Technology-Assisted Review Solutions Predictive Coding Language-Based Analytics SM (LBA) © 2013 RenewData

2 Where TAR Fits in the EDRM

A Major Challenge All Clients Face © 2013 RenewData 3 Now – with Data exploding, Discovery has become a Data problem. In the Past – Discovery was viewed mainly as a Legal problem…

For every 5 documents reviewed… © 2013 RenewData 4 The Challenges of Document Review … 4 usually have nothing to do with your matter 5X Cost Time

© 2013 RenewData 5 The Challenges of Document Review * Resnik et al Reviewers understand issues differently They interpret and code documents differently QC practices are too weak to provide good feedback It is too expensive to go back and fix errors Review Accuracy Less than * 50% How do you identify and correct errors before it is too late?

© 2013 RenewData 6 The Challenges of Document Review Knowledge gained by reviewers stays with the reviewers… Case team has to read documents again to understand key facts… Takes too long before issues are fully understood… Takes too long to understand how players communicate… How do you gain the full benefit of your review team’s work?

Defining terms: “TAR” and“CAR” are synonymous What is it? – TAR is the process of leveraging both technology and human intelligence to rapidly identify relevant documents in a large data set Objective? – To reduce costs and give the legal team the ability to hone in on important documents more quickly All TAR are not the same (processes or results) © 2013 RenewData 7 Technology-Assisted Review (TAR)

Types of TAR Predictive Coding Language-Based Analytics SM (LBA) © 2013 RenewData 8

Uses pattern matching to “predict” if relevant documents are present Need to “train” software to look for relevant documents Create a “seed set” that trains the “AI” (artificial intelligence) in the algorithms in the software “More like” the “seed set” Iterative process Success is highly dependent on skill of the trainer Wide variance in # of documents to train a seed set © 2013 RenewData 9 Predictive Coding

Linguistic approach using human understanding of language, context, synonyms, and logical expressions Coupled with the power of the computer to apply rules consistently without error (N.B.: Contrast this with what happens in “Traditional Linear Review” where human reviewers are expected to apply the rules consistently without error or misinterpretation) © 2013 RenewData 10 Language-Based Analytics SM (LBA)

2 Tools: – One for Processing – One for Review © 2013 RenewData 11 Language-Based Analytics SM (LBA)

Surgical – Ability to give specific instructions and input to find exactly what you want – Access to language experts to create queries that leverage actual language in documents Accurate and Defensible – Statistical analysis of results to ensure high level of accuracy and defensibility – Systematic approach easily defensible (can be printed) – Zero Defect Test against the discard pile Reusable asset if more documents are produced © 2013 RenewData 12 LBA for “Content Filtering”

Easy – Simple highlighter function for reviewers to use Fast – Multiple documents tagged instantaneously Management – Monitor reviewer highlights in real time – Make targeted corrections immediately across all data with a simple mouse click – Quickly provide feedback to reviewers Reusable asset if more documents are produced © 2013 RenewData 13 LBA for “Review Acceleration”

© 2013 RenewData 14 Results of LBA “Review Acceleration” 150,000 Documents; 15% of documents relevant; Average of 24 hits per highlight 3 Reviewers; 350 Docs per day Fast access to relevant documentsSignificant time and cost saved (Typically over 80%) LBA “Review Acceleration” Traditional Linear Review

Better insight for case team Significantly faster Better review quality Simple – highlighter feature Accuracy assured – no risk Defensible – no “black box” Over 80% cost savings © 2013 RenewData 15 LBA Review Acceleration – in a nutshell reap significant benefits with a small change in your workflow with no added risk

© 2013 RenewData 16 Combining Both LBA Tools Using LBA “Content Filtering” Accelerates Review Even More LBA “Review Acceleration” Traditional Linear Review

Da Silva Moore v. Publicis Groupe, 11 Civ (ALC)(AJP)(S.D.N.Y. Feb. 24, 2012) U.S. Magistrate Judge Andrew J. Peck did not endorse any particular TAR method “What the Bar should take away from this Opinion is that computer-assisted review is an available tool and should be seriously considered for use in large-data- volume cases….” “Computer-assisted review now can be considered judicially-approved for use in appropriate cases.” © 2013 RenewData 17 Court Acceptance of TAR

Questions and Further Discussions © 2013 RenewData 18

© 2013 RenewData 19 Evolution of Solutions Black and White -> Higher Confidence “Fuzzy” -> Lower Confidence Keyword Filtering Near Duplicates Threading Predictive Coding Language Based Analytics SM

Kelvin H. Chin Consulting Director RenewData 9500 Arboretum Blvd Suite L2-120 Austin, TX (512) © 2013 RenewData 20 CONTACT INFO:

Kelvin H. Chin is Consulting Director at RenewData. In this role, Chin consults with clients on eDiscovery best practices and appropriate forms of review acceleration. He provides guidance on effective ways to manage costs associated with document review, without compromising on defensibility. He also works closely with clients to develop tailored solutions to meet their unique data archiving needs related to compliance monitoring, data retention management, and legal holds. Chin has more than 20 years of strategic, leadership, management, and client services experience in legal services and consulting firms, and has managed eDiscovery and document review for large litigations and government investigations. He has recruited, trained and led teams to manage consultative, client-driven service relationships with CEOs, CFOs, and GCs at Fortune 500 and middle market companies. He was previously with the AmLaw100 firms Womble Carlyle Sandridge & Rice LLP and Edwards Angell Palmer & Dodge LLP, as well as the litigation firm Theodora Oringher PC, where he was chief client development officer. He was a regional vice president for a decade with the American Arbitration Association, a corporate associate at Choate Hall & Stewart LLP, a FINRA mediator/arbitrator for the financial services industry, and co-founder of a technology start up. Chin is a frequent presenter to national and state bar associations, international and national industry and trade groups, universities, and business and law schools, and is a National Advisory Council member of Asian Americans Advancing Justice. He is a graduate of Dartmouth College, Yale Graduate School, and Boston College Law School. © 2013 RenewData 21 Kelvin H. Chin