A Synergetic Model for Implementing Big Data in Organizations

2018 ◽  
pp. 281-298
Author(s):  
Mohanad Halaweh ◽  
Ahmed El Massry
Keyword(s):  
Big Data ◽  
Data Privacy ◽  
Primary Data ◽  
Extended Model ◽  
Management Support ◽  
Privacy And Security ◽  
Top Management Support ◽  
Top Executives ◽  
Research Findings ◽  
Synergetic Model

The term BIG DATA has been increasingly used recently. Big data refers to the massive amount of data that are processed and analyzed using sophisticated technology to gain relevant insights that will help top executives with the decision-making process. This study is an attempt to investigate the big data implementation in organizations. The literature review reveals an initial model of indicators that might affect big data implementation. This model was examined and extended by primary data collected from key people (CEO and managers) from ten organizations. The extended model of indicators, which is the result from this research, includes the factors that would affect the success or failure of big data implementation in organizations. The research findings showed the following factors: top management support, organizational change, IT infrastructure, skilled professional, contents (i.e. data), data strategy, data privacy and security.

A Synergetic Model for Implementing Big Data in Organizations

2017 ◽  
Vol 30 (1) ◽  
pp. 48-64 ◽  
Author(s):  
Mohanad Halaweh ◽  
Ahmed El Massry
Keyword(s):  
Big Data ◽  
Data Privacy ◽  
Primary Data ◽  
Extended Model ◽  
Management Support ◽  
Privacy And Security ◽  
Top Management Support ◽  
Top Executives ◽  
Research Findings ◽  
Synergetic Model

The term BIG DATA has been increasingly used recently. Big data refers to the massive amount of data that are processed and analyzed using sophisticated technology to gain relevant insights that will help top executives with the decision-making process. This study is an attempt to investigate the big data implementation in organizations. The literature review reveals an initial model of indicators that might affect big data implementation. This model was examined and extended by primary data collected from key people (CEO and managers) from ten organizations. The extended model of indicators, which is the result from this research, includes the factors that would affect the success or failure of big data implementation in organizations. The research findings showed the following factors: top management support, organizational change, IT infrastructure, skilled professional, contents (i.e. data), data strategy, data privacy and security.

A Synergetic Model for Implementing Big Data in Organizations

Web Services ◽  
2019 ◽  
pp. 473-489
Author(s):  
Mohanad Halaweh ◽  
Ahmed El Massry
Keyword(s):  
Big Data ◽  
Data Privacy ◽  
Primary Data ◽  
Extended Model ◽  
Management Support ◽  
Privacy And Security ◽  
Top Management Support ◽  
Top Executives ◽  
Research Findings ◽  
Synergetic Model

The term BIG DATA has been increasingly used recently. Big data refers to the massive amount of data that are processed and analyzed using sophisticated technology to gain relevant insights that will help top executives with the decision-making process. This study is an attempt to investigate the big data implementation in organizations. The literature review reveals an initial model of indicators that might affect big data implementation. This model was examined and extended by primary data collected from key people (CEO and managers) from ten organizations. The extended model of indicators, which is the result from this research, includes the factors that would affect the success or failure of big data implementation in organizations. The research findings showed the following factors: top management support, organizational change, IT infrastructure, skilled professional, contents (i.e. data), data strategy, data privacy and security.

Big Data Adoption

2022 ◽  
pp. 1817-1842
Author(s):  
Hemlata Gangwar
Keyword(s):  
Big Data ◽  
Data Privacy ◽  
Manufacturing Sector ◽  
Sem Analysis ◽  
Relative Advantage ◽  
Organizational Size ◽  
Management Support ◽  
Contributing Factors ◽  
Confirmatory Factor Analyses ◽  
Top Management Support

This study inspects how big data is comprehended by IT experts and the difficulties that they have in respect to the reception of big data examination. The study also looks into the contributing factors of big data adoption within the manufacturing and services sectors in India. The data were analyzed using exploratory and confirmatory factor analyses, and relevant hypotheses were derived and tested by SEM analysis. The findings revealed that relative advantage, compatibility, complexity, organizational size, top management support, competitive pressure, vendor support, data management, and data privacy are the factors that are important for both industries. Through a comparison of the industries, statistically significant differences between the service and the manufacturing sectors were found; in other words, it has been noted that the relative importance of all factors for big data adoption differs between the industries, with the only exception being its complexity – it was found to be insignificant for the manufacturing sector.

Big Data Adoption

2020 ◽  
pp. 138-171
Author(s):  
Hemlata Gangwar
Keyword(s):  
Big Data ◽  
Data Privacy ◽  
Manufacturing Sector ◽  
Sem Analysis ◽  
Relative Advantage ◽  
Organizational Size ◽  
Management Support ◽  
Contributing Factors ◽  
Confirmatory Factor Analyses ◽  
Top Management Support

This study inspects how big data is comprehended by IT experts and the difficulties that they have in respect to the reception of big data examination. The study also looks into the contributing factors of big data adoption within the manufacturing and services sectors in India. The data were analyzed using exploratory and confirmatory factor analyses, and relevant hypotheses were derived and tested by SEM analysis. The findings revealed that relative advantage, compatibility, complexity, organizational size, top management support, competitive pressure, vendor support, data management, and data privacy are the factors that are important for both industries. Through a comparison of the industries, statistically significant differences between the service and the manufacturing sectors were found; in other words, it has been noted that the relative importance of all factors for big data adoption differs between the industries, with the only exception being its complexity – it was found to be insignificant for the manufacturing sector.

Understanding the Determinants of Big Data Adoption in India

2018 ◽  
Vol 31 (4) ◽  
pp. 1-22 ◽  
Author(s):  
Hemlata Gangwar
Keyword(s):  
Big Data ◽  
Data Privacy ◽  
Service Sector ◽  
Manufacturing Sector ◽  
Sem Analysis ◽  
Relative Advantage ◽  
Organizational Size ◽  
Management Support ◽  
Confirmatory Factor Analyses ◽  
Top Management Support

This article sought to identify the drivers of Big Data adoption within the manufacturing and services sectors in India. A questionnaire-based survey was used to collect data from manufacturing and service sector organizations in India. The data was analyzed using exploratory and confirmatory factor analyses. Relevant hypotheses were then derived and tested by SEM analysis. The findings revealed that the following factors are important for both sectors: relative advantage, compatibility, complexity, organizational size, top management support, competitive pressure, vendor support, data management and data privacy. Statistically significant differences between the service and the manufacturing sectors were found. In other words, the relative importance of the factors for Big Data adoption differs between the sectors. The only exception was complexity, which was found to be insignificant in regard to the manufacturing sector. The factors identified can be used to facilitate Big Data adoption outcomes in organizations.

scholarly journals Solutions to Big Data Privacy and Security Challenges Associated With COVID-19 Surveillance Systems

2021 ◽  
Vol 4 ◽  
Author(s):  
Vibhushinie Bentotahewa ◽  
Chaminda Hewage ◽  
Jason Williams
Keyword(s):  
Big Data ◽  
Data Privacy ◽  
Personal Data ◽  
Surveillance Systems ◽  
Privacy Rights ◽  
Personal Privacy ◽  
Privacy And Security ◽  
Digital Surveillance ◽  
Feasible Solutions ◽  
The Impact

The growing dependency on digital technologies is becoming a way of life, and at the same time, the collection of data using them for surveillance operations has raised concerns. Notably, some countries use digital surveillance technologies for tracking and monitoring individuals and populations to prevent the transmission of the new coronavirus. The technology has the capacity to contribute towards tackling the pandemic effectively, but the success also comes at the expense of privacy rights. The crucial point to make is regardless of who uses and which mechanism, in one way another will infringe personal privacy. Therefore, when considering the use of technologies to combat the pandemic, the focus should also be on the impact of facial recognition cameras, police surveillance drones, and other digital surveillance devices on the privacy rights of those under surveillance. The GDPR was established to ensure that information could be shared without causing any infringement on personal data and businesses; therefore, in generating Big Data, it is important to ensure that the information is securely collected, processed, transmitted, stored, and accessed in accordance with established rules. This paper focuses on Big Data challenges associated with surveillance methods used within the COVID-19 parameters. The aim of this research is to propose practical solutions to Big Data challenges associated with COVID-19 pandemic surveillance approaches. To that end, the researcher will identify the surveillance measures being used by countries in different regions, the sensitivity of generated data, and the issues associated with the collection of large volumes of data and finally propose feasible solutions to protect the privacy rights of the people, during the post-COVID-19 era.

scholarly journals Secure Data-Encryption Strategy for Big-Data in Mobile Cloud

2019 ◽  
Vol 9 (1S) ◽  
pp. 1-4
Keyword(s):  
Big Data ◽  
Data Privacy ◽  
Data Encryption ◽  
Mobile Cloud ◽  
Timing Constraints ◽  
Privacy And Security ◽  
Secure Data ◽  
Consistent Performance ◽  
A New Technique ◽  
High Level

Now-a-days data plays a key role in Information Technology and while coming to privacy of that data it has become a considerable issue to maintain data security at high level. Large amounts of data generated through devices are considered as a major obstacle and also tough to handle in real time scenarios. To meetwith consistent performance applications at present abandon encryptions techniquesbecausethe time for the execution and the completion of encryption techniques plays a key role during processing and transmissions of data. In this paper our moto is to secure data and proposed a new technique called Dynamic Data Encryption Strategy (DDES)which selectively encrypts data and uses some algorithms which provides a perfect encryption strategy for the data packages under some timing constraints. By this method we can achieve data privacy and security for big-data in mobile cloud-computing by using an encryption strategy respective to their requirements during execution time.

Adoption of Big Data Analytics in Supply Chain Management

2021 ◽  
Vol 14 (2) ◽  
pp. 88-107
Author(s):  
Amin Khalil Alsadi ◽  
Thamir Hamad Alaskar ◽  
Karim Mezghani
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Top Management ◽  
Management Support ◽  
Chain Management ◽  
Top Management Support ◽  
Intention To Adopt

Supported by the literature on big data, supply chain management (SCM), and resource-based theory (RBT), this study aims to evaluate the organizational variables that influence the intention of Saudi SCM professionals to adopt big data analytics (BDA) in SCM. A survey of 220 supply chain respondents revealed that both top management support and data-driven culture have a high significant influence on their intention to adopt BDA. However, the firm entrepreneurial orientation showed no significant effect. Also, the findings revealed that supply chain connectivity positively moderates the link between top management support and intention. This study contributes to the practical field, offering valuable insights for decision makers considering BDA adoption in SCM. It also contributes to the literature by helping minimize the research gap in BDA adoption in the Saudi context.

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