May 7th 2 pm predictive coding recommind - litigation

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May 7th 2 pm predictive coding recommind - litigation

  1. 1. Predictive Coding and The Return onInvestment (ROI) of Advanced ReviewStrategies in eDiscoveryDrew LewiseDiscovery Counsel
  2. 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. 3. THE RECOMMIND STORY• Founded 2000• San Francisco (HQ),Boston, New York,London, Sydney & Bonn• #163 in Deloitte’s 2012Technology Fast 500TM• #10 in Fast Company’s2013 Most InnovativeCompanies in Big Data
  4. 4. PREDICTIVE CODING 101
  5. 5. PREDICTIVE CODING DEFINEDPeople Case ExpertsReviewersTechnology Keyword agnostic analyticsIterative machine learningProcess Principled, Measured, and DefensibleStatistically certain results
  6. 6. PREDICTIVE CODING BASICS
  7. 7. PREDICTIVE CODING OUTPUTSIteration Total DocsComputerSuggestedPercentageSuggestedDocs ReviewedPercentageReviewedResponsiveDocsPercentageResponsive ofDocs Reviewed1 948,271 3172 0.335% 3,172 0.33% 2,063 65.04%2 948,271 1313 0.138% 1,313 0.14% 1,029 78.37%3 948,271 636 0.067% 636 0.07% 421 66.19%4 948,271 290 0.031% 290 0.03% 176 60.69%5 948,271 5039 0.531% 5,039 0.53% 4,671 92.70%6 948,271 1428 0.151% 1,428 0.15% 1,143 80.04%7 948,271 687 0.072% 687 0.07% 540 78.60%8 948,271 2270 0.239% 2,270 0.24% 1,983 87.36%
  8. 8. MARKET TRENDS
  9. 9. MASSIVE GROWTH OF UNSTRUCTUREDCONTENT05,00010,00015,00020,00025,00030,00035,00040,0002006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020ExabytesStructured Data Unstructured DataWorldwide Corporate Data GrowthSource: IDC. The Digital Universe 201080% of Data Growth is Unstructured
  10. 10. CURRENT TRENDS IN-HOUSE Regulatory/Compliance Litigation: UP Employment Litigation: UP Legal Budgets: DOWN Number one concern: Doing more withless* Three most important qualities for outsidecounsel*:˗ Responsiveness˗ Budget˗ Speed of work* Source: ALM Corporate Counsel: Agenda 2013
  11. 11. CURRENT TRENDS Majority of companies have usedpredictive coding Another 1/3 of companies consideringusing predictive coding Most use is “experimental” – less than15% “systematic” use* Source: eDJ Group’s Q1 2013 Predictive Coding Survey, February 2013
  12. 12. JUDICIAL TRENDS
  13. 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. 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. 11 2013)
  15. 15. COMMON USE CASES
  16. 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 asfew solutions when possible
  17. 17. •Check existing coding•Confirming defensibilityQuality Control•Hunt for smoking gun•Who said what and whenHot Doc Identification•Identify custom document types forspecial handling•Source code identification•Potentially personal information (PPI)Custom Use Cases•Prioritize•Minimize review population•Confirm defensibilityResponsive Review•Internal or regulatory investigation•Identify potential topics and key facts•Identify key docs in opposing counselproductionInvestigative Workflow
  18. 18. THE ROI OF PREDICTIVECODING
  19. 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. (firstpass only)$786,641.63 for first pass review
  20. 20. CASE STUDY (Cont’d) Second Review (Predictive Coding): 92% cull rate (reduced to 17 GB) 22% reviewed Approximately 32.5 decisions/hr. 1150.8 hours needed forcomplete review (less validationphase) SME rate $500/hr.$575,384.62 for Complete ReviewSAVINGS: $211,983.81
  21. 21. THE “HIDDEN” ROI
  22. 22. STRATEGIC THINKING Increased and better visibility into data set Increased speed in identification of pertinent documents Increased level of information and understanding of unrevieweddocuments Increased level of information and understanding of document contentwithout granular document review Converts reviewers into knowledge workers
  23. 23. QUESTIONS & DISCUSSION

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