Skip to main content

Advertisement

Log in

Data Governance: Enhancing Innovation and Protecting Against Its Risks

  • Forum
  • Published:
Intereconomics

Abtract

Big Data is expected to unleash data-driven innovation,which is supposed to better address and solve challengesin our society.

As a so-called non-rival good, the sharingand re-using of data by one actor does not diminish its valuefor other actors and can create significant spillover effects.

Data is still often stored in data silos. Releasing data fromsilos and sharing it may enhance social and economic welfare.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. See V. Mayer-Schönberger, K. Cukier: Big Data: A Revolution that Will Transform How We Live, Work, and Think, New York 2013, Houghton Mifflin Harcourt Publishing Company, p. 30.

    Google Scholar 

  2. See M. Hilbert: Big Data for Development: A Review of Promises and Challenges, in: Development Policy Review, Vol. 34, No. 1, 2016, pp. 135–174, p. 142.

    Google Scholar 

  3. OECD: Data-Driven Innovation: Big Data for Growth and Well-Being, Paris 2015, OECD Publishing, p. 179.

  4. See M. von Grafenstein, J. Holzel, F. Irgmeier, J. Pohle: Nudging: Regulierung durch Big Data und Verhaltenswissenschaften, Berlin 2018, ABIDA (Assessing Big Data), p. 46.

    Google Scholar 

  5. See A. Habenstein, S. D'Onofrio, E. Portmann, M. Sturmer, T. Myrach: Open Smart City: Good Governance fur smarte Stadte, in: A. Meier, E. Portmann (eds.):Smart City: Strategie, Governance und Projekte, Wiesbaden 2016, Springer Vieweg, pp. 47–71, p. 48.

    Google Scholar 

  6. Regarding lock-in-effects, see E. Veronelli: Smart cities vs “locked- in” cities, CORDIS EU research results, 12 September 2016, available at https://cordis.europa.eu/news/rcn/135237/en.

    Google Scholar 

  7. Regarding smart cities and surveillance, see T. Wadhwa: Smart Cities: Toward the Surveillance Society?, in: D. Araya (ed.): Smart Cities as Democratic Ecologies, New York 2015, Palgrave Macmillan, pp. 125–141.

    Book  Google Scholar 

  8. See W. Hoffmann-Riem, S. Fritzsche: Innovationsverantwor- tung - zur Einleitung, in: M. Eifert, W. Hoffmann-Riem (eds.): Innovation und Recht III - Innovationsverantwortung, Berlin 2009, Duncker & Humblot, p. 16.

    Google Scholar 

  9. See for example the smart city project in Toronto: J. Wakefield:The Google city that has angered Toronto, BBC News, 18 May 2019.

    Google Scholar 

  10. Regarding biomedical data, see J. L. Contreras: Leviathan in the Commons: Biomedical Data and the State, in: K.J. Strandburg, B.M. Frischmann, M.J. Madison (eds.): Governing Medical Knowledge Commons, Cambridge 2017, Cambridge University Press, pp. 19–45.

    Google Scholar 

  11. See, regarding the perspective of the legal regulator, A. Vofßkuhle: Neue Verwaltungsrechtswissenschaft, in: W. Hoffmann-Riem, E. Schmidt-Aßmann.A. Voßkuhle (eds.): Grundlagen des Verwal- tungsrechts - Band I: Methoden - Maßstäbe - Aufgaben - Organisation, 2nd edition, Munich 2012, C.H. Beck, cip. 20; and regarding the governance perspective, J. Hofmann, C. Katzenbach, K. Golatz: Between coordination and regulation: Finding the governance in Internet governance, in: New Media & Society, Vol. 19, No. 9, 2017, pp. 1406–1423.

    Google Scholar 

  12. See, regarding regulation by the state: A. VofSkuhle, op. cit.; and regarding regulation by private companies: J. Black: Decentring regulation: Understanding the role of regulation and selfregulation in a “post-regulatory” world, in: Current legal problems, Vol. 54, No. 1, 2001, pp.103–146

    Article  Google Scholar 

  13. see the term “private ordering” at Elkin-Koren who defines the term as a situation wherein “the rule-making process regarding the use of information is privatized, and the legal power to define the boundaries of public access to information is delegated to private parties.” N. Elkin-Koren: A Public-Regarding Approach to Contracting over Copyrights, in: R. Dreyfuss, H. First, D. Zimmerman (eds): Expanding the Boundaries of Intellectual Property: hnovation Policy for the Knowledge Society, Oxford 2001, Oxford University Press, pp. 191

    Google Scholar 

  14. 192 as cited by S. Dusollier: Sharing Access to Intellectual Property through Private Ordering, in: Chicago-Kent Law Review, Vol. 82, 2007, pp. 1391, 1393, fn. 8

    Google Scholar 

  15. see S. Schwarcz: Private ordering, in: Northwestern University Law Review, Vol. 97, No. 1, 2002, p. 319.

    Google Scholar 

  16. Regarding the privacy paradox, see for example G. Müller, C. Flender, M. Peters: Vertrauensinfrastruktur und Privatheit als ökonomische Fragestellung, in:J.Buchmann (ed.): Internet Privacy: Eine multidisziplinäre Bestandsaufnahme, Heidelberg 2012, Springer Vieweg, pp. 143–188, p. 175.

    Google Scholar 

  17. See H. Richter, P. R. Slowinski: The Data Sharing Economy: On the Emergence of New Intermediaries, in: IIC-lnternational Review of Intellectual Property and Competition Law, Vol. 50, No. 1, 2019, pp. 4–29, p. 7, fn 15, discussing the risk of liability of breaching data protection law.

    Google Scholar 

  18. Regarding coopetition, see D.R. Gnyawali, R. Madhavan, J. He, M. Bengtsson: The competitioncooperation paradox in inter-firm relationships: A conceptual framework, in: Industrial Marketing Man- agement, Vol. 53, 2016, pp. 7–18; sometimes, the data donor also overestimates the value of its data due to an endowment effect

    Google Scholar 

  19. see D. Kahneman, J. Knetsch, R. Thaler: Anomalies: The Endow- ment Effect, Loss Aversion, and Status Quo Bias, in: Journal of Eco- nomic Perspectives, Vol. 5, No. 1, 1991, pp. 193–206.

    Google Scholar 

  20. Regarding transaction costs in law in general, see R.H. Coase: The problem of social cost, in: The Journal of Law and Economics, Vol. 111, 1960, pp. 1–44.

    Google Scholar 

  21. Bounded rationality, such as unwarranted fear of failing to comply with data protection law, may also contribute to the reluctance to share data, see, for example H.A. Simon: Bounded rationality and organizational learning, in: Organization science, Vol. 2, No. 1, 1991, pp. 125–134.

    Google Scholar 

  22. Finally, excessive transaction costs may lead to a socalled tragedy of the anti-commons, see M.A. Heler: The Tragedy of the Anticommons: Property in the transition from Marx to markets, in: Harvard Law Review, Vol. 111, No. 3, 1998, pp. 621–688

    Google Scholar 

  23. and regarding patents, M.A. Heller, R.S. Eisenberg: Can patents deter innovation? The anticommons in biomedical research, in: Science, Vol. 280, No. 5364, 1998, pp. 698–701.

    Google Scholar 

  24. See similar reasons mentioned by V. Kathuria: Greed for data and exclusionary conduct in data-driven markets, in: Computer law & security review, Vol. 35, No. 1, 2019, pp. 89–102.

    Google Scholar 

  25. See M. von Grafenstein: The Principle of Purpose Limitation in Data Protection Laws, Baden-Baden 2018, Nomos, p. 77.

    Book  Google Scholar 

  26. Regarding decision heuristics, see S. Mousavi, G. Gigerenzer: Risk, uncertainty, and heuristics, in: Journal of Business Research, Vol. 67, No. 8, 2014, pp. 1671–1678.

    Google Scholar 

  27. See M. Olson: Collective action, in: S.N. Durlauf, L.E. Blume:The New Palgrave Dictionary of Economics, 2nd edition, London 2008, Palgrave Macmillan, pp. 876–880.

    Google Scholar 

  28. Regarding the rationale of Art. 20 GDPR, see for example T. Jülicher, C. Rottgen, M. von Schönfeld: Das Recht auf Datenubertragbarkeit: Ein datenschutzrechtliches Novum, in: Zeitschrift für Dat- enschutz, Vol. 6, No. 8, 2016, pp. 358–362.

    Google Scholar 

  29. R. H. Weber: Improvement of Data Economy Through Compulsory Licences?, in:S. Lohsse, R. Schulze, D. Staudenmayer: Trading Data in the Digital Economy: Legal Concepts and Tools, Baden-Baden 2017, Nomos.p. 151.

    Google Scholar 

  30. See, for example, M. Grützmacher: Dateneigentumein Flickenteppich, in: Computer und Recht, Vol. 32, No. 8, 2016, pp. 485–495

    Google Scholar 

  31. N. Härting: “Dateneigentum” -Schutz durch Immaterialgüter- recht?, in: Computer und Recht, Vol. 32, No. 10, 2016, pp. 646–649; N. Jentzsch: Dateneigentum - Eine gute Idee für die Datenokönomie, Berlin 2018, Think Tank für die Gesellschaft in technologischen Wan- del, available at https://www.stiftung-nv.de/sites/default/files/nicola_jentzsch_dateneigentum.pdf}.

    Google Scholar 

  32. See H. Richter, R.M. Hilty: Die Hydra des Dateneigentums - eine methodische Betrachtung, Discussion Paper No. 12, Munich 2018, Max-Planck-Institut für Innovation und Wettbewerb, in: Stiftung Dat- enschutz (ed.): Dateneigentum und Datenhandel, Schriftenreihe Daten Debatten, Vol. 3, Berlin 2018, Erich Schmidt Verlag, pp. 241–259.

    Google Scholar 

  33. Regarding this last aspect, see again, ibid., p. 15.

  34. See A. Nahles: Digitaler Fortschritt durch ein Daten-für-Alle-Gesetz, Positionspapier der Parteivorsitzenden der Sozialdemokratischen Partei Deutschlands, Berlin 2019, available at https://www.spd.de/aktuelles/daten-fuer-alle-gesetz/}

    Google Scholar 

  35. See T. Ramge, V. Mayer-Schonberger: Das Digital: Markt, Wertschöpfung und Gerechtigkeit im Datenkapitalismus, 3rd edition. Berlin 2017, Ullstein.

    Google Scholar 

  36. Information on the project “Privacy by design in smart cities” available at https://www.hiig.de/en/project/privacy-by-design-in-smart-cities/

  37. Alexander von Humboldt Institute for Internet and Society: Data Protection by Design in Smart Cities, HUG Discussion Paper, forthcoming, available at https://www.hiig.de

  38. For more on “reasonable expectations”, see Article 29 Data Protection Working Party: Opinion 06/2014 on the notion of legitimate in- terests of the data controller under Article 7 of Directive 95/46/EC, 2014, p. 51; regarding risk expectations, see Art. 35 (9) GDPR: “Where appropriate, the controller shall seek the views of data subjects or their representatives on the intended processing, without prejudice to the protection of commercial or public interests or the security of processing operations.”

  39. See N. Purtova: The law of everything. Broad concept of persona data and future of EU data protection law, in: Law, Innovation and Technology, Vol. 10, No. 1, 2018, pp. 40–81.

    Google Scholar 

  40. See also W. Kerber: Digital markets, data, and privacy: competition law, consumer law and data protection, in: Journal of Intellectual Property Law & Practice, Vol. 11, No. 11, 2016, pp. 856–866.

    Google Scholar 

  41. M.A. Heller: The Tragedy of the Anticommons: Property in the transition from Marx to markets, in: Harvard Law Review, Vol. 111, No. 3, 1998, pp. 621–688

    Google Scholar 

  42. M.A. Heller, R.S. Eisenberg: Can patents deter nnovation? The anticommons in biomedical research, in: Science, Vol. 280, No.5364, 1998, pp. 698–701

    Google Scholar 

  43. R.P. Merges: Contracting into liability rules: Intellectual property rights and collective rights organi- zations, in: California Law Review, Vol.84, No.5, 1996, pp. 1293–1393

    Google Scholar 

  44. M.J. Barnett: The anti-commons revisited, in: Harvard Journal of Law & Technology, Vol.29, No. 1, 2015, pp. 127–203.

    Google Scholar 

  45. A. Acquisti, C. Taylor, L. Wagman: The economics of privacy, in: Journal of Economic Literature, Vol. 54, No. 2, 2016, pp. 442–92.

    Google Scholar 

  46. M.E. Stucke, A.P. Grunes: Big data and competition policy, Oxford 2016, Oxford University Press; N. Srnicek: Platform Capitalism, London 2017, Polity

    Google Scholar 

  47. V. Kathuria: Greed for data and exclusionary conduct in data-driven markets, in: Computer law & security review, Vol.35, No. 1, 2019, pp. 89–102.

    Google Scholar 

  48. H.W. Chesbrough: Open innovation: The new imperative for cre- ating and profiting from technology, Boston 2006, Harvard Business School Press

    Google Scholar 

  49. E. von Hippel: Democratizing innovation: The evolv- ng phenomenon of user innovation, in: Journal für Betriebswirtschaft, Vol. 55, No. 1, 2005, pp. 63–78

    Google Scholar 

  50. E. von Hippel, G. von Krogh: Open source software and the “privatecollective” innovation model: Issues for organization science, in: Organization Science, Vol. 14, No. 2, 2003, pp. 209–223.

    Google Scholar 

  51. R.B. Bouncken, J. Gast, S. Kraus, M. Bogers: Coopetition: a systematic review, synthesis, and future research directions, in: Review of Managerial Science, Vol. 9, No. 3, 2015, pp. 577–601.

    Google Scholar 

  52. See, for example, G. van Overwalle, E. van Zimmeren, B. Verbeure, G. Matthijs: Models for facilitating access to patents on ge- netic inventions, in: Nature Reviews Genetics, Vol. 7, No. 2, 2006, pp. 143–148

    Google Scholar 

  53. M. Mattioli: The data-pooling problem, in: Berkeley Tech- nology Law Journal, Vol. 32, No. 1, 2017, pp. 179–236

    Google Scholar 

  54. B. Lundqvist: Competition and data pools, in: Journal of European Consumer and Market Law, Vol. 7, No. 4, 2018, pp. 146–154

    Google Scholar 

  55. M. Finck: Blockchains and theGDPR, in: European Data Protection Law Review, Vol. 4, 2018, pp. 17–35

    Google Scholar 

  56. H. Richter, P.R. Slowinski: The Data Sharing Economy: On the Emergence of New Intermediaries, in: HC-International Review of Intellectual Property and Competition Law, Vol. 50, No. 1, 2019, pp. 4–29.

    Google Scholar 

  57. See the proposed terminology and a first categorisation of interorganisational data governance models in Alexander von Humboldt Institute for Internet and Society: Data Governance: Towards a Conceptual Framework, HUG Discussion Paper, forthcoming, available at http://www.hiig.de/paper-Data-Governance-Towards-Conceptual-Framework/

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Max von Grafenstein.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

von Grafenstein, M., Wernick, A. & Olk, C. Data Governance: Enhancing Innovation and Protecting Against Its Risks. Intereconomics 54, 228–232 (2019). https://doi.org/10.1007/s10272-019-0829-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10272-019-0829-9

Navigation