Forms of data sharing

The value of data may not be maximised with one single instance of data sharing. Rather, sharing data unlocks so-called big data value chains,⁹ where large amounts of data are collated, processed or transformed in several related steps

This requires established frameworks in which data can be shared, not only once, but consistently. Walker et al. (2019) identify the following list of already established practices that enable a form of data sharing in different contexts:

• Data commons: Resources are held in common, accessible to all members of a group. This primarily occurs for medical and related interoperable data (and tools) between researchers,¹⁰ but also in the energy sector.¹¹
• Data collaboratives: Private data which benefits society and the environment is shared for social good.¹²
• Data marketplaces: Intermediary platforms or online stores through which data can be bought or sold.¹³
• Data trusts: There is no one definition of what a data trust is (yet). As a working definition, O’Hara suggests that data trusts work within the law to provide ethical, architectural and governance support for trustworthy data processing.¹⁴ Data trusts can be for internal use only¹⁵ or to facilitate sharing externally, to support AI innovation¹⁶ or social good,¹⁷ or to protect citizens.¹⁸
• Open data: Data that is licensed and available for anyone to access, use and share for any purpose. Personal data can never be open data.

Data commons and data collaboratives are relatively well-defined concepts with specific aims. Data trusts and data marketplaces are more fluidly defined but are appropriate to a wider range of industry sectors and aims.

Case study: Dawex, a data marketplace

Dawex is a leading data marketplace, which allows organisations to make their data available for purchase under licence. They believe marketplaces will accelerate data sharing, because they make it scalable and affordable.

Like all markets, Dawex brings liquidity – it provides a forum for matching offer and demand. In fact they see themselves as a kind of ‘AirBnB for Data’. Like AirBnB’s rooms, some data sets are not sold at all and some are very popular. Also like AirBnB, Dawex stimulates supply and demand.

In the data marketplace, data purchasers (users) are more mature than data suppliers, often because they are organisations that have always needed more data – such as journalists, or companies seeking new customers. Key drivers for suppliers to share data are not only the creation of new revenue streams, but often also corporate social responsibility.

References

9 Curry, E (2016): The Big Data Value Chain: Definitions, Concepts, and Theoretical Approaches. In: Cavanillas J, Curry E, Wahlster W (eds) New Horizons for a Data-Driven Economy. Springer, Cham ^{Back to text}

10 Grossman, R. (2016) How Data Commons Are Changing the Way We Share Research Data and Make Discoveries: The Open Commons Consortium Perspective. NSF Data Science Seminar July 6, 2016 ^{Back to text}

11 https://lo3energy.com ^{Back to text}

12 Noveck, B (2016): Data Collaboratives: Sharing Public Data in Private Hands for Social Good, Forbes ^{Back to text}

13 Carnelley, P, Schwenk, H, Cattaneo, G, Micheletti, G, and Osimo, D. (2016). Europe’s Data Marketplaces – Current Status and Future Perspectives. IDC ^{Back to text}

14 O’Hara, K (2019): Data Trusts: Ethics, Architecture and Governance for Trustworthy Data Stewardship. Web Science Institute White Paper ^{Back to text}

15 e.g. https://hazy.com/, https://www.truata.com/ ^{Back to text}

16 Hall, W & Pesenti, J (2017): Growing the artificial intelligence industry in the UK. ^{Back to text}

17 Hardinges, J (2018): What is a data trust. Open Data Institute ^{Back to text}

18 https://sidewalklabs.com ^{Back to text}