Beyond Predictive Coding – The True Power of Analytics.

Slides:



Advertisements
Similar presentations
The new Sulzer website Internet Core Team | July 2012.
Advertisements

Analytics & Predictive Coding: Changing the Landscape for Investigations & eDiscovery Beth Patterson - Applied Legal Technology Director, Allens Jonathan.
Allvision Computing Protocol Training Day. Collection – Getting started Introduction to the marketplace Introduction to the marketplace Who are the vendors.
5 Vital Components of Every Custodian Interview David Meadows, PMP, Managing Director – Discovery Consulting, Kroll Ontrack Dave Canfield, EJD, Managing.
Project Planning and Management in E-Discovery DAVID A. ELLIS – MAYER BROWN BROWNING E. MAREAN – DLA PIPER.
The Real Cost of Privilege Review Patrick Oot, Esq. - Verizon, Director of Electronic Discovery and Senior Litigation Counsel Anne E. Kershaw, Esq. – A.
Text mining Extract from various presentations: Temis, URI-INIST-CNRS, Aster Data …
PRODUCT FOCUS 3/3/14 – 3/17/14 INTRODUCTION Our Product Focus for the next two weeks is IBM. The opportunity afforded to us in becoming an Authorized.
Data Mining Practical Machine Learning Tools and Techniques Slides for Chapter 3 of Data Mining by I. H. Witten, E. Frank and M. A. Hall.
Defensible Client File Collections 6 Common Roadblocks and Obstacles.
E-Discovery Revisited: A Broader Perspective for IR Researchers Jack G. Conrad, Thomson R&D ICAIL07 / DESI Workshop June 4, 2007.
1 CS 430 / INFO 430 Information Retrieval Lecture 8 Query Refinement: Relevance Feedback Information Filtering.
Information & Library Services SwetsWise User Guide Emma Crowley Senior Academic Services Librarian
Maximizing the Value of Your Investments With Advanced Campaign Management And Campaign Analysis Ad Campaigns.
Text Analytics And Text Mining Best of Text and Data
Get Off of My I-Cloud: Role of Technology in Construction Practice Sanjay Kurian, Esq. Trent Walton, CTO U.S. Legal Support.
Year of Innovation  Introduction  NSHMBA.org & NektPro.com  New platform “Planned Chaos” time to take action!  Internet Services Manager- Jacqui Rodriguez.
Technology-Assisted Review Solutions Predictive Coding Language-Based Analytics SM (LBA) © 2013 RenewData.
Automated Process of Electronic Discovery October 19, 2009.
Marco Nasca Senior Director, Client Solutions TRANSFORMING DISCOVERY THROUGH DATA MANAGEMENT.
Analysis 360: Blurring the line between EDA and PC Andrea Gibson, Product Director, Kroll Ontrack March 27, 2014.
Nobody’s Unpredictable Ipsos Portals. © 2009 Ipsos Agenda 2 Knowledge Manager Archway Summary Portal Definition & Benefits.
Nathan Walker building an ediscovery framework. armasv.org Objective Present an IT-centric perspective to consider when building an eDiscovery framework.
Presented to AIIM William Penn Chapter Meeting 5/13/08.
Meet and Confer Rule 26(f) of the Federal Rules of Civil Procedure states that “parties must confer as soon as practicable - and in any event at least.
Automated Process of Electronic Discovery October 4, 2010.
29-30 October, 2006, Estonia 1 IST4Balt Information analysis using social bookmarking and other tools IST4Balt Information analysis using social bookmarking.
The World’s Leading Information Provider 1 WiseEnterprise 3 Training Workshop Jun 26 th, 2015 o Basic Functions o Basic Editing o Reporting o Content Grabber.
Litigation Support Solutions in a Managed Services Environment: Where Do You Start?
Conducting Modern Investigations Analytics & Predictive Coding for Investigations & Regulatory Matters.
Using TEL’s Expanded Academic ASAP Christa Lewis IS 551 December 5, 2006.
Building an EDRM solution on the Microsoft & Tower Platform Jonny Chambers (Microsoft) & Jason Boswell (Tower Software)
Symantec Archiving & eDiscovery 1 Randy Law, Symantec Andy Becker, Trace3 Introducing the Clearwell eDiscovery Platform.
Support.ebsco.com Points of View Reference Center Tutorial.
© 2014, TERIS, Applying Analytics: Effective & Efficient Data Management in Litigation and Beyond.
Webinar: VenioOne January 20, :00AM EST. Panelists Arestotle Thapa CEO/CTO Chris Jurkiewicz Founder/VP Client Services Babs Deacon VP Training.
Chapter 8: Web Analytics, Web Mining, and Social Analytics
PATENT SEARCHES Istituto Nazionale Fisica Nucleare Rossella Osella.
Services Overview 1. Guarding your intellectual property from the greatest threats. Insight Accelerator / 2 Screen i/o is a service dedicated to identifying.
Women in Products Liability 2016 Annual Regional CLE November 3, 2016
eDiscovery Collection, Processing and Review
Background: The Big Data era
Information Organization: Overview
13 YEARS 11/2000 – 11/2013 Automated Privilege Detection, De-Threading & Automated Priv Logs 1st Quarter 2014 Confidential.
Tar Predictive Coding Simplified
6/22/2018 2:09 PM BRK3102 How Microsoft Legal drives down eDiscovery costs with machine learning in Office 365 Rachi Messing Senior Program Manager, O365.
Questioning: Foundation for an Effective Champion …
The Boolean Boost: Searching for Candidates
© 2016 Global Market Insights, Inc. USA. All Rights Reserved Text Analytics Market trends research and projections for : Global.
Speakers: Ian Campbell, Claire Hass,
The Office Is Out: Preservation And Collection In The Merry Old Land Of Office 365 June 26, v1.
9/13/2018 2:27 AM BRK3133 Quickly find what’s relevant and reduce risk with intelligent eDiscovery in Office 365 Rachi Messing Program Manager Atanu Banerjee.
Patrick Garbe, Dianne Bonnet, Ashish Prasad, Todd Haley, Elliot Beaver
Proactive steps to cut e-Discovery costs
The Team Players and Play in a Complex Document Review Project: Past, Present and Future Ralph C. Losey, Jackson Lewis Principal and National e-Discovery.
Course Lab Introduction to IBM Watson Analytics
NUR2300 – Guide to Searching ClinicalKey for Nursing
PolyAnalyst Web Report Training
e-Discovery through Text Mining
Duplication system overview
Our Workshop Our Hackathon
Overview of Business Area Strategy and Products and Services
Anatomy of a modern data-driven content product
Information Organization: Overview
The Hidden Costs of E-Discovery and Proven Strategies for Cost Cutting
ABI/INFORM Collection
Jack G. Conrad, Thomson R&D
Presentation transcript:

Beyond Predictive Coding – The True Power of Analytics

Panelists: Hal Marcus, eDiscovery Attorney working with Product Marketing team, Recommind Susan Stone, Senior Solutions Consultant, Advanced Discovery Moderator: Stephen Dooley, Senior Manager - Electronic Discovery & Litigation Support, Sullivan & Cromwell LLP Introductions

Researching your Data Clustering and Organizing Analysis QC and Redactions Categorization Overview

Researching Your Data

Search with text from anywhere: Excerpt from Wikipedia News Media Articles Documents from client Concept Searching

Concept Browsing

Phrase Analysis

Use for internal investigations, especially with fraud components Apply when keyword searches are not yielding much information Target documents of interest and then categorize to find additional documents Researching your Data - Recap

Clustering/Organizing

Clustering Review and filter by like concepts

Organizing with Smart Filters Filter and analyze. Then save into “Review Universes”

Group documents intelligently based on a range of criteria Overlay search, metadata, analytics Organize for priority treatment and/or discrete review workflows Clustering and Organizing - Recap

Analysis

Threading

“End of Branch”

Hypergraph Analysis

Reduce the volume of review substantially Ensure consistency in coding (view and/or bulk code as desired) Identify possibly missing components Analysis – Recap

QC and Redactions

Identify Textual Near Duplicates

Textual near Duplicate Compare

Consistent Coding

Smart Redactions

Global Smart Redactions

Textual Near Duplicates Identify near duplicates Review changes Provide consistent coding Smart Redactions Identify PII, PCI, PHI Redacts regular expressions Redact full phrases/sentences Use colors and reasons for QC QC and Redactions – Recap

Categorization

Coding just 5 documents able to categorize hundreds

Docs with priv search terms Docs suggested by machine learning Relevant and privileged 2nd: Use relevant priv docs to train for privilege - and find anything missed by keyword searches Total document corpus Help identify potentially privileged docs 1st: Prioritize your review of docs hitting on priv search terms

Use Clustering to identify materials provided by opposition Use categorization on documents from your review set to identify hot materials from opposition documents. Where machine learning overlaps with search and/or other analytics, you have key documents to review Categorization – Recap