(Re)making data markets: an exploration of the regulatory challenges

Taylor, Lance; Mukiri‐Smith, Hellen; Petročnik, Tjaša; Savolainen, Laura; Martin, Aaron

doi:10.1080/17579961.2022.2113671

Cited by 4 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Much of the recent technological development has been driven by giant corporations that retain the power to decide the areas of innovation, having as their core value the pursuit of economic gains (Birch, Chiappetta, & Artyushina, 2020;Taylor, 2021;Viljoen, 2021). In this sense, the question of the purpose of data-driven innovation underlines the discussion about increased access to data and improved data-sharing conditions (Taylor et al, 2022).…”

Section: The Concept Of Data Governance and Its Dominant Modelsmentioning

confidence: 99%

“…However, in the current state of affairs, data and the technological capacity to explore it are concentrated in the hands of a few actors and benefit first and foremost powerful private groups pursuing exclusively economic interests (Micheli, Ponti, Craglia, & Berti Suman, 2020). This situation, also referred to as the platform economy, reinforces the power imbalance in modern societies and hampers innovation (Pistor, 2020; Petročnik, Savolainen, & Martin, 2022). Moreover, the most important data uses are linked with targeted advertising, personalised insurance premiums and credit score ratings (Ohm & Frankle, 2018;Viljoen, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…Although significant from an economic point of view, these applications are of limited relevance to improving social welfare (Viljoen, Goldenfein, & McGuigan, 2021). In this context, the concentration of technological capacity limits the bottom-up processes of social innovation through which civil society, social entrepreneurs and small businesses tackle social needs that are generally not addressed by the market or by players motivated solely by profit (Taylor et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Managing public sector data: National challenges in the context of the European Union’s new data governance models

Buttow,

Weerts

2024

View full text Add to dashboard Cite

In its regulatory enterprise to improve the conditions of data sharing and reuse, the European Union has enacted new legislation: the Data Governance Act (DGA). The DGA envisages new forms of sharing public sector data (PSD). Based on a legal analysis of the DGA and an in-depth study of data governance literature, this paper highlights what is at stake in the new regulatory framework and argues that more than the mere openness of more PSD will be necessary to ensure that the European Union policy goals are achieved, especially those concerning enhancing innovation for the common good. From this perspective, the paper argues that the public data trust model of data governance and the Responsible Research and Innovation approach offer two powerful tools for public sector data governance. In this context, this paper contributes to the debate about new data governance models and discusses tools and frameworks enabling the use of data for the common good. It also provides insights to public administration practitioners aiming to implement a framework for increased and sustainable PSD sharing.

show abstract

Section: The Concept Of Data Governance and Its Dominant Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Managing public sector data: National challenges in the context of the European Union’s new data governance models

Buttow,

Weerts

2024

View full text Add to dashboard Cite

show abstract

High-Quality Data Collection in Data Markets

2024

SpringerBriefs in Service Science

View full text Add to dashboard Cite

Strategies for data supply in high-granularity data trade in smart cities

Palviainen,

Kotovirta

2024

Environ Syst Decis

View full text Add to dashboard Cite

The smart city infrastructures, such as digital platforms, edge computing, and fast 5G/6G networks, bring new possibilities to use near-real-time sensor data in digital twins, AR applications, and Machine-to-Machine applications. In addition, AI offers new capabilities for data analytics, data adaptation, event/anomaly detection, and prediction. However, novel data supply and use strategies are needed when going toward higher-granularity data trade, in which a high volume of short-term data products is traded automatically in dynamic environments. This paper presents offering-driven data supply (ODS), demand-driven data supply (DDS), event and offering-driven data supply (EODS), and event and demand-driven data supply (EDDS) strategies for high-granularity data trade. Computer simulation was used as a method to evaluate the use of these strategies in supply of air quality data for four user groups with different requirements for the data quality, freshness, and price. The simulation results were stored as CSV files and analyzed and visualized in Excel. The simulation results and SWOT-analysis of the suggested strategies show that the choice between the strategies is case-specific. DDS increased efficiency in data supply in the simulated scenarios. There was higher profit and revenues and lower costs in DDS than in ODS. However, there are use cases that require the use of ODS, as DDS does not offer ready prepared data for instant use of data. EDDS increased efficiency in data supply in the simulated scenarios. The costs were lower in EODS, but EDDS produced clearly higher revenues and profits.

show abstract

(Re)making data markets: an exploration of the regulatory challenges

Cited by 4 publications

References 0 publications

Managing public sector data: National challenges in the context of the European Union’s new data governance models

Managing public sector data: National challenges in the context of the European Union’s new data governance models

High-Quality Data Collection in Data Markets

Strategies for data supply in high-granularity data trade in smart cities

Contact Info

Product

Resources

About