ACEDS-UBIC 2-19-14 Big Data Webcast


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E-discovery webcast on streamlining information governance to reduce risks and costs of big data.

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  • Hello and welcome to this ACEDS webcast, “Big Data Challenges in the Legal Profession,” presented by UBIC. I’m your host Robert Hilson of and I’m joined today by two great presenters who I’ll introduce in a moment. But first, as always, I have a couple of quick announcements.
  • For those of you who aren’t familiar with us, We are a members-only association that provides training, resources, and networking to the e-discovery community. We also offer the Certified E-Discovery Specialist credential, which is held by hundreds of professional across the Americas, Europe and Asia. You can join today and start receiving a number of benefits exclusive to our members, including news content, on-demand web seminars, our bits+bytes newsletter and discounts on certification and our annual e-discovery conference in April.
  • Speaking of… The ACEDS 2014 E-Discovery Conference and Exhibition will be held at the beautiful Westin Diplomat in Hollywood, Florida. The conference features 13 panels, 35 top speakers and beachside networking events. Among the presenters is Steven Levy, the former head of legal operations at Microsoft and a pioneer of legal project management, and Judge David Waxse, who has authored several groundbreaking e-discovery decisions. Two federal judges from the US Court in the Southern District of Florida will also speak, along with the head of eDiscovery at Siemens, a member of the federal rules advisory committee, the E-Discovery Director at UnitedHealth Group and many more more. You can register or get more information at And, as a thank you for being here, if you enter the code BIGDATA2014, you will receive 20% off. Again that’s BIGDATA2014.
  • As I said, I’m joined today by two great experts. Elle Pyle is discovery counsel at McDermott Will & Emery in Washington, DC where she focuses her practice on e-discovery, data privacy and information issues. With a track record of successes in government investigations and litigation focusing on tax evasion, securities, bank fraud and white collar crime, she has been called on to prepare responses to search warrants, subpoenas, and grand jury investigations, and prepare witnesses, produce records and prepare settlement agreements. Prior to joining McDermott, she worked for another AmLaw 100 firm on a variety of e-discovery matters related to antitrust and white-collar criminal defense. Elle is a pleasure to have you here. Thanks for joining us.Elle is joined by Paul Starrett, Chief Global Risk Officer at UBIC, North America. Paul leads UBIC’s global legal, operations and risk management groups. An attorney in California and licensed private investigator, he has attained the certifications of Certified Fraud Examinerand EnCase Certified Computer Forensics Examiner. Paul’s twenty-five year career includes four years in law enforcement, twelve years in corporate security and five years in information-security engineering. More recent experience includes four years as an electronic discovery project manager with Iron Mountain where he managed every aspect of very large cases for AMLAW 100 firms and Fortune 500 companies. Paul, thanks for being here. Good to have you back…. Okay, just a quick reminder to our audience before we begin. If you have questions, please type them in the chat box to the right of your screen and we will get to them if time allows.
  • ACEDS-UBIC 2-19-14 Big Data Webcast

    1. 1. Big Data Challenges in the Legal Profession Paul Starrett, Esq. Counsel and Chief Global Risk Officer UBIC North America, Inc. Elle Pyle, Esq. Discovery Counsel McDermott, Will and Emery, LLC
    2. 2. ACEDS Membership Benefits Training, Resources and Networking for the E-Discovery Community Exclusive News and Analysis Weekly Web Seminars Podcasts On-Demand Training Networking Resources Jobs Board & Career Center bits + bytes Newsletter CEDS Certification And Much More! “ACEDS provides an excellent, much needed forum… to train, network and stay current on critical information.” Kimarie Stratos, General Counsel, Memorial Health Systems, Ft. Lauderdale Join Today! or Call ACEDS Member Services 786-517-2701
    3. 3. 13 panels, 35 speakers, 14 networking events Federal Rules amendments, judicial guidance, legal holds & more! Presenters include: Federal judges including Hon. David Waxse, Rules Advisory Committee members, top law firm and corporate lawyers Register or learn more at Use code “BIGDATA2014” to save 20%
    4. 4. Presenters Ellen S. Pyle | Discovery Counsel McDermott Will & Emery • Focuses on e-discovery, data privacy and information governance • Background in tax evasion, securities, fraud and white collar litigation Paul Starrett | Chief Global Risk Officer UBIC, North America • Head of global legal, operations and risk management groups • Background in law enforcement, corporate security and information security engineering • Chair of ABA Big Data Committee
    5. 5. Big Data – Outline 1. What is big data? (Paul) 2. Legal Issues (Elle) 3. Domain Experts and Data Scientists (Paul) 4. Best Practices and Strategies (Elle) 5. The Future (Paul) 6. Closing Thoughts (Paul and Elle)
    6. 6. Big Data – What is Big Data? What is Big Data?
    7. 7. Big Data – What is Big Data? • What is big data? – Data that is too large or complex for conventional methods to handle – Complexity / Volume / Velocity • What additional issues affect decision to define data as “big”? – Time – Cost – Expertise levels
    8. 8. Big Data – What is Big Data? Structured: • Database, spreadsheet Semi-Structured: • HTML, XML, email (?), web, social media Unstructured: • Text, word processing files
    9. 9. Big Data – What is Big Data? Applications / Areas of Study: Structured • Database programs, SQL, “In-database” (high speed) Semi-Structured • Text analytics, social media API’s, web scrapers Unstructured • Text mining, NLP
    10. 10. Big Data – Tools and Resources Clustering: to find structure, commonality in data. “Unsupervised” learning. Association Rules: discover relationships between actions or items. “Market basket” analysis. Classification: assign known labels or classes to data. Includes Supervised learning. Modeling / Sampling. Regression: establish relationship between input and output data. Prediction of one variable from another.
    11. 11. Big Data – Tools and Resources Anomaly detection example: • Looks for outliers or unusual activity often indicative of errant behavior. • Unsupervised - uses descriptive analytics such as clustering to establish "normal" or "regular" patterns already existing in data. Anything outside regular patterns is an anomaly. • Supervised - uses pre-determined patterns (established by iterative training) known to be indicative of normal (non-threat) or abnormal (threat?) behavior. • Is anomaly just "noise"?
    12. 12. Big Data – Legal Issues Legal Issues
    13. 13. Big Data – Legal Issues • Various Legal Issues arise out of Big Data. • Legal practitioners may be aware of latent liabilities when they are brought in for other cases and can make clients aware, counsel them • Truth seeking (Litigation, Compliance, Regulatory) can be enhanced if data easier to retrieve; legacy data creating enormous cost and time burden • Enhanced regulatory requirements now in place for Data Privacy and Confidentiality • Liabilities can be shifted through contractual techniques, so review of contract clauses (indemnification, warranties) may be warranted
    14. 14. Big Data – Legal Issues • Truth seeking (Litigation, Compliance, Regulatory) can be enhanced if data easier to retrieve; legacy data creating enormous cost and time burden
    15. 15. Big Data – Legal Issues • Enhanced regulatory requirements now in place for Data Privacy and Confidentiality
    16. 16. Big Data – Legal Issues • Liabilities can be shifted through contractual techniques, so review of contract clauses (indemnification, warranties) may be warranted
    17. 17. Big Data – Legal Issues Storage Strategies Retain Less Retain More` Relevant Smart Sorting = Improved Recall = Reduced Data Costs Lower to recall Lower to store
    18. 18. Big Data Domain Experts and Data Scientists Domain Experts and Data Scientists
    19. 19. Big Data Domain Experts and Data Scientists Structured Information is Abstract (may be in ANY form) • Investigation? • Compliance? • Lawsuit? Semi-structured Unstructured
    20. 20. Big Data Domain Experts and Data Scientists Domain Experts Data Scientists
    21. 21. Big Data Domain Experts and Data Scientists Domain Experts • • • • • • • (Info Governance) Cyber / Info Security Investigations E-discovery Compliance Business Intelligence Document Mgmt. (Etc.) Data Scientist • Data: – Analysts – Info Retrieval • Subject: – Math – Statistics / Linguistics – Text / Data Mining • Technical: – DBA’s – Programmers
    22. 22. Big Data Best Practices and Strategies Best Practices and Strategies
    23. 23. Big Data, Smart Data Strategies Smart Sorting of Relevant Data Improved Regulatory Compliance Smart Sorting of Data Smarter Compliance Finding The Issues Before the WhistleBlower
    24. 24. Big Data, Secure Data Strategies Smart Sorting of Relevant Data Identifying the Security Gaps Enhanced Data Protection Schemes
    25. 25. Big Data Strategies Review Less Review Faster and more Accurately Use Lower Cost Resources with significant review experience Transparency and Metrics
    26. 26. Big Data Strategies – Process Change - Formation of IG program including: • Active Senior management and business line leader involvement • Consider multi- organizational level program • Consistent timely evaluation and re-evaluation (quarterly/monthly) • Transparency and education • Clarification and education of process, exceptions • Incentives and recognition, ownership and accountability • Implement common mechanisms across the organization / support synergies
    27. 27. Big Data Strategies • Data Tracked Across Multiple Projects and Business Groups • Reduction of Data Volume • Analytics, Culling, Clustering = Smart Data Handling
    28. 28. Big Data – The Future The Future
    29. 29. Big Data – The Future Big Data Committee of ABA will generate: • Top-level Best Practice Guide: Big Data Reference Model • Recommendations: Subcommittees, Working Groups and Task Forces around verticals • This is first time this entire effort is being done in legal profession so may need to reconsider as we go (iterative process)
    30. 30. Big Data – The Future Legal Profession Issues: • Integrity of process • The truth, the whole truth and nothing but the truth • Data Privacy and Confidentiality • Cost vs. Benefit (e.g. Proportionality) • Time – deadlines
    31. 31. Big Data Closing Thoughts • Data science and conventional methods used together • Devil always in details • Information may be in any form of big data • Data science is patchwork of statistics, data mining, machine learning, linguistics, programming, etc. Each discipline rarely knows what the other does! Legal issues there??
    32. 32. Questions? 650-654-7664 202-756-8986