Predicting Litigation Outcomes: Extracting Insights and Adding Value Using Big Data and Analytics (Recording) 

Feb. 17, 2022
Online

CBA Skilled Lawyer Series

 Predicting Litigation Outcomes: Extracting Insights and Adding Value Using Big Data and Analytics (Recording)
Thursday, February 17, 2022 | 12:00 pm–1:30 pm ET

Registration Fees

TIME EVENT

12:00 pm–1:30 pm ET

Predicting Litigation Outcomes: Extracting Insights and Adding Value Using Big Data and Analytics

In the current information age, there are copious amounts of data available to help litigators inform their strategies and to thoroughly assess risk. Knowing how to use these rich data sources to resolve disputes and staying on top of the emerging technology trends impacting litigation is absolutely essential for all litigators. Master the fundamental knowledge you need about how to use predictive analytics in litigation including:

  • What are the inherent human biases that affect a lawyer’s ability to predict possible outcomes for clients?
  • How can algorithms, and their processing powers over massive datasets, help experts predict outcomes better?
  • What are the best methods for transforming data into an asset you can leverage?
  • How can predictive coding be implemented in complex document reviews that involve an analysis of multiple issues?
  • Which analytics tools can help you investigate a judge’s case history, know your opponent better and conduct more thorough research?

 

Speaker(s)

Geoff Cowper, Fasken
Paul-Erik Veel, Lenczner Slaght
Mona Datt, Loom Analytics 

Moderator

Alexandra Mitretodis, Fasken

 


 

Registration Fees

CBA Members: $75
CBA Young Lawyer Members (<5 years): $50
CBA Student Members: $15
Non-Members: $175
Government: $115
Plus applicable taxes

Group Rates For Firms / Organizations

5 – 9 registrants = 10% discount
10 – 19 registrants = 15% discount
20+ = 20% discount
To take advantage of the discount, please contact: pd@cba.org

 

 

 

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CONTACT INFO

pd@cba.org
613-237-2925
1-800-267-8860
FAQ & Cancellation