The Palgrave Encyclopedia of Interest Groups, Lobbying and Public Affairs

Living Edition
| Editors: Phil Harris, Alberto Bitonti, Craig S. Fleisher, Anne Skorkjær Binderkrantz

Data Collaboratives

Living reference work entry
DOI: https://doi.org/10.1007/978-3-030-13895-0_92-1
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Definition

Data collaboratives are an emerging form of public-private partnership in which actors from across sectors exchange and analyze data, or provide data science insights and expertise to create new public value and generate fresh insights (Verhulst & Sangokoya, 2015). Data collaboratives, sometimes referred to as “corporate data philanthropy” (Taddeo, 2017), can be considered a new form of corporate social responsibility in the data age (Verhulst, 2017).

Introduction

The rise of the open data movement means that a growing amount of data is today being broken out of information silos and released or shared with third parties. Yet despite the growing accessibility of data, there continues to exist a mismatch between the supply of, and demand for, data (Verhulst & Young, 2018). This is because supply and demand are often widely dispersed – spread across government, the private sector, and civil society – meaning that those who need data do not know where to find it, and those who...

Keywords

Data Data sharing Data for good Open data Corporate social responsibility Public-private partnerships Collaboration 
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References

  1. Adler, N., Cattuto, C., Kalimeri, K., Paolotti, D., Tizzoni, M., Verhulst, S., et al. (2019). How search engine data enhance the understanding of determinants of suicide in India and inform prevention: Observational study. Journal of Medical Internet Research, 21, e10179.Google Scholar
  2. Alemanno, A. (2018). Data for good: Unlocking privately-held data to the benefit of the many. European Journal of Risk Regulation. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3194040.
  3. Buchanan, J., & Kock, N. (2001). Information overload: A decision making perspective. In M. Köksalan & S. Zionts (Eds.), Multiple criteria decision making in the new millennium. Lecture notes in economics and mathematical systems (Vol. 507). Heidelberg/Berlin: Springer.Google Scholar
  4. De Meersman, F., Seynaeve, G., Debusschere, M., Lusyne, P., Dewitte, P., Baeyens, Y., et al. (2016). Assessing the quality of mobile phone data as a source of statistics. In European conference on quality in official statistics. https://ec.europa.eu/eurostat/cros/system/files/assessing_the_quality_of_mobile_phone_data_as_a_source_of_statistics_q2016.pdf
  5. de Montjoye, Y., Gambs, S., Blondel, V., Canright, G., de. Cordes, N., Deletaille, S., et al. (2018). On the privacy-conscientious use of mobile phone data. Scientific Data, 5, 180286.  https://doi.org/10.1038/sdata.2018.286.
  6. de Montjoye, Y., Kendall, J., & Kerry, C. F. (2014). Enabling humanitarian use of mobile phone data. Brookings. https://www.brookings.edu/research/enabling-humanitarian-use-of-mobile-phone-data/
  7. Gauvin, L., Tizzoni, M., Piaggesi, S., Young, A., Adler, N., Verhulst, S., et al. (2020). Gender gaps in urban mobility. Nature Humanities and Social Science Communications, 7. https://www.nature.com/articles/s41599-020-0500-x.
  8. Hajian, S., Bonchi, F., & Castillo, C. (2016). Algorithmic bias: From discrimination discovery to fairness-aware data mining. In KDD '16: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. https://dl.acm.org/doi/abs/10.1145/2939672.2945386
  9. Klievink, B., van der Voort, H., & Veeneman, V. (2018). Creating value through data collaboratives. Information Policy, 23, 379–397.Google Scholar
  10. O’Hara, K. (2019). Data trusts: Ethics, architecture and governance for trustworthy data stewardship. https://www.researchgate.net/publication/331275252_Data_Trusts_Ethics_Architecture_and_Governance_for_Trustworthy_Data_Stewardship/
  11. OECD. (2019). Enhancing access to and sharing of data: Reconciling risks and benefits for data re-use across societies. https://www.oecd.org/sti/ieconomy/enhancing-access-to-and-sharing-of-data-276aaca8-en.htm
  12. Solove, D. J., & Citron, D. K. (2017). Risk and anxiety: A theory of data-breach harms. Texas Law Review. https://heinonline.org/HOL/LandingPage?handle=hein.journals/tlr96&div=28&id=&page=
  13. Susha, I., Janssen, M., & Verhulst, S. (2017). Data collaboratives as ‘bazaars’? A review of coordination problems and mechanisms to match demand for data with supply. Transforming Government: People, Process and Policy, 11, 157–172.CrossRefGoogle Scholar
  14. Taddeo, M. (2017). Data philanthropy and individual rights. Minds & Machines, 27, 1–5.CrossRefGoogle Scholar
  15. Verhulst, S. (2017). Corporate social responsibility for a data age. Stanford Social Innovation Review. https://ssir.org/articles/entry/corporate_social_responsibility_for_a_data_age#
  16. Verhulst, S., & Sangokoya, D. (2015). Data collaboratives: Exchanging data to improve people’s lives. Medium. https://medium.com/@sverhulst/data-collaboratives-exchanging-data-to-improve-people-s-lives-d0fcfc1bdd9a
  17. Verhulst, S., & Young, A. (2018). How the data that internet companies collect can be used for the public good. Harvard Business Review. https://hbr.org/2018/01/how-the-data-that-internet-companies-collect-can-be-used-for-the-public-good
  18. Verhulst, S., Young, A., & Srinivasan, P. (2016). An Introduction to Data Collaboratives: Creating Public Value by Exchanging Data. https://datacollaboratives.org/static/files/data-collaboratives-intro.pdf
  19. Verhulst, S, Young, A., Winowatan, M., & Zahuranec, A. J. (2019). Leveraging private data for public good: A descriptive analysis and typology of existing practices. https://datacollaboratives.org/existing-practices.html
  20. Young, A., & Verhulst S. (2016). Battling Ebola in Sierra Leone: Data sharing to improve crisis response. Open data’s impact. http://odimpact.org/case-battling-ebola-in-sierra-leone.html

Further Reading

  1. Beckwith, R., Sherry, J., Pendergast, D. (2018). Data flow in the Smart City: Open data versus the commons. In M. de Lange & M. de Waal (Eds.), The Hackable City: Digital media and Collaborative City-making in the network society (pp. 205–221). Singapore: Springer.Google Scholar
  2. High-Level Expert Group on Business-to-Government Data Sharing. (2020). Towards a European strategy on business-to-government data sharing for the public interest. https://www.euractiv.com/wp-content/uploads/sites/2/2020/02/B2GDataSharingExpertGroupReport-1.pdf
  3. King, G., & Persily, N. (2019). A new model for industry-academic partnerships. PS: Political science and politics. Publisher’s Version Copy at https://j.mp/2q1IQpH
  4. World Economic Forum. (2019). Data collaboration for the common good: Enabling trust and innovation through public-private partnerships. https://www.weforum.org/reports/data-collaboration-for-the-common-good-enabling-trust-and-innovation-through-public-private-partnerships
  5. Young, M., Rodriguez, L., Keller, E., Sun, F., Sa, B., Whittington, J., et al. (2019). Beyond open vs. closed: balancing individual privacy and public accountability in data sharing. In FAT* '19: Proceedings of the conference on fairness, accountability, and transparency. Association for Computing Machinery, pp 191–200,  https://doi.org/10.1145/3287560.3287577

Authors and Affiliations

  1. 1.The GovLabNew York UniversityNew YorkUSA
  2. 2.Governance LaboratoryNew YorkUSA

Section editors and affiliations

  • Alberto Bitonti
    • 1
  1. 1.Università della Svizzera ItalianaLuganoSwitzerland