Legal Aspects of Managing Big Data (Kemp IT Law, V.2, September 2014) Ii
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Legal Aspects of Managing Big Data Richard Kemp September 2014 TABLE OF CONTENTS Para Heading Page Para Heading Page 22. Level 3: IP rights in relation to data (v) – likely A. INTRODUCTION ................................................... 1 direction of travel ................................................. 17 1. ‘Big Data is everywhere’........................................ 1 23. Level 4: contracting for data (i) - introduction...... 17 2. The US NIC’s December 2012 report ................... 2 25. Level 4: contracting for data (ii) – key areas for Big 3. What is ‘Big Data’? ................................................ 2 Data ..................................................................... 18 4. The policy perspective – the Commission’s July 25. Level 5: data regulation (i) - introduction............. 18 2014 Communication ............................................ 3 26. Level 5: data regulation (ii) – Data Protection ..... 18 5. Scope and aims of this white paper ...................... 4 27. Level 5: data regulation (iii) – ICO’s 28 July 2004 Report on Big Data and Data Protection............. 19 B. THE BUSINESS CONTEXT: BIG DATA IN KEY 28. Level 5: data regulation (iv) – competition law .... 21 VERTICAL SECTORS .......................................... 5 6. Introduction: pebbles and mountains .................... 5 29. Level 5: data regulation (v) – sector specific regulation ............................................................ 21 7. The banking sector ................................................ 5 30. Level 6: information management and security .. 21 8. Information architecture in the banking sector: TOGAF and BIAN ................................................. 6 31. The legal framework for Big Data – a complex picture.................................................................. 22 9. The insurance sector ............................................. 6 10. The air transport industry ...................................... 7 D. BIG DATA OPERATIONS INSIDE THE 11. The recorded music industry ................................. 7 ORGANISATION ................................................ 23 12. The healthcare sector ........................................... 8 32. Introduction ......................................................... 23 13. The public sector ................................................... 9 33. Data input operations .......................................... 23 34. Data processing operations ................................ 24 C. TOWARDS A COMMON LEGAL FRAMEWORK 35. Data output operations ........................................ 24 FOR BIG DATA .................................................. 10 36. The ‘pan-enterprise’ view .................................... 24 14. Introduction: what is data in legal terms?............ 10 15. The 6 level data stack ......................................... 11 E. MANAGEMENT AND GOVERNANCE OF BIG 16. Level 1: platform infrastructure ........................... 11 DATA PROJECTS .............................................. 25 17. Level 2: information architecture ......................... 12 37. Introduction ......................................................... 25 18. Level 3: intellectual property rights in relation to 38. The Forrester Research IIG Report .................... 25 data (i) - introduction ........................................... 12 39. Step 1: risk assessment ...................................... 26 19. Level 3: IP rights in relation to data (ii) - 40. Step 2: strategy statement .................................. 27 copyright .............................................................. 12 41. Step 3: policy statement ...................................... 27 20. Level 3: IP rights in relation to data (iii) – database 42. Step 4: processes and procedures ..................... 28 right ..................................................................... 14 21. Level 3: IP rights in relation to data (iv) – F. CONCLUSION .................................................... 28 confidentiality and trade secrets ......................... 16 43. Conclusion .......................................................... 28 TABLE OF FIGURES Figure 1: Towards a comprehensive legal framework for Big Data Figure 2: The Big Data engine – input, processing and output operations Figure 3: Towards a structured approach for managing Big Data projects Legal Aspects of Managing Big Data (Kemp IT Law, v.2, September 2014) ii LEGAL ASPECTS OF MANAGING BIG DATA A. INTRODUCTION 1. ‘Big Data is everywhere’. ‘If you haven’t heard’ trumpeted the Financial Times’ Lex column of 27 June 2014, ‘Big Data is everywhere’.1 Over the past twenty years, the bow wave in IT has moved on from hardware and software to the data that they process, and in an increasingly competitive and data-centric world, harnessing the tides of the Big Data ocean will confer competitive advantage in enabling a company to know more about its customers and market place than its competitors. Commenting that the business intelligence and analytics (‘BIA’) software market is worth $16bn a year and growing at 8% a year, the FT Lex column called out research from consultancy Gartner Inc.2 who showed that the BIA market is currently undergoing an ‘accelerated transformation’ from retrospective BIA software - used mainly for measurement and reporting - to prospective BIA software used for prediction, forecasting and modelling. This is fuelling a race as the BIA software majors – Oracle, SAP, IBM and SAS, whose combined BIA software turnover totals around $10bn – vie with smaller, faster growing BIA specialists like QlikTech, Splunk and Tableau to bridge the gap between the oceans of available Big Data and BIA software’s ability to harness it for competitive advantage in a structured, legally compliant way. The European Commission (Commission) in its Communication of 2 July 20143, quoting a UK report, also comments on this accelerating growth: “Big data technology and services are expected to grow worldwide to USD 16.9 billion in 2015 at a compound annual growth rate of 40% – about seven times that of the information and communications technology (ICT) market overall. A recent study predicts that in the UK alone, the number of specialist big data staff working in larger firms will increase by more than 240% over the next five years.” It is this race for competitive advantage – knowing more than your competitor not so much about what your customers have just done as about what they are likely to do next – that is at the commercial epicentre of Big Data. But it is a race that is just beginning: Gartner also points out4 that only 15% of Fortune 500 companies will be able to exploit Big Data for competitive advantage by the end of 2015 and that only 8% of companies are currently using Big Data analytics at all. 1 http://www.ft.com/cms/s/3/525236ca-fd4f-11e3-bc93-00144feab7de.html?siteedition=uk#axzz35vtpzx2A 2 http://www.gartner.com/technology/reprints.do?id=1-1QHKSEP&ct=140206&st=sb 3 Towards a thriving data-driven economy (COM(2014) 442 Final) at https://ec.europa.eu/digital- agenda/en/news/communication-data-driven-economy 4 http://www.gartner.com/technology/topics/big-data.jsp Legal Aspects of Managing Big Data (Kemp IT Law, v.2, September 2014) 1 2. The US NIC’s December 2012 report. Big Data’s direction of travel is well signposted in the December 2012 long range report of the US National Intelligence Council ‘Global Trends 2030: Alternative Worlds’5 where it articulates a focus on data solutions and Big Data as a key IT driver over the next two decades: “Information technology is entering the Big Data era. Process power and data storage are becoming almost free; networks and the cloud will provide global access; and pervasive services; social media and cybersecurity will be large new markets.”6 Opportunities arising through Big Data are not without their challenges and issues however: “Since modern data solutions have emerged, big datasets have grown exponentially in size. At the same time, the various building blocks of knowledge discovery, as well as the software tools and best practices available to organizations that handle big datasets, have not kept pace with such growth. As a result, a large - and very rapidly growing - gap exists between the amount of data that organizations can accumulate and organizations’ abilities to leverage those data in a way that is useful. Ideally, artificial intelligence, data visualization technologies and organizational best practices will evolve to the point where data solutions ensure that people who need the information get access to the right information at the right time - and don’t become overloaded with confusing or irrelevant information.”7 It is these challenges and issues that the fast growing BIA software market is seeking to address. 3. What is ‘Big Data’? As used in this White Paper, ‘Big Data’ is shorthand for the aggregation, analysis and increasing value of vast exploitable datasets of unstructured and structured digital information. Along with Cloud8, mobile9 and social computing, it is one of the four main drivers of change in information technology as it moves into new areas whose features currently include machine learning, 3D printing, virtual reality, the Internet of Things and nanotechnology. Two recent papers, one from each side of the Atlantic, have addressed Big Data. Commenting that there was no one generally accepted definition, the White House’s Executive Office of the President (EOP) in a report dated