The Potential and Practice of Data Collaboratives for Migration

Verhulst, Stefaan; Young, Andrew

doi:10.1007/978-3-030-12554-7_24

Cited by 12 publications

(10 citation statements)

References 13 publications

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“…With respect to the kind of value pursued, we see that DPS mainly focus on producing economic value, while other forms of value gradually ‘chime in’ in the remaining models, such as social change, public interest, fairness, and data subjects’ self-determination. It should also be noted that for the most part, these models could be found in niche initiatives or pilot projects, and there is still limited research concerning the value they generate and their sustainability over time (Borkin, 2019; Verhulst, 2019). Value production and redistribution, thus, can be assessed more at the level of the imaginary, than from evaluations of tangible outcomes.…”

Section: Discussionmentioning

confidence: 99%

Emerging models of data governance in the age of datafication

et al. 2020

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The article examines four models of data governance emerging in the current platform society. While major attention is currently given to the dominant model of corporate platforms collecting and economically exploiting massive amounts of personal data, other actors, such as small businesses, public bodies and civic society, take also part in data governance. The article sheds light on four models emerging from the practices of these actors: data sharing pools, data cooperatives, public data trusts and personal data sovereignty. We propose a social science-informed conceptualisation of data governance. Drawing from the notion of data infrastructure we identify the models as a function of the stakeholders’ roles, their interrelationships, articulations of value, and governance principles. Addressing the politics of data, we considered the actors’ competitive struggles for governing data. This conceptualisation brings to the forefront the power relations and multifaceted economic and social interactions within data governance models emerging in an environment mainly dominated by corporate actors. These models highlight that civic society and public bodies are key actors for democratising data governance and redistributing value produced through data. Through the discussion of the models, their underpinning principles and limitations, the article wishes to inform future investigations of socio-technical imaginaries for the governance of data, particularly now that the policy debate around data governance is very active in Europe.

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Section: Discussionmentioning

confidence: 99%

Emerging models of data governance in the age of datafication

et al. 2020

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“…Specific to the use of mobile phone call detail record (CDR) data, the presence of multiple mobile network operators in a country mean that multiple data sharing agreements would be required (23). In addition, there are significant issues around the potential extraction of sensitive information contained in big data (24,25) and data sources are often fragmentated across disciplines which reduces the awareness of available datasets (26). Accounting for multiple biases and the complex analyses required to interpret the data are further examples of methodological difficulties associated with the use of big data (6,27).…”

Section: Accessing and Utilising Big Datamentioning

confidence: 99%

Key opportunities and challenges for the use of big data in migration research and policy

Franklinos

Parrish

Burns

et al. 2021

Preprint

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Migration is one of the defining issues of the 21st century. Better data is required to improve understanding about how and why people are moving, target interventions and support evidence-based migration policy. Big data, defined as large, complex data from diverse sources, has been proposed as a solution to help address current gaps in knowledge. The authors participated in a workshop held in London, UK, in July 2019, that brought together experts from the UN, humanitarian NGOs, policy and academia to develop a better understanding of how big data could be used for migration research and policy. We identified six key areas regarding the application of big data in migration research and policy: accessing and utilising data; integrating data sources and knowledge; understanding environmental drivers of migration; improving healthcare access for migrant populations; ethical and security concerns; and addressing political narratives. We advocate the need for increased cross-disciplinary collaborations to advance the use of big data in migration research whilst safeguarding vulnerable migrant communities.

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“…By our definition, a data trust is a legal, technical, and organizational structure for enabling the sharing of data (Walker et al, 2019); they can assist with the exchange of data for a variety of purposes, one of which is to help solve business or societal problems. In that, they differ from data collaboratives, which have the distinct goal to solve societal problems through collaboration between organizations from diverse sectors (Verhulst et al, 2015). For data trusts, as well as related structures such as data collaboratives, the design, development, and utilization of robust mechanisms for responsible data sharing are crucial to engender trust and ultimately drive forward data-driven innovation and achieve their organizational goals, regardless whether these are social or economic.…”

Section: Data Trustsmentioning

confidence: 99%

Data protection by design: Building the foundations of trustworthy data sharing

et al. 2020

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Data trusts have been conceived as a mechanism to enable the sharing of data across entities where other formats, such as open data or commercial agreements, are not appropriate, and make data sharing both easier and more scalable. By our definition, a data trust is a legal, technical, and organizational structure for enabling the sharing of data for a variety of purposes. The concept of the “data trust” requires further disambiguation from other facilitating structures such as data collaboratives. Irrespective of the terminology used, attempting to create trust in order to facilitate data sharing, and create benefit to individuals, groups of individuals, or society at large, requires at a minimum a process-based mechanism, that is, a workflow that should have a trustworthiness-by-design approach at its core. Data protection by design should be a key component of such an approach.

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The Potential and Practice of Data Collaboratives for Migration

Cited by 12 publications

References 13 publications

Emerging models of data governance in the age of datafication

Emerging models of data governance in the age of datafication

Key opportunities and challenges for the use of big data in migration research and policy

Data protection by design: Building the foundations of trustworthy data sharing

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