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EMPLOYING NEW REVIEW
STRATEGIES & TECHNOLOGIES
TO BETTER MANAGE THE COST
AND RISK OF eDISCOVERY
Our Panelists

• Jason R. Baron, Director of Litigation –
  National Archives

• Mark Cordover, Founder & CEO – IT.com

• Jonathan Redgrave, Partner – Redgrave LLP
Goals of this Discussion

• Demystify Computer Assisted Review

• Deliver a Practical Framework for Leveraging
  These Tools

• Share Real-World Examples of Where New
  Review Tools are Delivering Value
Response to NY Times Article
Today’s eDiscovery Landscape

•   History of Search
•   Clustering
•   Supervised Learning
•   Predictive Coding
•   Unsupervised Learning
•   Auto-Categorization
Barriers & Opportunities

• Barriers:
   – Traditional law firm economics & dynamics
   – Fear of unknown


• Opportunities:
   – FRE 502
   – Reduced costs & increased accuracy
Key Considerations

• How to design an optimal process that produces
  quality results?

• Are there ways to regularize or standardize that
  process in order to “certify” the results?
What Search Methodologies are
Right for What Projects?
• Need to understand the various types of search
  methods and how those search methods work
  on different data types

• Scale of the data set

• What you want to get out of the data set
Defensibility of Technology

• Focus on results – not the “math”




• “Transparent” or “Translucent”

• Comparison of human results vs. technology
Judicial Response
TREC 2009 Results
• Average efficiency and effectiveness of the five
  technology-assisted reviews surpasses that of the
  five manual reviews
• Technology-assisted reviews require, on average,
  human review of only 2.1% of the documents, a
  nearly fifty-fold savings over exhaustive manual
  review
• There was not one single measure for which manual
  review was significantly better than technology-
  assisted review
Maura R. Grossman, Wachtell, Lipton, Rosen & Katz & Gordon V.
Cormack, University of Waterloo: “Technology-Assisted Review in
E-Discovery Can Be More Effective and More Efficient Than
Exhaustive Manual Review,” Richmond J. of Law and Tech. (2011)
(Vol. 17 forthcoming)

 “The myth that exhaustive manual review is the most effective – and
 therefore, the most defensible – approach to document review is
 strongly refuted. Technology-assisted review can (and does) yield more
 accurate results than exhaustive manual review, with much lower effort.
 Of course, not all technology- assisted reviews (and not all manual
 reviews) are created equal. The particular processes found to be superior
 in this study are both interactive, employing a combination of computer
 and human input. While these processes require the review of orders of
 magnitude fewer documents than exhaustive manual review, neither
 entails the naïve application of technology absent human judgment.”
Real World Examples

• Reduce reviewer
  time by 25 – 60%

• Reduce review
  costs by more than
  $2 million




                       17
Redgrave LLP New Review Strategies - eDiscovery

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Redgrave LLP New Review Strategies - eDiscovery

  • 1. EMPLOYING NEW REVIEW STRATEGIES & TECHNOLOGIES TO BETTER MANAGE THE COST AND RISK OF eDISCOVERY
  • 2. Our Panelists • Jason R. Baron, Director of Litigation – National Archives • Mark Cordover, Founder & CEO – IT.com • Jonathan Redgrave, Partner – Redgrave LLP
  • 3. Goals of this Discussion • Demystify Computer Assisted Review • Deliver a Practical Framework for Leveraging These Tools • Share Real-World Examples of Where New Review Tools are Delivering Value
  • 4. Response to NY Times Article
  • 5. Today’s eDiscovery Landscape • History of Search • Clustering • Supervised Learning • Predictive Coding • Unsupervised Learning • Auto-Categorization
  • 6.
  • 7.
  • 8.
  • 9.
  • 10. Barriers & Opportunities • Barriers: – Traditional law firm economics & dynamics – Fear of unknown • Opportunities: – FRE 502 – Reduced costs & increased accuracy
  • 11. Key Considerations • How to design an optimal process that produces quality results? • Are there ways to regularize or standardize that process in order to “certify” the results?
  • 12. What Search Methodologies are Right for What Projects? • Need to understand the various types of search methods and how those search methods work on different data types • Scale of the data set • What you want to get out of the data set
  • 13. Defensibility of Technology • Focus on results – not the “math” • “Transparent” or “Translucent” • Comparison of human results vs. technology
  • 15. TREC 2009 Results • Average efficiency and effectiveness of the five technology-assisted reviews surpasses that of the five manual reviews • Technology-assisted reviews require, on average, human review of only 2.1% of the documents, a nearly fifty-fold savings over exhaustive manual review • There was not one single measure for which manual review was significantly better than technology- assisted review
  • 16. Maura R. Grossman, Wachtell, Lipton, Rosen & Katz & Gordon V. Cormack, University of Waterloo: “Technology-Assisted Review in E-Discovery Can Be More Effective and More Efficient Than Exhaustive Manual Review,” Richmond J. of Law and Tech. (2011) (Vol. 17 forthcoming) “The myth that exhaustive manual review is the most effective – and therefore, the most defensible – approach to document review is strongly refuted. Technology-assisted review can (and does) yield more accurate results than exhaustive manual review, with much lower effort. Of course, not all technology- assisted reviews (and not all manual reviews) are created equal. The particular processes found to be superior in this study are both interactive, employing a combination of computer and human input. While these processes require the review of orders of magnitude fewer documents than exhaustive manual review, neither entails the naïve application of technology absent human judgment.”
  • 17. Real World Examples • Reduce reviewer time by 25 – 60% • Reduce review costs by more than $2 million 17