Abstract
We present a systematic literature review based on bibliometric analysis to clarify the role of data governance in sustainable development. We made a concept-centric review of 35 relevant papers (out of an initial set of 2214) selected from Scopus and Web of Science and classified them into (1) sector-specific, (2) causal relationships and approaches, (3) data accessibility for sustainable development, and (4) smart contexts. Our contribution includes a conceptual framework for sustainable data governance in product lifecycles. Pursuing data-driven sustainability requires actions in structure, processes, and relational mechanisms. Data attributes (e.g., privacy, immutability, permissions, fairness), scope of data to be covered, and supporting technology are increasingly important to reduce all forms of waste while ensuring a long-term strategy to generate sustainable value from data.
Paper Type
Full Paper
DOI
10.62036/ISD.2022.44
Sustainable Data Governance: A Systematic Review and a Conceptual Framework
We present a systematic literature review based on bibliometric analysis to clarify the role of data governance in sustainable development. We made a concept-centric review of 35 relevant papers (out of an initial set of 2214) selected from Scopus and Web of Science and classified them into (1) sector-specific, (2) causal relationships and approaches, (3) data accessibility for sustainable development, and (4) smart contexts. Our contribution includes a conceptual framework for sustainable data governance in product lifecycles. Pursuing data-driven sustainability requires actions in structure, processes, and relational mechanisms. Data attributes (e.g., privacy, immutability, permissions, fairness), scope of data to be covered, and supporting technology are increasingly important to reduce all forms of waste while ensuring a long-term strategy to generate sustainable value from data.
Recommended Citation
Machado Ribeiro, V. H., Barata, J., & da Cunha, P. R. (2022). Sustainable Data Governance: A Systematic Review and a Conceptual Framework. In R. A. Buchmann, G. C. Silaghi, D. Bufnea, V. Niculescu, G. Czibula, C. Barry, M. Lang, H. Linger, & C. Schneider (Eds.), Information Systems Development: Artificial Intelligence for Information Systems Development and Operations (ISD2022 Proceedings). Cluj-Napoca, Romania: Risoprint. ISBN: 978-973-53-2917-4. https://doi.org/10.62036/ISD.2022.44