Governing the Complexity of Smart Data Cities: Setting a Research Agenda

Edelenbos, Jurian; Hirzalla, Fadi; Zoonen, Liesbet van; Dalen, Jan van; Bouma, Geiske; Slob, Adriaan; Woestenburg, Alexander

doi:10.1007/978-3-319-58577-2_3

Cited by 24 publications

(21 citation statements)

References 31 publications

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“…Regarding the design of these data portals, Ruijer et al (2017) found that each type of democratic process requires a different approach aimed at ensuring effective use of open data. Finally, it should be also noted that there is an increasing interest in smart technologies and their applications in BOLD analytics (Edelenbos et al, 2018;Lněnička et al, 2018). In such a context, smart technologies are characterized by "a transition to some extent intelligent electronic devices or systems enabling broad access to relevant information and knowledge that should help people to get informed and improve the decisionmaking process."…”

Section: Technical Dimensionmentioning

confidence: 99%

“…In such a context, smart technologies are characterized by "a transition to some extent intelligent electronic devices or systems enabling broad access to relevant information and knowledge that should help people to get informed and improve the decisionmaking process." Edelenbos et al (2018) discussed these efforts in the context of the complexity of smart data cities and concluded that these data are key drivers of physical, economic and social change.…”

Section: Technical Dimensionmentioning

confidence: 99%

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Big and open linked data analytics ecosystem: Theoretical background and essential elements

Lněnička

Komárková

2019

Government Information Quarterly

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Big and open linked data are often mentioned together because storing, processing, and publishing large amounts of these data play an increasingly important role in today's society. However, although this topic is described from the political, economic, and social points of view, a technical dimension, which is represented by big data analytics, is insufficient. The aim of this review article was to provide a theoretical background of big and open linked data analytics ecosystem and its essential elements. First, the key terms were introduced including related dimensions. Then, the key lifecycle phases were defined and involved stakeholders were identified. Finally, a conceptual framework was proposed. In contrast to previous research, the new ecosystem is formed by interactions of stakeholders in the following dimensions and their sub-dimensions: transparency, engagement, legal, technical, social, and economic. These relationships are characterized by the most important requisites and public policy choices affecting the data analytics ecosystem together with the key phases and activities of the data analytics lifecycle. The findings should contribute to relevant initiatives, strategies, and policies and their effective implementation.

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Section: Technical Dimensionmentioning

confidence: 99%

Section: Technical Dimensionmentioning

confidence: 99%

Big and open linked data analytics ecosystem: Theoretical background and essential elements

Lněnička

Komárková

2019

Government Information Quarterly

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“…Consider, for example, a system for social rating based on microcontent contributions of citizens on social media, capable of measuring sentiments, political beliefs etc. Smart cities and smart government research [20] must take into consideration, a number of delicate issues related to privacy, security, safety and social responsibility of individuals and groups. Without a focus on sustainability [21,22], social inclusive economic growth and social justice, any isolated, monolithic big data application in the long term will unfortunately fail to promote its social impact.…”

Section: Literature Review-understanding the Debate On Big Data And Tmentioning

confidence: 99%

Big Data and Their Social Impact: Preliminary Study

Lytras

Visvizi

2019

Sustainability

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Big data is the buzz-word of today, and yet their specific impact on individuals and societies remains assumed rather than fully understood. Clearly, big data and their use have already given rise to a number of questions, including those of how data can be collected and used in ethical and socially sensitive ways. Building on these points, the objective of this study was to explore how precisely big data and big data based services influence individuals and societies. This paper elaborates on individuals’ perceptions of data, especially on how they perceive the actual sharing of their data. In this way, this paper defines a value space for the social impact of big data relevant to three factors, namely the intention to share personal data, individual’s concerns, and social impact of big data.The main contribution of this study consists of the insights into the still nascent area of research that unfolds at the cross-section of social science and computer science. We expect that in the next years this area of research will gain prominence.

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“…Some research shows that big data can completely change the functioning of an organization [62]. Recently, the notion of smart data-big data that has been initially preprocessed-has been gaining popularity [63,64]. "Smart data arises by expediently structuring information from big data which then can be used for knowledge advances and decision making" [65].…”

Section: Big Data and Sustainability Issuesmentioning

confidence: 99%

A Conceptual Framework for Assessing an Organization’s Readiness to Adopt Big Data

Olszak

Mach‐Król

2018

Sustainability

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The main aim of this paper is to provide a theoretically and empirically grounded discussion on big data and to propose a conceptual framework for big data based on a temporal dimension. This study adopts two research methods. The first research method is a critical assessment of the literature that aims to identify the concept of big data in organizations. This method is composed of a search for source materials, the selection of the source materials, and their analysis and synthesis. It has been used to develop a conceptual framework for assessing an organization’s readiness to adopt big data. The purpose of the second research method is to provide an initial verification of the developed framework. This verification consisted of conducting qualitative research with the use of an in-depth interview in 15 selected organizations. The main contribution of this study is the Temporal Big Data Maturity Model (TBDMM) framework, which can help to measure the current state of an organization’s big data assets, and to plan their future development with respect to sustainability issues. The proposed framework has been built over a time dimension as a fundamental internal structure with the goal of providing a complete means for assessing an organization’s readiness to process the temporal data and knowledge that can be found in modern information sources. The proposed framework distinguishes five maturity levels: atemporal, pre-temporal, partly temporal, predominantly temporal, and temporal, which are used to evaluate data/knowledge, information technology (IT) solutions, functionalities offered by IT solutions, and the sustainable development context.

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Governing the Complexity of Smart Data Cities: Setting a Research Agenda

Cited by 24 publications

References 31 publications

Big and open linked data analytics ecosystem: Theoretical background and essential elements

Big and open linked data analytics ecosystem: Theoretical background and essential elements

Big Data and Their Social Impact: Preliminary Study

A Conceptual Framework for Assessing an Organization’s Readiness to Adopt Big Data

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