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Is Big Data for everyone? The challenges of Big Data adoption in SMEs

Is Big Data for everyone? The challenges of Big Data adoption in SMEs

Shah, S., Bardon Soriano, C. and Coutroubis, A. D. (2018) Is Big Data for everyone? The challenges of Big Data adoption in SMEs. In: Proceedings of the 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). IEEE, pp. 803-807. ISBN 978-1-5386-0947-7 ISSN 2157-362X (Online) (doi:10.1109/IEEM.2017.8290002)

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Abstract

The aim of this paper is to present an investigative study on the concept of Big Data and its challenges towards implementation in manufacturing SMEs. Big Data aims to facilitate the collaborative approach in SMEs through the creation of real time data visualization to address key challenges to many of the market variations for every sector SMEs. Although, earlier research studies have highlighted the importance of Big Data from technological perspectives, this study focuses towards SMEs due to its feasibility and flexibility within the market space. This research aims to investigate the use of case study approach for the re-use, adoption and understanding of strategic future direction from the findings. The findings and early analysis from this paper could be referred by researchers when addressing the use of big data analytics within manufacturing SMEs. Finally, the paper provides a key strategic point towards the exploration of Big Data within SMEs.

Item Type: Conference Proceedings
Title of Proceedings: Proceedings of the 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)
Additional Information: The IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) was held in Singapore from 10-13th December 2017.
Uncontrolled Keywords: Big data, Big data analytics, Manufacturing, Small and medium enterprises (SMEs)
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Faculty / Department / Research Group: Faculty of Engineering & Science
Faculty of Engineering & Science > Department of Applied Engineering & Management
Faculty of Engineering & Science > Engineering Enterprise
Related URLs:
Last Modified: 27 Feb 2018 16:49
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
URI: http://gala.gre.ac.uk/id/eprint/17586

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