scholarly journals Bibliometrics and Knowledge Graph Analysis of Domestic Big Data Management Research

2021 ◽  
Vol 1827 (1) ◽  
pp. 012132
Author(s):  
Yang Weihua ◽  
Xu Dong
2019 ◽  
Vol 57 (8) ◽  
pp. 2113-2123 ◽  
Author(s):  
S.M. Riad Shams ◽  
Ludovico Solima

PurposeBig data management research and practice, however, have received enormous interest from academia and industry; the extant literature demonstrates that the authors have limited understanding and challenges in this knowledge-stream to fully capitalize with its potentials. One of the contemporary challenges is to accurately verify data veracity, and developing value from the verified data for an organization and its stakeholders. Consequently, the purpose of this paper is to develop insights on how the authors could strategically deal with the contemporary challenges in strategic management of big data, related to data veracity and data value.Design/methodology/approachThe inductive–constructivist approach is followed to develop insights at the intersection of dynamic capabilities theory and stakeholder relationship management concept, in order to strategically deal with the contemporary challenges in big data management, related to data veracity and data value.FindingsAt the intersection of dynamic capabilities theory and stakeholder relationship management concept, an implication is acknowledged, which has research and practical significance to strategically verify data source, its veracity and value. Following this implication, a framework of a data incubator is proposed to proactively develop insights on veracity and value of data. Empirical insights are also presented in this study to support this initial framework.Practical implicationsFor future research in strategic management of big data, academics will have contextual understanding on the particular interconnected and interdependent attributes from dynamic capabilities and stakeholder relationship management research streams to further enhance the understanding on big data management. For practice, these insights will be useful for executives to focus on specific attributes of the proposed data incubator to confirm data veracity and develop insights on how to design, deliver and evaluate stakeholder value based on the verified data.Originality/valueFollowing a synthesis at the intersection of dynamic capabilities theory and stakeholder relationship management concept, this study introduces a data incubator to meaningfully deal with the big data management challenges, related to veracity and value of data.


2021 ◽  
Vol 1771 (1) ◽  
pp. 012004
Author(s):  
Ling Chao Gao ◽  
Li Ming Yao ◽  
ZhiWei Yang ◽  
Fei Zheng

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Syed Iftikhar Hussain Shah ◽  
Vassilios Peristeras ◽  
Ioannis Magnisalis

AbstractThe public sector, private firms, business community, and civil society are generating data that is high in volume, veracity, velocity and comes from a diversity of sources. This kind of data is known as big data. Public Administrations (PAs) pursue big data as “new oil” and implement data-centric policies to transform data into knowledge, to promote good governance, transparency, innovative digital services, and citizens’ engagement in public policy. From the above, the Government Big Data Ecosystem (GBDE) emerges. Managing big data throughout its lifecycle becomes a challenging task for governmental organizations. Despite the vast interest in this ecosystem, appropriate big data management is still a challenge. This study intends to fill the above-mentioned gap by proposing a data lifecycle framework for data-driven governments. Through a Systematic Literature Review, we identified and analysed 76 data lifecycles models to propose a data lifecycle framework for data-driven governments (DaliF). In this way, we contribute to the ongoing discussion around big data management, which attracts researchers’ and practitioners’ interest.


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