Data work in context: Value, risks, and governance
Open accessAbstract
While always integral to scientific activity, data work has recently emerged as a key set of processes within societal activities of all kinds. While data work presents new opportunities for discovery, value creation, and decision making, its emergence also raises significant ethical issues, including those of ownership, privacy, and trust. This article presents a review of data work, and how negotiating a trade‐off between its value and risks requires locating its processes within the contexts of its conditions and consequences. These include international, national, and sectoral conditions of law, policy, and regulation at a macro level; organizational conditions of information and data governance that aim to address the value and risks of data work at a meso level; along with attention to the everyday contexts of data and information handling by data information and other professionals at a micro level. In conclusion, a conceptual framework is presented that locates the processes of data work within the matrix of its macro meso and micro conditions, its consequences for individuals, organizations, and society, and the relations between them. Suggestions are given for how research into the study of data work—its value risks and governance— can be advanced by using this framework.
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Data governance is important for creating value and moderating risk in data science initiatives.
“Data governance is important for creating value and moderating risk in data science initiatives (Foster et al. 2018)”
From Trusted Decision-Making: Data Governance for Creating Trust in Data Science Decision Outcomes (2020) · cites this paper as Foster et al. 2018
The 'Data work at a meso level' section says information and data governance initiatives address the balance between the value and risks of data work, and 'Data governance: value and accountability' links governance to realizing value from data. The Introduction says the review primarily considers data work outside the immediate domain of science, so it does not specifically establish the claim for data science initiatives. Page numbers were not provided in our copy's passages. (LLM, conf=0.83)
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Cited by
Sorted by citations
Zhang, D., Pee, L. G., & Cui, L. (2021) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2020.102304 · Cited by 228 · see citing works
Trusted Decision-Making: Data Governance for Creating Trust in Data Science Decision Outcomes
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“Data governance is important for creating value and moderating risk in data science initiatives (Foster et al. 2018; Jones et al. 2019), as it can help organizations make use of data as a competitive asset (Morabito 2015).”
Background
- Authors
- Foster, J., McLeod, J., Nolin, J., & Greifeneder, E.
- Affiliations
- University of Sheffield (GB)
- Northumbria University (GB)
- University of Borås (SE)
- Humboldt-Universität zu Berlin (DE)
- Corresponding author
- Foster, Jonathan
- Venue
- Journal of the Association for Information Science and Technology
- Publisher
- Wiley
- Published
- 2018-11-19
- Volume
- 69
- Issue
- 12
- Pages
- 1414-1427
- ISSN
- 2330-1635 (ISSN-L)
- 2330-1643
- OpenAlex
- W2884324956
- Cited by
- 40
- References
- 94
- Citations by year
- 2026: 6 · 2025: 5 · 2024: 9 · 2023: 3 · 2022: 5 · 2021: 4 · 2020: 6 · 2019: 2
- FWCI
- 5.98
- Type
- journal article
- Language
- en
- Open access
- Locations
- Journal of the Association for Information Science and Technology (publishedVersion)
- White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (acceptedVersion)
- Northumbria Research Link (Northumbria University) (submittedVersion)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Law › Digitalization, Law, and Regulation
- Physical Sciences › Computer Science › Information Systems › Research Data Management Practices
- Keywords
- data work, data governance, data ethics, data ownership, privacy, trust, data regulation, information governance, conceptual framework, data professionals
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