Analytics & Predictive Coding:Changing the Landscape for Investigations & eDiscovery

Beth Patterson - Applied Legal Technology Director, AllensJonathan Wong - Associate Freshfields Bruckhaus Deringer

Akiko Miyake – Director, FTI

3 March 2014

Overview

• Why Analytics & Predictive Coding Matter

• Defining Analytics & Predictive Coding

• What is the uptake: Asia? Worldwide?

• Deciding on what to use & why: It’s not either/or

• Case Studies

• Key Takeaways

Why Analytics & Predictive Coding Matter

Disruptive Technologies: Advances that will transform life, business, and the global economy McKinsey Global Institute May 2013

Why Analytics & Predictive Coding Matter

Disruptive Technologies: Advances that will transform life, business, and the global economy McKinsey Global Institute May 2013

Defining eDiscovery Analytics

• Analyze information: how are documents distributed based on custodians, date ranges, keywords and other critical factors

• Concept clustering: visualizations of large data sets using concepts

• Review prioritization: prioritization of specific documents

• Reporting: monitor review across a variety of metrics, real-time reporting

Defining Predictive Coding

“E-discovery Taking Predictive Coding Out of the Black Box”, FTI Journal, Nov 2012

Predictive Coding eDJ Group 2013 Asian eDiscovery Survey

Predictive Coding eDJ Group 2013 Asian eDiscovery Survey Results

Predictive Coding eDJ Group 2013 Asian eDiscovery Survey Results

Discussion & Case Studies

Case Study Analytics Review Prioritisation

90-100 80-90 70-80 60-70 50-60 40-50 30-40 20-30 10-20 0-100%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Proportion of Relevant/Non-Relevant Documents in each Con-fidence Level

Non Relevant Relevant

Analytics Confidence Level

Prop

ortio

n of

Doc

umen

ts

Key Takeaways

Akiko Miyake – Director, FTI

Jonathan Wong - Associate Freshfields Bruckhaus Deringer

Beth Patterson - Applied Legal Technology Director, Allens