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It is time to put an end to family batching, one of the most widespread document review practices in the e-discovery world. It’s also one of the worst possible workflows if you want to implement an efficient technology-assisted review (TAR) protocol.

In this on-demand webinar, our speakers bust the myths around family batching and provide new research on why it is an expensive, unnecessary review relic.

This webinar is for anyone seeking to reduce the cost of e-discovery and improve review outcomes.

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Watch this video to learn:

  • What spawned the practice of batching documents for review by family
  • Case law that questions the obligation to produce non-responsive family members
  • New research on document-level review vs. family batching
  • How to better structure your review to save time and money

To learn more, download Catalyst’s newly released third edition of TAR for Smart People.


Tom Gricks
Thomas C. Gricks III | Managing Director,Professional Services | Catalyst

A prominent e-discovery lawyer and a leading authority on the use of technology assisted review, Tom is managing director, professional services, at Catalyst. He advises corporations and law firms on best practices for applying technology to reduce the time and cost of discovery and investigations. Tom has more than 25 years’ experience as a trial lawyer and in-house counsel, most recently with the law firm Schnader Harrison Segal & Lewis, where he was a partner and chair of the E-Discovery Practice Group. He was lead e-discovery counsel in Global Aerospace v. Landow Aviation, the first case in the country to authorize the use of TAR over the objection of opposing counsel.

Tracy Drynan
Jeremy Pickens | Chief Scientist | Catalyst

Jeremy is one of the world’s leading information retrieval scientists and a pioneer in the field of collaborative exploratory search. He has seven patents and patents pending in the field of search and information retrieval. As chief scientist at Catalyst, Jeremy spearheaded the development of Insight Predict, and his ongoing research focuses on methods for continuous learning and the variety of real-world TAR workflows that are only possible with this approach. Jeremy earned his doctoral degree at the University of Massachusetts, Amherst, Center for Intelligent Information Retrieval. He conducted his post-doctoral work at King’s College, London. Before joining Catalyst, he spent five years as a research scientist at FX Palo Alto Lab, Inc. In addition to his Catalyst responsibilities, Jeremy continues to organize research workshops and speak at scientific conferences around the world.


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