SFL Data Launches PredictiveReview & CategorizedReview to Save Law Firms, Corporations Up to 50% on Litigation
Suite of Services Enables Faster Document Decisions per Hour to Minimize Number One e-Discovery Cost Multiplier: Attorney Review
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San Francisco, Calif. – August 22, 2011 – SFL Data, the first provider of a fixed-price e-discovery managed service for corporations and law firms, has launched the PredictiveReview™ & CategorizedReview™ service to help law firms and corporations improve individual reviewers’ productivity and minimize duplicate document review. Using customized review workflows from both kCura Relativity® and Symantec Clearwell®, PredictiveReview & CategorizedReview threads and categorizes electronically stored information (ESI) to identify content groupings. This enables attorneys and other reviewers to make faster document decisions per hour, resulting in an average e-discovery cost savings of 50 percent. PredictiveReview & CategorizedReview uses proprietary methods such as:
- Review Workflow Setup – Analysis and development of discovery requirements and scope of review
- Analysis Tools – Provision of software tools needed to most efficiently analyze ESI
- Relativity Training – Training to boost effectiveness and accuracy of Relativity
- Conceptual Search – Organization of similar ESI in one queue to increase reviewer efficiency by 300-400%
- Near-Duplicates and Grouping – Prevention of duplicate document review to stay on deadline
“The leading cost multiplier in e-discovery is attorney review – especially when irrelevant or duplicate data is analyzed multiple times. PredictiveReview & CategorizedReview is a compilation of the best practices we’ve learned while reviewing multiple terabytes of data. Most recently, we engaged nearly 500 reviewers on two simultaneous matters using a large-scale, topic-based batching system. Our track record shows we’re well equipped to handle this high level of complexity and reduce review costs in the process,” said SFL Data CEO Christian Lawrence.
According to Gartner, approximately 70% of enterprise data is duplicate and hasn’t been accessed in over 90 days. This makes it challenging for corporations and law firms to collect and review relevant ESI internally. Furthermore, Network Computing concluded that “the earlier in a case that review and analysis can produce workable results, the more cost-effective litigation e-discovery becomes.”
SFL Data is currently accepting meeting appointments for The International Legal Technology Association (ILTA) event from August 21-25 in Nashville, Tenn. While in Booth 112, SFL Data will introduce four e-discovery expert solutions in addition to PredictiveReview & CategorizedReview: LitAudit™ custom litigation-readiness assessment to gauge gaps in an e-discovery plan and implement action to improve the process; a SmartSetup™ service designed to manage the first phase of a company’s e-discovery process to ensure defensibility while maximizing efficiency; RemoteCollect™, a cost-effective method for enabling collection from geographically dispersed custodians with forensically prepared hard drives; and Defensible SelfCollect™, a method to train IT staff or custodians to perform collections.
About SFL Data
SFL Data is the first e-discovery service provider to deliver a fixed-price managed service that enables Fortune 500 corporate legal departments and law firms to gain a world class e-discovery function without building it internally. Using an e-discovery managed service can reduce litigation costs by more than 50 percent and achieve better control, visibility, and defensible results. SFL Data’s clients include Oracle, T-Mobile, and Yahoo!, as well as more than 100 AmLaw 250 law firms. Founded in 1998, SFL Data is a privately held company based in San Francisco. More information is available at 415-392-2900 or at www.sfldata.com.
PredictiveReview™ & CategorizedReview™, LitAudit™, SmartSetup™, RemoteCollect™ and DefensibleSelfCollect™ are trademarks of SFL Data. All other registrered trademarks are the property of their respective owners.