UNIVERSITÀ DEGLI STUDI DI PISA
DIPARTIMENTO DI GIURISPRUDENZA
CORSO DI LAUREA MAGISTRALE IN GIURISPRUDENZA
TESI DI LAUREA
The Regime of Non-personal Data between
Intellectual Property and Ownership Rights
Candidata Relatrice Lorena Sarah Loos Chiar.ma Prof.ssa Dianora Poletti
1
The Regime of Non-personal Data between
Intellectual Property and Ownership Rights
SUMMARY
INTRODUCTION ... 3
BIG DATA: SHAPING THE TOPIC FROM A GENERAL TO A SPECIFIC PERSPECTIVE ... 7
1. Social impact and legal issues in the technological environment ... 7
1.1 Information society ... 7
1.2 Law and technology: the challenges of regulation. ... 10
2. Big data: a definition. ... 15
3. The economic importance of big data. ... 20
3.1 The value of big data for businesses. ... 21
3.2 EU strategy to promote the data economy. ... 23
4. Focus on “industrial data”. ... 28
THE INTELLECTUAL PROPERTY TEST ... 32
1. Contextualising the problem ... 32
1.1. Big data and intellectual property ... 32
1.2. Big data and market failures ... 35
2. Legal protection of databases: focus on EU Directive ... 39
2.1 Testing database protection for industrial data ... 44
2.1.1 Copyright and non-personal data ... 45
2.1.2 Sui generis right and non-personal data... 46
3. Legal protection of trade secrets: focus on EU regime ... 51
3.1 Testing trade secret protection for industrial data ... 58
4. An alternative testing approach under patent law ... 63
2 OWNERSHIP RIGHTS IN INDUSTRIAL DATA: A CRITICAL
OVERVIEW ... 69
1. Introduction ... 69
2. A practical example: the case of the automotive industry ... 70
3. The EU proposal for a “data producer`s right” ... 80
3.1 Merits and demerits of the EU approach ... 87
4. Traditional legal concepts and data ... 92
4.1 Different approaches to regulation ... 93
4.2 Court cases addressing the topic ... 100
5. Data as intangible goods: a new (intellectual) property right for non-personal data? ... 105
5.1 The structure and arguments in favour of a new (intellectual) property right in data... 111
5.2 Arguments against a new (intellectual) property right in data ... 118
CONCLUSION ... 124
3
INTRODUCTION
The insight in the dynamic world of data can metaphorically be explained with the image of opening Pandora’s box. The first reason behind this consideration is that data are rarely considered on their own as single elements. The generic term “data”, even when not explicitly mentioned, usually also implies the adjective “big”. In other words, the ongoing digitalisation process and the development of the data economy lead to the creation of an increasing amount of large data sets which are the fundaments of what is known as the big data phenomenon. Due to their characteristics, big data rise big problems.
This is connected to the second reason behind our introductory consideration, namely the range of different problematic aspects that emerge once the focus lays on the data environment. From a general point of view, big data have an equal impact on the economic, social and legal level and therefore require more of a multidisciplinary approach and a better understating of the relationship between different issues. In relation to the specific legal point of view, nowadays new technological developments continuously challenge various fields of law and the regulatory effort attached to data is by no means less challenging. Information monopolies, privacy concerns, competition law issues, ownership rights and the relationship with existing legal measures are only some of the
4 main problematics that emerge when questioning whether and how regulating data. Moreover, these apparently separated points are often interrelated with each other, so that addressing one aspect has legal effects on another aspect as well.
The purpose of this study is to focus on one specific problematic aspect related to the big data phenomenon and to delimitate the area of research to one specific type of data. The main object of attention is the potential legal framework for industrial data, in particular in the light of existing intellectual property regimes and of an eventual alternative approach concerning data producers’ ownership rights. As we will have the opportunity to establish further on in this study, it is necessary to make an important distinction, in order to avoid confusion about the topic. Industrial data are non-personal data, mainly machine-generated or collected through sensors which therefore rise different issues in comparison to personal data. This is not to underestimate the privacy problematic or to hide that often it might be difficult to draw a precise line between the two types of data. But given that the topic of personal data is highly discussed these days and attracts most of the attention, it is even more interesting to adopt a still less explored point of view.
The aim of this study is therefore to analyse the different legal implications attached to non-personal data which are already permeating several industrial sectors, ranging from the automotive industry to the energy, retail or logistic industry. In
5 fact, the collection and analysis of industrial data is already happening and enables the improvement of production processes and machine maintenance and consequently leads to increased efficiency and reduction of costs. In sum, non-personal data are a source of valuable knowledge and information which favour an outcome of optimisation at all levels of production. It is probably just a matter of time before more attention is given to the topic, especially in the light of the very basic question of who owns non-personal data. We will see more in detail that there are always different stakeholders involved and obviously each of them would attempt to exclusively exploit the potential value of non-personal data.
The first chapter of this study gradually shapes the topic from a general to a specific perspective. Non-personal data are to be seen in the wider big data context which characterises our modern information society and determines a shift towards a new socioeconomic model. Furthermore, we will see that the topic directly relates to the complex relationship between law and technology. Law is challenged by technological development and has the difficult task to find a balance between regulating and promoting innovation. The topic will be further shaped through the definition of big data and their economic importance for businesses and on EU policy level. Finally, the specific perspective of industrial data is adopted and enables to continue with the main topic of discussion.
6 The second chapter represents a test of existing intellectual property regimes, in particular in relation to their suitability for regulating non-personal data. In fact, since industrial data do not have a clear legal framework yet, the first step should be to determine whether existing rules might be fit for purpose. The approach will be to analyse three categories, namely database protection, trade secret protection and patent law and determine whether those categories can also encompass non-personal data. It can already be anticipated that the outcome is not completely satisfactory and that limited protection might only be provided by the trade secret regime.
The third and last chapter represents an overview of an alternative approach: the protection of non-personal data in the form of proprietary rights. How exactly those rights should be configurated and structured is currently still unclear. The difficulty in allocating ownership is demonstrated by the practical example of the automotive industry. This chapter therefore provides a critical review of different position statements on the topic, in particular from the perspective of recognising ownership to data producers. A critical approach always implies the examination of both the merits and demerits of alternative points of view. Although it might be too premature to favour one specific point of view, we will notice that perhaps some proposals are more adequate than others.
7
CHAPTER 1
BIG DATA: SHAPING THE TOPIC FROM A
GENERAL TO A SPECIFIC PERSPECTIVE
1. Social impact and legal issues in the technological environment
1.1 Information society
Big data1 is inevitably the driving force behind several
revolutionary changes concerning different sectors of our human existence, so saying that big data is everywhere is certainly not an overstatement2.
1 At this point of the study the concept is considered as given. “Big data” will be
defined in the second section of this chapter, see p. 9.
2 McKinsey Global Institute, Big data: The next frontier for innovation, competition
and productivity, June 2011, 2 online available at
https://www.mckinsey.com/~/media/McKinsey/Business%20Functions/McKinsey% 20Digital/Our%20Insights/Big%20data%20The%20next%20frontier%20for%20inno vation/MGI_big_data_full_report.ashx, underlining that “digital data is now everywhere – in every sector, in every economy, in every organisation and user of digital technology”.
8 On one hand, the outcome of this ongoing process determines a significant shift towards a new socioeconomic model3, which is facilitated by an increasing amount of available
data and by a greater social and economic value of the data itself4.
On the other hand, this aspect represents nothing more than the evolution of our modern society, against the backdrop of big data innovation. Modern society, which is nowadays defined as information society where information is the key issue, fostered by the implementation of new digital information and communications technologies5. On the side of industrialised
countries, the production of tangible assets is no longer at the heart of the world economy, as the focus lays with steady increase on the creation of information and knowledge, considered new intangible goods6. Those “goods” are new
sources of productivity, enhancing competitiveness among firms and on a larger scale among different countries7.
3 OECD, Exploring Data-Driven Innovation as a New Source of Growth: Mapping the
Policy Issues Raised by "Big Data", OECD Publishing, Paris, 2013, online available at
https://doi.org/10.1787/5k47zw3fcp43-en.
4 Ibid.
5 M. DURANTE, Ethics, Law and the Politics and Information, Springer, 2017 p. 3,
the author is here mainly referring to the theory of information of L. Floridi who outlines the characteristics of the so-called information revolution, from a philosophical point of view. L. FLORIDI points out that the revolution has not only an impact on society, but on the whole perception of reality as well, for further detail see L. FLORIDI, The Philosophy of Information, OUP Oxford, 2013.
6 F. FAINI, P. PIETROPAOLI, Scienza giuridica e Tecnologie Informatiche, Torino,
Giappichelli, 2017, 18. The discussion on tangible and intangible goods will be discussed more in detail in Chapter 3 about ownership rights in big data.
7 M. CASTELLS, An Introduction to the Information Age, in City, 2(7), 1997, 6 ss., 7,
9 The acquisition of technological skills is therefore an important tool for disseminating knowledge with beneficial outcomes for both individuals and society8.
However, the general use of information and specifically the generalisation of data processing technologies might create different types of information society, changing life in general and for instance giving rise to social exclusion issues as well9. So,
information society as a dynamic and creative opportunity, but potentially subject only to market powers, which therefore needs to be controlled from a social point of view10. Whereas the main
focus of this study is not on this social exclusion problem, it is nevertheless useful to outline that there are always different issues involved; different point of views of the same problem, which should not be silenced without at least a superficial reference11, in order to maintain coherence in the whole work.
As far as our main topic concerns, information society is the environment enabling the creation and flourishing of big data technics. One of the main challenges will be to develop the
8 S. HORNBY, Z. CLARKE, Challenge and Change in the Information Society, in The
International Journal on Media Management, 5(2), 2012, 151 ss., 151. See also A.
MANTELERO, Big Data: I Rischi della Concentrazione del Potere Informativo e
Digitale e gli Strumenti di Controllo, in Il Diritto dell’Informazione e dell’Informatica,
1, 2012, 135-144 where the author outlines the risk of big companies and public authorities collecting huge amounts of data and subsequently hindering access for others. The creation of information monopolies certainly rises social concerns.
9 European Commission, Building the European Information Society for Us All,
published on 20th September 1997, online available at
https://publications.europa.eu/en/publication-detail/-/publication/2aca04ac-7e7d-4eb4-b69d-1638ce0ddeb0.
10 M. CASTELLS, An Introduction to the Information Age, supra, 9.
11 For a more detailed discussion on this social exclusion topic see for example N.
M. RICHARDS, J. H. KING, Three Paradoxes of Big Data, in Stanford Law Review
10 required skills and ICTs to transform the abundance of data in usable information and consequently in knowledge12. This
involves mainly the technical perspective in the IT field. From a broader perspective instead, the information age requires mainly cross-disciplinary solutions, involving the competences of experts in various branches and first of all policymakers charged with the task of finding responses to the complex issues involved13.
1.2 Law and technology: the challenges of regulation
Amongst the different angles by which the topic of big data can be addressed, the legal perspective inevitably plays a pivotal role. If big data represent new challenges and changes to the future socio-economic model, then law is required to find answers.
Indeed, big data are giving rise to several legal issues and legal uncertainties14. IT law is a relatively young field of law,
12 European Commission, Building the Information Society for Us All, supra, 15, the
EU Commission correctly outlines at this point that legal tools have traditionally resolved issue concerning scarcity of resources whereas in this field the issue will be the effective use of the big amount of available data
13 S. ZULHUDA, A.H. ANSARI in B.C NIRMAL, R. K. SINGH, Contemporary issues in
International law, Springer, 2018, 371-381, “This is because the information age is a discourse of a cross-disciplinary realm. It is for this reason that it is a matter of concern to all, including scientists, technocrats, lawyers, accountants, social scientists, political scientists and business people. But it is a matter of prime concern of policymakers”.
14 M. CORRALES, M. FENWICH, N. FORGO´, Disruptive Technologies Shaping the Law
of the Future, in M. CORRALES et al., New Technologies, Big Data and the Law,
11 attempting to address this type of problems and at the same time trying to reduce the growing gap between new technologies and legal systems15. Therefore, the discussion at the heart of this
study about intellectual property and ownership rights in big data perfectly fits into this legally uncertain environment.
The background of this uncertainty is the complicated relationship between law and technology. Ongoing technological developments put pressure on the legal and regulatory framework, today more than ever16. In view of this, it
is found that technology itself is often the driving force behind this uncertainty, with little attention on the complex and fragmented legal reality, resulting from the impact of new technological discoveries17. Big data as well face some issues in
relation to the legal implications. Even recent legislative tools failure to address this field of inquiry directly, demonstrating the veil of unclarity surrounding the topic18.
discussion about ownership rights in big data, problems in competition law, as well as personal data protection. See also, C. BUSCH, A. DE FRANCESCHI, Granular Legal
Norms: Big Data and the Personalisation of Private Law, in V. MAK et al., Research Handbook on Data Science and Law, Edward Elgar, 2018, 1-17, highlighting that big
data could technics could determine a shift from impersonal laws to more personal legal norms, although further research is required in relation to this aspect.
15Ibid.
16 G. LAURIE, S.H. HARMON, F. ARZUAGA, Foresighting Futures: Law, New
Technologies, and the Challenges of Regulating for Uncertainty, Law, Innovation and Technology, 4(1), 2012 in Law, Technology and Innovation, 1-33, online
available at https://doi.org/10.5235/175799612800650626.
17 Ibid.
18I am mainly referring to the Regulation (EU) 2016/679 of the European Parliament
and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation) which does not address big data directly, except of some new legal tools. The issue will be discussed more precisely in section 3.2 of this chapter when dealing with the EU action plan on the topic.
12 Although our legal system has a certain degree of flexibility, rapid incorporation of new technologies has often challenged its ability to quick changes19. Moreover, “law-making is a slow
process, while technology changes rapidly”20. The outcome is a
legally ambiguous environment, where technological innovations impose themselves on the factual level and rights and responsibilities cannot easily be traced21.
The point in question is commonly explained with a metaphor among legal experts: the relationship between law and technology is like a race and law is failing to keep up with the speed of technological development22. Furthermore, the
tendency is to highlight the positive aspects of new technologies, as means to promote well-being and prosperity of society, and considering law falling behind them23.
Despite there certainly is an element of truth in this argument, the metaphor addressing the conflict between law and technology is often just reflecting generic critiques to law in specific technological fields24. In other words, it is somehow a
19 A. SCHWABACH, Internet and the Law: Technology, Society and Compromises,
Santa Barbara Denver Oxford, ABC-CLIO, 2006, 3.
20 E. PALMERINI, E. STRADELLA, Law and Technology: The Challenge of Regulating
Technological Development, RoboLaw series 1, Pisa University Press, 2013.
21 Ibid., the author is underlining that “the development and diffusion of
(unregulated) technological influences users’ behaviour, generates needs, triggers a market demand, and ends up imposing itself with the force of the fact”.
22 L. B. MOSES, Agent of Change, in Griffith Law Review, 20(4), 2011, 763-794,
online available at https://doi.org/10.1080/10383441.2011.10854720.
23Ibid.
24 L. B. MOSES, Recurring Dilemmas: The Law’s race to keep up with Technological
Change, UNSW Law Research Paper, n.21, 2007 online available at
13 superficial generalisation which requires a further step of analysis, in order to completely understand the implications.
Whereas it is out of question that new technologies bring new legal dilemmas, a more methodical approach is needed to understand the different legal problems which are involved, namely problems concerning the creation of specific rules, the interpretation of existing law and sometimes the obsolescence of current legislation25. With this in mind, the issues concerning the
legal environment can easily be reduced to four main categories: new rules, uncertainty, scope of rules, justification of rules26.
Although it is useful to bear in mind that not every technological change will raise difficulties and sometimes only some of the above-mentioned categories are involved, it is nevertheless worth underlying that a technological-neutral legislation is not the solution27. The legal system has an economic and social
responsibility to address new technologies by regulating and promoting them.
The discussion about what should be the foundation of the relationship between law and emerging technologies is still a matter of concern for legal experts in IT law. On one hand, a proper theory of law and technology is considered the best
25 Ibid.
26 L.B. MOSES, Why Have a Theory and Law and Technological Change?, in
Minnesota Journal of Law, Science and Technology, 8(2), 2007, 589 ss., online
available at https://scholarship.law.umn.edu/mjlst/vol8/iss2/12.
27 L. B. MOSES, Recurring Dilemmas: The Law’s Race to Keep Up With Technological
Change, supra, the author is underlining that the legal dilemmas given by
technological development need to be addressed from a broader perspective and at the same time comprehensive of the differences regarding different
14 solution for learning from past technological innovations and apply those discoveries to future problems28. On the other hand,
some studies refuse such a theoretical approach, as law should simply discover the present technologies and then deal with them as with any other social factor29.
On the whole, the debate around techno-regulation is the outcome of clashing considerations: the urge to overcome the legal gap in this field, but at the same time trying not to hinder technological innovation with excessively cogent legislation30.
For the purpose of this complex process, “legal foresighting” is a useful tool for regulators who need to find legal solutions for our technologically developed society31. In particular, this exercise
involves a better understanding of the issues and possibilities involved, considering the opinions of different stakeholders and giving a structure for law shaping the future32.
At this point it is clear by what type of problems big data regulation is surrounded. The topic perfectly fits into this environment of uncertainty, reaching its climax when it comes to finding legal solutions. Big data is still considered to cause unknown consequences in the legal system, influencing
28 The point is explained more in detail in A. J. COCKFIELD, Towards a Law and
Technology Theory, in Manitoba Law Journal, 30, 2004, p.383.
29 On this point see for example A. SANTUOSSO et al., The Challenge of Innovation
in Law: The Impact of Technology and Science on Legal Studies and Practice, Pavia
University Press, 2015, p. 30.
30 E. PALMERINI, E. STRADELLA, Law and Technology: The Challenge of Regulating
Technological Development, supra.
31 G. LAURIE, S.H. HARMON, F. ARZUAGA, Foresighting Futures: Law, New
Technologies, and the Challenges of Regulating for Uncertainty, Law, Innovation and Technology, supra.
15 decision-making and leading to irrational outcomes33. However,
this should not function as a disincentive, as too fundamental values are put into play: the stability, effectiveness and accountability of governance and the legal system in general34.
Alongside the economic importance of big data, which will be outlined further on, this represents the background of this study. Moreover, it is the reason why it is worth addressing at least one particular legal aspect concerning big data, in this case intellectual property and the discussion about ownership rights, although the complexity and uncertainty is certainly acknowledged by the author.
2. Big data: a definition.
What are big data? To further understand the topic this section explores the definition of big data and for what purpose they can be used. As a premise, the point of view herein adopted attempts to give an overview of the main characteristics, acknowledging that a detailed technical discussion belongs to IT experts35. It is nevertheless important to understand the
implications of technologies interacting with law.
33 C. DEVINS et al., The Law and Big Data, in Cornell Journal of Law and Public
Policy, 27(2), 2017, online available at
https://scholarship.law.cornell.edu/cjlpp/vol27/iss2/3.
34 K. A. BAMBERGER, Foreword: Technology’s Transformation of the Regulatory
Endeavour, in Berkley Tech. Law Journal, 26, 2011, p.1315.
35 For a more technical insight see for example A. Y. ZOMAYA, Handbook of Big
16 Big data is a widespread term nowadays, everyone has at least heard of it sometimes. Yet, it is still unclear what it really means. Rather than a legal term, it is more a phenomenon affecting different fields, for instance the technical practice, social and legal sciences and economics36. Moreover, big data is not
some utopian entity, but a phenomenon already shaping reality and affecting businesses37. However, as already mentioned, there
is still no consensus about the meaning of big data, consequently providing a concise definition is a challenging, if not impossible38. It is nevertheless possible to analyse some
explanatory efforts and extrapolate some common characteristics.
The most commonly used and perhaps famous definition of big data is the one referring to the three V’s: volume, variety, velocity39.
Volume is maybe the most predominant characteristic, referring to the big amount of data already available and
36 N. FORGO´ et al., The Principle of Purpose Limitation and Big Data in M.
CORRALES et al., New Technologies, Big Data and The Law, supra, p. 20
37 P. SIMON, Too Big to Ignore: The Business Case for Big Data, Wiley, 2013, 50, the
author provides a list of major companies involved “Amazon, Apple, Facebook, Google, Yahoo!, Facebook, LinkedIn, American Airlines, IBM, Twitter, and scores of other companies currently use Big Data and related applications”.
38 Ibid. See also J. DUTCHER, What is Big Data?, Berkley School of Information,
published on 3th September 2014, online available at
https://datascience.berkeley.edu/what-is-big-data/ ,listing 40 different definitions of the phrase “big data” provided by leaders of various industries.
39 D. LANEY, 3-D Data Management: Controlling Data Volume, Velocity and Variety,
Application Delivery Strategies by META Group Inc., 2001, online available at http://blogs.gartner.com/doug‐laney/files/2012/01/ ad949‐3D‐Data‐Management‐ Controlling‐Data‐Volume‐Velocity‐and‐Variety.pdf.
17 increasing even more in the future40. A report from IDC estimates
the volume of big data will be grown from 33 zettabytes in 2018 to 175 zettabytes in 202541. However, it is impractical to provide
a fixed standard related to big data volume, as it really depends on time and different type of data, alongside the increasing storage capacities42.
Variety refers to the different types of data collected and processed nowadays. The heterogeneous category of big data includes mainly three type of data: structured, unstructured and semi-structured. Structured data is usually organised in relational databases and is therefore the most easily searchable, both by human queries and via algorithms 43. Alternatively,
unstructured data is basically everything else: human- or machine-generated data with an internal structure, but without a predefined data-model44. The latter is the predominant
category in terms of amount of created data, including for instance human-generated text files, emails, social media, websites, mobile data, media, but also machine-generated
40 D. PICKEL, What Is Big Data? A Complete Guide, published on 22th August 2018,
online available athttps://learn.g2crowd.com/big-data is giving a definition of the “three Vs”.
41 D. REINSEL, J. GANTZ, J. RYDNING, Datagate 2025: The Digitization of The World,
IDC White Paper, 2018, online available at https://www.seagate.com/files/www-content/our-story/trends/files/idc-seagate-dataage-whitepaper.pdf. A zettabyte corresponds to 102 bytes.
42 A. GANDOMI, M. HAIDER, Beyond The Hype: Big Data Concepts, Methods and
Analytics, in International Journal of Information Management, 35, 2015, 137-144.
43 C. TAYLOR, Structured vs. Unstructured Data, Datamation, 2018, online available
at https://www.datamation.com/big-data/structured-vs-unstructured-data.html.
18 scientific data, sensor data or satellite imagery45. The further
development of IoT and smart cities will increase even more the variety of sources for collecting big data46. Semi-structured data
is somehow a category in between because it is structured, but not organised in a rational model, such as email for example.
Finally, velocity refers to the speed at which the big data universe is growing. An exponential fast expansion characterises the creation of big data, which consequently leads to an important implication, namely the growing need for velocity in processing and analysing data. If data management systems are not capable of performing real-time analysis, then most of the collected data will be lost47. It is here that big data technologies
come into play which enable the creation of real-time knowledge by processing a huge amount of “perishable data”48.
Over time, due to the increased awareness about the implications of big data, three other main characteristics have been added to those original “three v’s”. Firstly, veracity is an important point to consider in relation to the reliability of big data. As the amount of low veracity data is growing, it becomes
45 A precise description of different big data resources can be found in C. TANG, The
Data Industry: The Business and Economics of Information and Big Data, Wiley,
2016, 19-40.
46 For a more detailed analysis see M. STROBACH et al., Towards a Big Data
Analytics Framework for IoT and Smart City Applications in F. XHAFA et al.,
Modeling and Processing for Next Generation Big Data Tecnologies, Springer, 2015,
257-282.
47 B. DYKES, Big Data: Forget Volume and Variety, focus on Velocity, Forbes, 2017,
online available at https://www.forbes.com/sites/brentdykes/2017/06/28/big-data-forget-volume-and-variety-focus-on-velocity/#64cb757c6f7d the author is underlining the importance of velocity, in comparison to the other two
characteristics, namely volume and variety.
19 even more challenging to extrapolate valuable information and to technologically cope with this type of uncertainties49.
Secondly, value is playing an important role when dealing with big data. Usually economically useful information is hidden in a larger set of data, which therefore requires a prior identification of the valuable material and a subsequent transformation in economically interesting data50. Finally, it has often been
outlined that big data is above all a matter of complexity, rather than just of size. In fact, the term is frequently used in relation to the use of new computer technologies, for instance machine learning and artificial intelligence, to a complex assemblage of data51. Moreover, this aspect highlights the insufficiency of
standard data management systems due to the unpredictable combinations of big data itself52. The merit of this latter
explanation is that it highlights the strong linkage between big data and their related technologies, which therefore become an important part of the definition itself53. Starting from this point it
is possible to outline some similarities among the different
49 J. PALFREYMAN, Big Data-Vexed by Veracity?, IBM Government Industry Blog,
2013, online available at https://www.ibm.com/blogs/insights-on-business/government/big-data-vexed-by-veracity/.
50 Oracle White Paper, Oracle: Big Data for Enterprise, Oracle, 2013, online
available at www.oracle.com/us/products/database/big-data-for-enterprise-519135.pdf.
51 Microsoft, The Big Bang: How the Big Data Explosion is changing the world, 2013,
online available at https://news.microsoft.com/2013/02/11/the-big-bang-how-the-big-data-explosion-is-changing-the-world/.
52 R. HILLARD, It’s Time for a New Definition of Big Data, MIKE2.0 open
methodology, 2012, online available at
http://mike2.openmethodology.org/blogs/information-development/2012/03/18/its-time-for-a-new-definition-of-big-data/.
53 J. S. WARD, A. BAKER, Undefined by Data: A Survey of Big Data Definitions,
20 definitions: size, complexity and technologies are all considered critical factors when dealing with big data54. If we then consider
that the attributes of value and veracity can easily be encompassed into the category of complexity, this latter definition is certainly more comprehensive and accurate in relation to the different angles of the topic in question.
3. The economic importance of big data.
Although the main part of this study55 will concern the law
perspective of big data, it is nevertheless useful to continue this introductory chapter by posing a question: why it is even important to discuss about big data among legal experts? It has already been outlined that big data is inevitably shaping our society, better known as information society. Moreover, the topic fits into the wider phenomenon of law attempting to regulate technology56. At this point it is possible to add another angle: the
economic value of big data, both for the private and public sector. This is certainly a pivotal aspect because about this subject the discussion becomes even more animated. After all, productivity is a matter of concern for both government policy and business strategies.
54 Ibid.
55 See further on Chapter 2 addressing the relationship between IP and Big Data
and Chapter 3 discussing ownership rights in data.
21
3.1 The value of big data for businesses.
Through big data processing and analysing enterprises can improve the understanding of their business models and their consumers’ preferences and in addition maintain a competitive and innovative position. The big data journey is all about discovering new things and the development of new business models57. The end goal for a company should be the effortless
integration of big data in their business structure and consequently the extrapolation of information, which means value58. In fact, many businesses are adopting big data as a core
element of their functioning, as it is certainly a competitive advantage to collect information and extract meaningful and strategic knowledge59. To be more precise, the data economy
involves different types of companies dealing with big data: data companies which supply data related products, services and technologies in the data market; data user companies which represent the demand side of the data market, thus those collecting, processing and analysing data for their purposes60.
57 C. CORDON et al., Strategy is Digital: How Companies Can Use Big Data in the
Value Chain, Springer, 2016, 151, the author is here giving a detailed description of
those different companies’ characteristics by classifying them into categories such as sector they are operating in or by different countries.
58 Oracle White Paper, Oracle: Big Data for Enterprise, supra.
59 J.M CAVANILLAS, E. CURRY, W. WAHLSTER, New Horizons for a Data-Driven
Economy: A Roadmap for Usage and Exploitation of Big Data in Europe, Springer,
2018.
60 IDC, European Data Market, Smart 2013/0063: Final Report, published on 1st
February 2017, online available at datalandscape.eu/data-driven-stories-news-studies/european-data-market-study-final-report-out.
22 When talking about businesses which leverage the most economical value of big data, then the so called “data value chain” comes into play: “the value chain categorises the generic value-adding activities of an organisation allowing them to be understood and optimised”61. From a very general point of the
data value chain entails different activities and the outcome is the acquirement of valuable knowledge. First step is the data acquisition, meaning the gathering of data which represents a challenge in relation to big data, as it is not easy at all to find adequate technologies for the purpose. The next step is data analysis and therefore the process of selecting material and recognise the valuable information. The latter is a very important point and is then followed by data curation which entails the life-cycle management of the collected data, for the purpose of quality for instance. Furthermore, data needs to be quickly and easily accessible, thus this involves data storage. The final step is data usage: the extrapolated information is incorporated into the business’ strategy, contributes to decision-making, reduces costs and most importantly increases value62.
61 J.M CAVANILLAS, E. CURRY, W. WAHLSTER, New Horizons for a Data-Driven
Economy: A Roadmap for Usage and Exploitation of Big Data in Europe, supra.
62 Ibid. pp.29-33 This is a generic description of the value chain, but it encompasses
all the important steps. Some classifications refer only to the three main stages of the data value chain, namely the acquisition, the organisation and the analysis of big data.
23 So how is big data actually creating value for businesses? A pioneering McKinsey Report63 highlights five ways of added
value thanks to big data:
increased transparency which leads to better quality in products and services.
a better monitoring and therefore improvement in business performance.
segmentation of customers in order to provide more tailored solutions.
improvement of decision-making and discovery of otherwise hidden information by resorting to algorithms.
finally, the development of the next generations of products and innovative business models64.
3.2 EU strategy to promote the data economy.
The development of the knowledge economy is a great source for new opportunities within the EU. In order to fully benefit from this data-driven innovation the Commission and Europe’s governments need to embrace data as a core element of
63McKinsey Global Institute, Big data: The next frontier for innovation, competition
and productivity, supra, pioneering because the report is from 2011, so somehow
forerunning all the main discussion about the big data potential which arrive a few years later and are predominant today.
24 economic progress65. Data’s potential must be unlocked by
specific policy strategies which go in line with the innovation wave of new technologies, all based on the extraction of value from data66. “Data will be the raw material of the twenty first
century. The EU would shift towards a common digital market"67.
Indeed, the EU commission has promoted various initiatives over the years to create the conditions for economic growth, by addressing the big data topic as well. A communication in 2014 presents the idea of a coordinated action plan involving Member States and the EU and suggests an initial set of actions toward a data driven economy68. In particular, it points out that EU has
embraced the data evolution more slowly in comparison to the US. Afterwards, the Digital Single Market Strategy, as the title suggests, outlines the very first strategical plan to address the issues hindering the leverage of the digital economy’s full
65 P. MACDONNELL, D. CASTRO, Europe Should Embrace the Data Revolution,
Centre for Data Innovation, 2016, online available at
https://www.datainnovation.org/2016/02/europe-should-embrace-the-data-revolution/ “The key for Europe is to implement data-driven innovation within sectors that currently dominate its economy, because the continent’s economic future depends on their continuing strength. These industries include agriculture, manufacturing, finance, transportation, and healthcare”.
66 IDC, Lisbon Council, First Report on Policy Conclusions: How the Power of Data
Will Drive Eu Economy, 2018, online available at
http://datalandscape.eu/sites/default/files/report/EDM_D2.2_First_Report_on_Po licy_Conclusions_20.04.2018.pdf.
67 A. Merkel’s speech at the World Economic Forum 2018, online available at https://www.weforum.org/agenda/2018/01/angela-merkel-at-davos-we-need-global-cooperation-not-walls/.
68 European Commission, Communication from the Commission to the European
Parliament, the Council, The European Economic and Social Committee and the Committee of the Regions: Towards a Thriving Data-Driven Economy, 02 July
25 potential69. First of all, the document addresses the reason behind
the need of a digital single market, outlining that the Internet and digital technologies rapidly transform our lives and create opportunities for growth, jobs and innovation. Thus, there is an acknowledgment that the starting point must be a coordinated EU policy action to eliminate barriers still hindering the development of the digital economy. The Digital Single Market enables the EU to maintain a competitive position as a world leader. Among the three pillars on which the strategy is built70,
the third one addressing the growth potential of the EU Digital Economy also explicitly refers to investments in ITC technologies, such as big data. The Commission states that the Big Data sector is growing 40% per year, seven times faster than the entire IT market. The list of technological and legal barriers that need to be removed refers to restrictions related to data location, fragmented implementation of copyright rules and in general obstacles for a cross-border free flow of data. In fact, unjustified restrictions on free movement of data slow down the process of embracing the data economy. The issue concerns all type of data collected by different enterprises and actors dealing with machine-generated data, whether personal or
69 European Commission, Communication from the Commission to the European
Parliament, The Council, The European Economic and Social Committee and the Committee of the Regions: A Digital Single Market Strategy for Europe, 06 July
2015, COM(2015) 193 final.
70 Ibid. p. 3-4 the three pillars are: 1. Better access for consumers and businesses to
online goods and services across Europe; 2. Creating the right conditions for digital networks and services to flourish; 3. Maximising the growth potential of our European Digital Economy.
26 personal, as well as with human generated data71. Different
measures have been adopted to tackle different problems related to the free flow of both personal and non-personal data.
On one hand, in order to strengthen businesses’ and consumers’ trust in cross-border transactions72 the GDPR73 has
introduced a strong set of data protection rules which indeed have the advantage to boost confidence in online services in general. Moreover, it is clearly stated that data protection rules should not restrict or prohibit the free circulation of data itself74,
so that in the end the GDPR is a first foundation upon which the principle of free movement of data can be built. However, the GDPR is obviously dealing with issues concerning personal data and has also somehow lost the opportunity to address directly the topic of big data75.
The following measures are more a matter of interest for this study. In fact, on the other hand, the Midterm Review on the
71 European Commission, Communication from the Commission to the European
Parliament, The Council, The European Economic and Social Committee and the Committee of the Regions: Building a European Data Economy, 10 January 2017,
COM(2017) 9 final.
72 This aspect mainly concerns cross-border cloud systems for storing and processing
data.
73 General Data Protection Regulation (GDPR), supra, in force since May 2018. 74 Art. 1(3) GDPR “The free movement of personal data within the Union shall be
neither restricted nor prohibited for reasons connected with the protection of natural persons with regard to the processing of personal data”.
75 The next section outlines that personal data is not the main concern of this
study, however big data are obviously rising concerns about data protection as well. See for example S. SRINIVASAN, Guide to Big Data Applications, in Studies in Big Data, 26, Springer, 2018, 25 “The evolution of networked information and communication technologies has, in one generation, radically changed the value of and ways to manage data. These trends carry profound implications for privacy. The creation and dissemination of data has accelerated around the world, and is being copied and stored indefinitely, resulting in the emergence of Big Data”.
27 implementation of the Digital Single Market76 highlighted that
Member States need to adopt a principle of free movement also for non-personal data, enabling continuous access and free circulation within the single market. Data location requirements which are limiting storage of data within certain territories, should only be justified in limited cases, for instance national security77. In line with this approach on 14 November 2018 the
European Parliament and the Council signed a Regulation78 that,
together with the GDPR on the privacy level, ensures a comprehensive programme to the free movement of all data in the EU. The context of the initiative is a probably 160 billion worth data market by 202079, driven by new technologies such as
cloud computing, Internet of Things, Artificial Intelligence and obviously big data80. Especially the preamble of the Regulation
in question outlines again that the digitalisation of economy is rapidly growing and ITC is already the foundation of all innovative economic systems. An efficient non-personal data processing mechanism is a fundamental part in any value chain. The aim is to favour the growth of a competitive, trustworthy
76 European Commission, Communication from the Commission to the European
Parliament, The Council, The European Economic and Social Committee and the Committee of the Regions on The Mid-term Review on the Implementation of the Digital Single Market Strategy: A Connected Digital Market for All, 10 May 2017,
COM(2017) 228 final.
77Ibid. p. 11.
78 Regulation (EU) 2018/1807 of the European parliament and of the Council of 14
November 2018 on a framework for the flow of non-personal data in the European Union, entering into force in May 2019.
79 IDC, European Data Market, Smart 2013/0063: Final Report, supra.
80European Commission, Proposal for a Regulation of the European parliament and
of the Council on a framework for the free flow of non-personal data in the European Union, 13 September 2017, COM(2017) 495 final.
28 and reliable market which insures the free flow of non-personal data by laying down rules regarding three different issues81:
firstly, the prohibition of data localisation measure; secondly, access of data for competent authorities to perform their duties and finally, encourage the development of self-regulatory tools in order to facilitate data portability for professional users82.
It is clear at this point that a clear, coherent and predictable legal framework is highly beneficial and moreover also necessary to develop the full potential of a data driven economy. As a matter of fact, big data are also “big” because they inevitably represent a huge part of today’s economy. Otherwise we would not be able to explain the increased interest on the topic by the EU institutions, the single Member States and obviously many businesses already monetising the value of big data.
4. Focus on “industrial data”.
The last building block of this big data analysis is narrowing the topic to what represents the connecting point to the core of this study. As the title suggests, the scope of this first chapter is to shape the topic from a general to a specific perspective. In fact, it is not possible to understand the point of view herein adopted
81 Art.1 Regulation (EU) 2018/1807, supra: This Regulation aims to ensure the free
flow of data other than personal data within the Union by laying down rules relating to data localisation requirements, the availability of data to competent authorities and the porting of data for professional users.
29 without at least a brief overview of the bigger picture, as previously done.
Although nowadays, especially after the adoption of the GDPR, personal data seem to be at the heart of every discussion about data in general, the focus of this study lays on the processing83 of non-personal data. This is not to underestimate
the delicate balance between commercial interests and individual privacy rights84, but just a choice to consider a
different and less known aspect of big data. We have already seen85 that among recent EU initiatives to foster the data
economy, attention is given to both types of data, as part of a certain and coherent legal framework.
Non-personal data is defined by art. 3 of the Regulation on the free-flow of non-personal data as “data other than personal data as defined in point (1) of Article 4 of Regulation (EU) 2016/679”86. Obviously in the case of a data set including both
personal and non-personal data, the rules of the GDRP apply for
83 “Processing’ means any operation or set of operations which is performed on
data or on sets of data in electronic format, whether or not by automated means, such as collection, recording, organisation, structuring, storage, adaptation or alteration, retrieval, consultation, use, disclosure by transmission, dissemination or otherwise making available, alignment or combination, restriction, erasure or destruction” art. 3(2) Regulation (EU) 2018/1807, supra.
84 O. TENE, J. POLONETSKY, Privacy in the Age of Big Data: a Time for Big Decisions,
in Stanford Law Review Online, 64, 2012, 63-69.
85 See above section 3.2.
86 For comparison the definition of personal data under Art. 4(1) GDPR: “personal
data’ means any information relating to an identified or identifiable natural person (‘data subject’); an identifiable natural person is one who can be identified, directly or indirectly, in particular by reference to an identifier such as a name, an
identification number, location data, an online identifier or to one or more factors specific to the physical, physiological, genetic, mental, economic, cultural or social identity of that natural person”.
30 the non-personal part87. Non-personal data, different from
personal information, is often defined as “industrial data”, meaning data created and processed in a commercial context. For instance, the manufacturing, production, shipping, transport, aeronautical sectors represent important sources for the creation of a huge amount of industrial data88.
Non-personal data does not enable the identification of an individual person, either because the personal data is anonymised or because it is not referable to an individual from the beginning89. In relation to the latter category, usually such
originally not personal information is machine-generated data, although if directly linked to a person it can still fall under personal data protection as well, such as in the case of IoT collecting data from a registered user90. Thus, as far as this study
concerns, the term big data it is mostly related to machine-generated data, simply because it is predominant in volume. The so-called “intelligent products” are at the centre of the 4.0 industrial revolution, consequently the biggest amount of data is created by computer processes, products or services or by sensors, without human intervention91.
87 Art. 2(2)Regulation (EU) 2018/1807, supra.
88 J. RITTER, A. MAYER, Regulating Data as Property: A New Construct for Moving
Forward, Duke Law and Technology Review, 16, 2018, p.222.
89 J. CEVRIZ, Two Roads Diverged in the Woods: on non-personal data as a legal
category in the EU, KU Leuven CiTip blog, 2017, retrievable from
https://www.law.kuleuven.be/citip/blog/two-roads-of-data-diverged-in-the- woods-and-i-shall-address-the-non-personal-one-on-non-personal-data-as-a-legal-category-in-the-eu/.
90Ibid.
91 Max-Planck-Institut für Innovation und Wettbewerb, Argumente Gegen ein
31 The category of big data related to non-personal data has not been fully shaped yet, except from a generic framework. As the amount of such type of data is steadily increasing, also the legal perspective needs to be considered. Legal uncertainty is never desirable and indeed there have been some calls for regulation of this topic92. Who owns non-personal data? Should they be
regulated and if yes, how? Usually, non-personal data are objects of contractual agreements: the service providers keep the data secret and enable distribution through licensing93. If the desirable
outcome should be an environment of strong protection for industrial big data, then further legal analysis might be helpful to achieve this objective. We can tackle the problem from two different angles: the first one is to consider whether existing intellectual property measures are fit for purpose. Alternatively, data producers’ ownership rights represent another possible legal response to the issue. The topic is shaped: the above-mentioned questions represent the conclusion of this general overview and at the same time represent the starting point for the next chapters of this study.
https://www.ip.mpg.de/fileadmin/ipmpg/content/forschung/Argumentarium_Dat eneigentum_de.pdf.
92 J. RITTER, A. MAYER, Regulating Data as Property: A New Construct for Moving
Forward, supra.
93 J. CEVRIZ, Two Roads Diverged in the Woods: on non-personal data as a legal
32
CHAPTER 2
THE INTELLECTUAL PROPERTY TEST
1. Contextualising the problem
1.1. Big data and intellectual property
Data is often referred to as the new oil of the 21th century. Despite the undiscussed economic value, data has completely different characteristics than material assets, such as oil94. This
might lead to confusion as far as the regulatory framework concerns. Both are valuable resources, but in comparison to oil, data is not a scarce resource. The increasing amount of data potentially encounters only limits in relation to the storage capacity of computers, which is continuously increasing95.
94 Max-Planck-Institut für Innovation und Wettbewerb, Argumente Gegen ein
„Dateneigentum“: 10 Fragen und Antworten, supra.
95 L. H. SCHOLZ, Big Data is not Big Oil: The Role of Analogy in the Law of New
Technology, FSU College of Law, Public Law Research Paper n.895, FSU College of
Law, Law, Business & Economics Paper n. 18-12, 2018, online available at
https://ssrn.com/abstract=3252543 , throughout the article the author outlines that the analogy of data as oil should be considered carefully, as it has been mainly
33 Data are non-rivalrous, meaning that once access is provided, they can be used by an undefined number of users and in several different ways96, since data can easily be reproduced without
altering its properties. Moreover, data are non-fungible and are related to actions of businesses and other actors in society, whereas oil is clearly fungible and a natural resource97. The Big
Data phenomenon, and therefore industrial data as the biggest part of it, do not involve the same economic and infrastructural issues concerning a material asset like oil. In sum, data are easy and cheap to produce. In line with these features, we have already seen that the EU has embodied big data in its strategy to promote a data driven economy98. Certainly, the basis for this
innovative economic model is the commercialisation of data, which inevitably leads to questions related to the regulatory framework and discussion about who owns the bespoken industrial data99.
The first regulatory response that comes into mind when considering again the above-mentioned characteristics is the existing intellectual property regime. The aim of this chapter will
developed by industry experts and it might be misleading for legal professionals. If analogies are to be used than to represent the different issues concerning big data.
96 Ibid. 97 Ibid.
98 See supra Chapter 1, par. 3.2.
99 J. DREXL, Designing Competitive Markets for Industrial Data: Between
Propertisation and Access, in JIPITEC, 8, 2017, 257 ss., “Obviously, this new data
economy has to rely on the commercialisation of data. But what kind of regulation is needed in order to make the data economy work? Do we need new ownership rights in data? Or should regulation focus on access in order to make data as widely available as possible? The European Commission is currently trying to formulate answers to these questions”.
34 be to test some of the most suitable IP rights and see if industrial data can fit into one of those categories100.
From a general point of view, intellectual property represents a wide subject matter with the main focus on protecting creative effort and commercial reputation101. IPR
regimes protect different types of works, products or knowledge with a variety of legal tools102. One of the main reasons justifying
intellectual property is the presence of a strong legal environment, which enables the production and publication of profitable works and encourages further dissemination of information and knowledge103.
Due to its role IPRs play an important role also in relation to new technological discoveries. Economic growth and the development of a highly performing technological environment go hand in hand. Therefore, IP provides useful tools through which public policy measures can create incentives for creating knowledge and foster innovation104. Indeed, for a long period of
time intellectual property has been considered the paramount system for adequate support and protection of new technological
100This is the approach adopted in almost every written piece on this yet
unexplored topic, which will be analysed further on. The categories considered are mainly database protection, trade secrets and sometimes patents.
101 D. I. BAINBRIDGE, Intellectual Property,6th Edition, Edinburgh, Pearson, 2007, 1.
102 E. HARISON, Intellectual Property Right, Innovation And Software Technologies:
The Economics of Monopoly Rights and Knowledge Disclosure, Cheltenham, Edward
Elgar Publishing, 2008, 6.
103 D. I. BAINBRIDGE, Intellectual Property, supra, 17, the author also underlines
that IPRs entitle to rights partly based on morality and partly based on the concept of. Whereas some countries, such as the UK, mostly recognise the economic reward, in other countries much more emphasis is given to moral aspects.
104 R. C. BIRD, S. C. JAIN, The Global Challenge of Intellectual Property Rights,
35 developments. However, over the years it has been questioned whether emerging technological markets should be governed by new or existing IPRs or whether regulation might be left to market powers and the invisible hand105. New technologies lay
on widespread knowledge, thus some legal and economic scholars express some concerns about the grant of monopolistic rights to inventors and the capability of IPRs to ensure sustainable industrial development106. It is unquestioned that
especially the digital revolution is challenging the field of intellectual property. Innovative progress and technological growth are both a source for new opportunities and for destabilisation of well-known IP categories107. The debate about
legal protection of non-personal data is by no means less detrimental to the stability of those categories.
1.2. Big data and market failures
The economic perspective gives insight to some problematic aspects concerning information and knowledge goods, due to the common ground they share with public goods108. Public
105 E. HARISON, Intellectual Property rights, innovation and software technologies,
supra, 31
106 Ibid, 60.
107 R. LALLEMENT, Intellectual Property and Innovation Protection: New Practices
and New Policy Issues, 1st edition, Wiley, 2017, 63 underlining that “the digital
revolution probably represents the common ground and the catalysing agent of these new challenges”.
36 goods are commonly characterised by two main features: non-rivalry and non-excludability.
A good is non-rival when the amount available for others is not reduced after someone’s consumption109. Non-excludability
refers to the fact that it is not possible to exclude others from enjoying the good, although they might not financially invest in the good itself110. The tricky point is that often to determine the
value of any invention it is necessary to disclose the content and thus, without adequate protection, anyone could appropriate and benefit from that information111. As a consequence, when
someone is creating knowledge and imitators can nevertheless benefit from it, then the value of private investment is certainly lower, and this might lead to underinvestment issues in innovation112. Here is where intellectual property comes into
play. By addressing the issues of appropriation and by recognising the risk of investments in innovation, IP regimes limit access to certain works or products and confer exclusive rights to individuals or organisations113.
109 G. S. MADDALA, E. MILLER, Microeconomics: theory and application, Singapore,
McGraw-Hill, 1989, 544. See also on the same issue H.R. VARIAN, Microeconomic
Analysis, 3rd edition, London New York, Norton, 1992, 415 ss.
110 Ibid., 545 in relation to the free-rider problem: “since it costs nothing to provide
the good to each additional customer after the first, social welfare is maximised by giving away the good for free. This leads to the free-rider problem: every individual wants to get a free ride and does not want to pay for the provision of the good because it is possible to get it free once someone else pays for the provision”.
111 R. LALLEMENT, Intellectual Property and Innovation Protection: New Practices
and New Policy Issues, supra, 3.
112 Ibid.
113 Ibid. “therefore, it is a matter of protecting these activities by making it possible
to reap the resulting benefits, all the more so as creating knowledge by investing in innovation is a risky activity with unpredictable returns, which occasionally entails extremely high costs”.
37 How is all the these linked to big data and especially to our main topic industrial data? At the beginning of this chapter, in order to distinguish data from tangible assets, we have outlined that data as well is non-rivalrous once used by someone114. Thus,
information, ideas and data are non-rival. Many people can use the same data with the same information anytime, without loss of its properties115. The digital revolution enables rapid and easy
copying and dissemination of digital content, thus weakening also exclusion barriers116. Reduced strength of excludability
barriers is an issue affecting the digital technology environment, in the same way it affects the discussion about public goods and here again we have questions about ownership and free riding117.
So, a common view of data is that it is both rival and non-excludable.
However, the topic can also be approached differently. In relation to rivalry “we have a similar situation as with innovations and many creative works, which justifies discussing data property in analogy to intellectual property instead of physical property”118. Thus, non-rivalry is always a fixed point,
but beside this similarity, the characteristic of non-excludability
114 See supra p. 26.
115 N. DUCH-BROWN, B. MARTENS and F. MUELLER-LANGER, The Economics of
Ownership, Access and Trade in Digital Data, Digital Economy Working Paper: JRC
Technical Reports, 2017(01), 13.
116 Ibid.
117 Ibid. “Just like low-cost printing technology triggered demand for copyright
protection of writers6, low-cost digital information technology raised questions about data free-riding and the protection of ownership”.
118 W. KERBER, A New (Intellectual) Property Right for Non-Personal Data? An
Economic Analysis, Joint Discussion Paper Series in Economics, 2016, 3, online
