Predictive Coding and The Return on Investment (ROI) of Advanced Review Strategies in ediscovery
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1 Predictive Coding and The Return on Investment (ROI) of Advanced Review Strategies in ediscovery Drew Lewis ediscovery Counsel
2 AGENDA A Predictive Coding Primer Predictive Coding and Market Trends Predictive Coding in Court Selecting the Right Technology for the Job: Common Use Cases The ROI of Predictive Coding The Hidden ROI: Strategic Advantages of Technology
3 THE RECOMMIND STORY Founded 2000 San Francisco (HQ), Boston, New York, London, Sydney & Bonn #163 in Deloitte s 2012 Technology Fast 500 TM #10 in Fast Company s 2013 Most Innovative Companies in Big Data
4 PREDICTIVE CODING 101
5 PREDICTIVE CODING DEFINED People Technology Process Case Experts Reviewers Keyword agnostic analytics Iterative machine learning Principled, Measured, and Defensible Statistically certain results
6 PREDICTIVE CODING BASICS
7 PREDICTIVE CODING OUTPUTS Iteration Total Docs Computer Suggested Percentage Suggested Docs Reviewed Percentage Reviewed Responsive Docs Percentage Responsive of Docs Reviewed 1 948, % 3, % 2, % 2 948, % 1, % 1, % 3 948, % % % 4 948, % % % 5 948, % 5, % 4, % 6 948, % 1, % 1, % 7 948, % % % 8 948, % 2, % 1, %
8 MARKET TRENDS
9 Exabytes MASSIVE GROWTH OF UNSTRUCTURED CONTENT 40,000 35,000 30,000 Worldwide Corporate Data Growth 80% of Data Growth is Unstructured 25,000 20,000 15,000 10,000 5, Structured Data Unstructured Data Source: IDC. The Digital Universe 2010
10 CURRENT TRENDS IN-HOUSE Regulatory/Compliance Litigation: UP Employment Litigation: UP Legal Budgets: DOWN Number one concern: Doing more with less* Three most important qualities for outside counsel*: Responsiveness Budget Speed of work * Source: ALM Corporate Counsel: Agenda 2013
11 CURRENT TRENDS Majority of companies have used predictive coding Another 1/3 of companies considering using predictive coding Most use is experimental less than 15% systematic use * Source: edj Group s Q Predictive Coding Survey, February 2013
12 JUDICIAL TRENDS
13 PREDICTIVE CODING S HOLY TRINITY In re Actos (Pioglitazone)Products Liability Litigation (W.D. La. July 27, 2012) Da Silva Moore v. Publicas Groupe SA, (S.D.N.Y. Feb. 24, 2012) Kleen Products LLC v. Packaging Corp. of Am. (N.D. III. Sept. 28, 2012)
14 PREDICTIVE CODING GAINING GROUND EORHB, Inc., et al. v HOA Holdings, Inc., et. al. (Del. Ch. Ct. Oct 15, 2012) Robocast, Inc. v. Apple, Inc. (D. Del. 2012) Chevron Corp v. Donnziger (S.D.N.Y. Mar. 15, 2013) Harris v. Subcontracting Concepts, LLC (S.D.N.Y. Mar )
15 COMMON USE CASES
16 THE RIGHT TECHNOLOGY FOR THE JOB What is the problem you need to solve? Expenses Efficiency Understand of data Confidential data Technology procurement should account for as many problems with as few solutions when possible
17 Quality Control Responsive Review Prioritize Minimize review population Confirm defensibility Investigative Workflow Internal or regulatory investigation Identify potential topics and key facts Identify key docs in opposing counsel production Check existing coding Confirming defensibility Hot Doc Identification Hunt for smoking gun Who said what and when Custom Use Cases Identify custom document types for special handling Source code identification Potentially personal information (PPI)
18 THE ROI OF PREDICTIVE CODING
19 CASE STUDY Initial Review (linear): 33% cull rate (reduced to 68 GB) 679,349 documents for review Approximately 47.5 decisions/hr. 14,302.1 hours needed for review Contractor rate of $55/hr. (first pass only) $786, for first pass review
20 CASE STUDY (Cont d) Second Review (Predictive Coding): 92% cull rate (reduced to 17 GB) 22% reviewed Approximately 32.5 decisions/hr hours needed for complete review (less validation phase) SME rate $500/hr. $575, for Complete Review SAVINGS: $211,983.81
21 THE HIDDEN ROI
22 STRATEGIC THINKING Increased and better visibility into data set Increased speed in identification of pertinent documents Increased level of information and understanding of unreviewed documents Increased level of information and understanding of document content without granular document review Converts reviewers into knowledge workers
23 QUESTIONS & DISCUSSION
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