for complete legal and compliance control.
New Proofpoint eDiscovery Analytics functionality includes:
- Advanced Search: New concept search identifies documents based on context, returning results commonly missed with standard keyword searches. Big data techniques returns results in seconds regardless of data volume.
- Data Visualizations: Organizations can more easily identify key custodians, track the path of discussions and view a timeline detailing when communications took place. Administrators can also quickly identify relevant patterns in the document set, including possible outliers.
- Streamlined Review: Document tagging and conversation threading optimize the review process, while predictive coding leverages machine learning to increase accuracy and cut legal review costs.
