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! 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 EDiscoveryConference.com! Use code BIGDATA2014 to save 20%
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
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)
Big Data What is Big Data? What is Big Data?
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
Big Data What is Big Data? Structured: Database, spreadsheet Semi-Structured: HTML, XML, email (?), web, social media Unstructured: Text, word processing files
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
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.
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"?
Big Data Legal Issues Legal Issues
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 www.mwe.com
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 www.mwe.com
Big Data Legal Issues Enhanced regulatory requirements now in place for Data Privacy and Confidentiality www.mwe.com
Big Data Legal Issues Liabilities can be shifted through contractual techniques, so review of contract clauses (indemnification, warranties) may be warranted www.mwe.com
Big Data Legal Issues Storage Strategies! Retain Less! Retain More`\ Relevant! Smart Sorting = Improved Recall! = Reduced Data Costs! Lower to recall! Lower to store www.mwe.com
Big Data Domain Experts and Data Scientists Domain Experts and Data Scientists
Big Data Domain Experts and Data Scientists Structured Information is Abstract (may be in ANY form) Investigation? Compliance? Lawsuit? Semi-structured Unstructured
Big Data Domain Experts and Data Scientists Domain Experts Data Scientists
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
Big Data Best Practices and Strategies Best Practices and Strategies
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 www.mwe.com
Big Data, Secure Data Strategies! Smart Sorting of Relevant Data! Identifying the Security Gaps! Enhanced Data Protection Schemes www.mwe.com
Big Data Strategies! Review Less! Review Faster and more Accurately! Use Lower Cost Resources with significant review experience! Transparency and Metrics www.mwe.com
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 www.mwe.com
Big Data Strategies Data Tracked Across Multiple Projects and Business Groups Reduction of Data Volume Analytics, Culling, Clustering = Smart Data Handling www.mwe.com
Big Data The Future The Future
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)
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
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??
Questions? paul_starrett@ubicna.com 650-654-7664 epyle@mwe.com 202-756-8986