Government Big Data Ecosystem: Definitions, Types of Data, Actors, and Roles and the Impact in Public Administrations

IF 1.5 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
Syed Iftikhar Husain Shah, Vassilios Peristeras, Ioannis Magnisalis
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引用次数: 8

Abstract

The public sector, private firms, business community, and civil society are generating data that are high in volume, veracity, and velocity and come from a diversity of sources. This type of data is today known as big data. Public administrations pursue big data as “new oil” and implement data-centric policies to collect, generate, process, share, exploit, and protect data for promoting good governance, transparency, innovative digital services, and citizens’ engagement in public policy. All of the above constitute the Government Big Data Ecosystem (GBDE). Despite the great interest in this ecosystem, there is a lack of clear definitions, the various important types of government data remain vague, the different actors and their roles are not well defined, while the impact in key public administration sectors is not yet deeply understood and assessed. Such research and literature gaps impose a crucial obstacle for a better understanding of the prospects and nascent issues in exploiting GBDE. With this study, we aim to start filling the above-mentioned gaps by organizing our findings from an extended Systematic Literature Review into a framework to organise and address the above-mentioned challenges. Our goal is to contribute in this fast-evolving area by bringing some clarity and establishing common understanding around key elements of the emerging GBDE.
政府大数据生态系统:定义、数据类型、行动者和角色及其对公共管理的影响
公共部门、私营企业、商业团体和公民社会正在产生大量、准确性和速度高的数据,这些数据来自多种来源。这种类型的数据今天被称为大数据。公共行政部门将大数据视为“新石油”,并实施以数据为中心的政策,收集、生成、处理、共享、利用和保护数据,以促进良好治理、透明度、创新数字服务和公民参与公共政策。以上构成了政府大数据生态系统(GBDE)。尽管人们对这一生态系统非常感兴趣,但缺乏明确的定义,各种重要类型的政府数据仍然模糊,不同的行动者及其作用没有得到很好的界定,而对关键公共行政部门的影响尚未得到深刻的理解和评估。这样的研究和文献差距对更好地理解利用GBDE的前景和新出现的问题造成了重大障碍。在这项研究中,我们的目标是通过将我们从扩展的系统文献综述中得到的发现组织成一个框架来组织和解决上述挑战,从而开始填补上述空白。我们的目标是通过对新兴的GBDE的关键要素进行澄清和建立共识,为这个快速发展的领域做出贡献。
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来源期刊
ACM Journal of Data and Information Quality
ACM Journal of Data and Information Quality COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
4.10
自引率
4.80%
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0
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