Big Data, Big Issues: Global Challenges and Effective Solutions SCCE September 26, 2016 Chicago James Lin & Jonathan Armstrong What we’ll cover • Big data as an opportunity and a threat • The Science Bit... • Attitude of courts & regulators to big data • Use of data in diligence & investigations • Privacy considerations • Accuracy • Practical tips © Cordery 2016 1 1 What is big data? “Big data is a broad term for data sets so large or complex that traditional data processing applications are inadequate. Challenges include analysis, capture, data curation, search, sharing, storage, transfer, visualisation and information privacy” © Cordery 2016 2 The Four Vs of Big Data •data at scale, •fast speed at large amount of which data is data generated Volume Velocity Variety Veracity •many different •Data uncertainty, forms of data various quality and accuracy © Cordery 2016 3 2 The “Small” in Big Data What we really want from big data is actionable insight —small, focused, and in-context intelligence that can be acted upon to solve a business problem. Russell Ackoff, a pioneering systems theorist, the content of the human mind can be classified into five categories: WWiissddoomm 1. Data: facts, observations, empirical perceptions, symbols (e.g., text files, numbers, images, video files) UUnnddeerrssttaannddiinngg 2. Information: data that are processed to be useful answering “who”, “what”, “where”, and “when” questions KKnnoowwlleeddggee 3. Knowledge: organizing information in such a way that it describes how things work and can be used to answer “How” IInnffoorrmmaattiioonn 4. Understanding: synthesizing multiple sources of knowledge over time to explain “why” DDaattaa 5. Wisdom: actionable insights, judgement, the ability to increase effectiveness Source: Ackoff, R., 1989, “From Data to Wisdom”, Journal of Applied Systems Analysis16: 3-9. © Cordery 2016 4 Turning Big Data into Actionable Insights Business Objectives Start with biz objectives and end user needs not big data and technology. Big Data Platform • People + Process + Technology Big Data • AI and Machine Learning Actionable Insights • Intelligence Augmentation (IA) • Data Mining and Refining • Data Plumbing and Processing Data Governance to ensure data integrity, proper use, privacy, security, and regulatory compliance © Cordery 2016 5 3 Benefits for Diligence (Vendors, Customers, Patients, Third Party) Risk Rating NewDataInput statistical modeling replacing media, legal news, interaction judgmental analysis for risk data . . ., multiple countries, rankings /classification multiple sources, “Dynacism” Consolidated Viewpoint timely updating and integrated diligence combining continuous monitoring, real static research with time or just-in-time transaction data © Cordery 2016 6 The Hot Debate ANONYMISATION V PSEUDONYMISATION © Cordery 2016 7 4 Attitude of courts to big data • Data privacy increasingly litigated • Vidal-Hall case in UK • Schrems litigation in Ireland, Austria – 2 new ECJ cases • RTBF cases in Europe, Russia, Japan © Cordery 2016 8 Attempts to regulate use of Big Data • Norway Datatilsynet Report September 2013 • US Executive Office of the President Report May 2014 • UK ICO consultation October 2014 • EDPS Opinion 7/2015 November 2015 • FTC report January 2016 • Japan Federation of Bar Associations objection to government GPS guidelines – August 2016 • EDPS conference – 29 September 2016 © Cordery 2016 9 5 Example: Deutsche Bahn • Monitoring employees as anti-corruption measures – c.173,000 employees affected • Reconciliation of employee data with data on 80,000 suppliers • Collectionof bank data of employees • Interception of email traffic • Overall fines of €1.1m © Cordery 2016 10 EU – US Data Transfer • Safe Harbor dead • Privacy Shield born but under attack (e.g. Ireland, Germany) • Standard Contractual Clauses under attack • BCRs © Cordery 2016 11 6 GDPR • Comes into force May 2018 • Regulates use of personal data • Has extra-territorial effect • Big data implications e.g. in health studies • Fines of up to 4% of global annual turnover for breach • Mandatory DPIAs • Subject Access Requests • Right to request algorithms etc. • Right to Data Portability • Increased consent requirements © Cordery 2016 12 Big Data and Transparency Laws • Prosecutors (e.g. the DoJ or SFO) increasingly have more informationat their disposal • The growthin whistleblowersadds to that data pot • Transparency laws oblige companies in high-risk sectors (e.g. mining, oil, gas, forestry etc.) to volunteer information to government including payments to overseasofficials • Pressure groups are increasingly reviewing data and using FOI requests e.g. UK Modern SlaveryAct 2015 • Prosecutors are employing e-discovery platforms, big data experts & new techniques (e.g. TAR) © Cordery 2016 13 7 Example: UK SFO Annual Report 2015-2016 “Once the new system is in place, the SFO plans to invest further in its data analysis capability, where it will seek to utilise Artificial Intelligence and machine learning ...” © Cordery 2016 14 However, is data always true? © Cordery 2016 15 8 © Cordery 2016 16 False whistleblowing • Malicious • Mistaken • Money-grabbing • Mad © Cordery 2016 17 9 What are the data privacy and compliance challenges? • Increased focus on data privacy issues • Right to be forgotten • The impact of Snowden • Employee screening legislation (e.g. Germany) • Concerns over use of Big Data to drive behaviours • Need for greater transparency © Cordery 2016 18 Example: Uber • Uber users sign up to Uber’s terms • What data collectiondo they disclose? © Cordery 2016 19 10