Data has become a critical resource for organizations and society. Yet, it is not always as valuable as it could be since there is no well-defined approach to managing and using it. This article explores the increasing importance of global data governance due to the rapid growth of data and the need for responsible data use and protection. While historically associated with private organizational governance, data governance has evolved to include governmental and institutional bodies. However, the lack of a global consensus and fragmentation in policies and practices pose challenges to the development of a common framework. The purpose of this report is to compare approaches and identify patterns in the emergent and fragmented data governance ecosystem within sectors close to the international development field, ultimately presenting key takeaways and reflections on when and why a global data governance framework may be needed. Overall, the report highlights the need for a more holistic, coordinated transnational approach to data governance to manage the global flow of data responsibly and for the public interest. The article begins by giving an overview of the current fragmented data governance ecology, to then proceed to illustrate the methodology used. Subsequently, the paper illustrates the most relevant findings stemming from the research. These are organized according to six key elements: (a) purpose, (b) principles, (c) anchoring documents, (d) data description and lifecycle, (e) processes, and (f) practices. Finally, the article closes with a series of key takeaways and final reflections.
翻译:虽然数据治理在历史上与私营组织治理有关,但数据治理已演变成包括政府和机构机构在内。然而,由于在管理和使用数据方面没有明确界定的方法,因此数据并非总能成为各组织和社会的关键资源。然而,数据治理并非总能有其价值,因为没有明确界定的管理和使用方法。本条款探讨了由于数据迅速增长以及需要负责任地使用和保护数据而使全球数据治理越来越重要。数据治理在历史上与私营组织治理有关,但数据治理已演变为包括政府和机构机构。然而,缺乏全球共识以及政策和做法的分散化,对共同框架的制定构成挑战。本报告的目的是比较方法,并查明与国际发展领域相近的部门中新兴和分散的数据治理生态系统的格局,最终提出关键的取舍和反思,说明何时和为何可能需要建立全球数据治理框架。总体而言,报告强调需要对数据治理采取更全面、协调的跨国方法,以负责任地管理全球数据流动,并符合公众利益。文章首先概述了目前分散的数据治理生态环境,然后对所采用的方法进行反思。随后,本文件说明了从研究中得出的最相关结论。这些结论按六个关键要素编排:(a)最后的周期、(b)和系列(f)最后说明(c)数据、周期(c)进程。</s>