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Critical analysis of Big Data challenges and analytical methods

Author

Listed:
  • Sivarajah, Uthayasankar
  • Kamal, Muhammad Mustafa
  • Irani, Zahir
  • Weerakkody, Vishanth
Abstract
Big Data (BD), with their potential to ascertain valued insights for enhanced decision-making process, have recently attracted substantial interest from both academics and practitioners. Big Data Analytics (BDA) is increasingly becoming a trending practice that many organizations are adopting with the purpose of constructing valuable information from BD. The analytics process, including the deployment and use of BDA tools, is seen by organizations as a tool to improve operational efficiency though it has strategic potential, drive new revenue streams and gain competitive advantages over business rivals. However, there are different types of analytic applications to consider. Therefore, prior to hasty use and buying costly BD tools, there is a need for organizations to first understand the BDA landscape. Given the significant nature of the BD and BDA, this paper presents a state-of-the-art review that presents a holistic view of the BD challenges and BDA methods theorized/proposed/employed by organizations to help others understand this landscape with the objective of making robust investment decisions. In doing so, systematically analysing and synthesizing the extant research published on BD and BDA area. More specifically, the authors seek to answer the following two principal questions: Q1 – What are the different types of BD challenges theorized/proposed/confronted by organizations? and Q2 – What are the different types of BDA methods theorized/proposed/employed to overcome BD challenges?. This systematic literature review (SLR) is carried out through observing and understanding the past trends and extant patterns/themes in the BDA research area, evaluating contributions, summarizing knowledge, thereby identifying limitations, implications and potential further research avenues to support the academic community in exploring research themes/patterns. Thus, to trace the implementation of BD strategies, a profiling method is employed to analyze articles (published in English-speaking peer-reviewed journals between 1996 and 2015) extracted from the Scopus database. The analysis presented in this paper has identified relevant BD research studies that have contributed both conceptually and empirically to the expansion and accrual of intellectual wealth to the BDA in technology and organizational resource management discipline.

Suggested Citation

  • Sivarajah, Uthayasankar & Kamal, Muhammad Mustafa & Irani, Zahir & Weerakkody, Vishanth, 2017. "Critical analysis of Big Data challenges and analytical methods," Journal of Business Research, Elsevier, vol. 70(C), pages 263-286.
  • Handle: RePEc:eee:jbrese:v:70:y:2017:i:c:p:263-286
    DOI: 10.1016/j.jbusres.2016.08.001
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    References listed on IDEAS

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    1. Wang, Gang & Gunasekaran, Angappa & Ngai, Eric W.T. & Papadopoulos, Thanos, 2016. "Big data analytics in logistics and supply chain management: Certain investigations for research and applications," International Journal of Production Economics, Elsevier, vol. 176(C), pages 98-110.
    2. Irani, Zahir & Ghoneim, Ahmad & Love, Peter E.D., 2006. "Evaluating cost taxonomies for information systems management," European Journal of Operational Research, Elsevier, vol. 173(3), pages 1103-1122, September.
    3. Alexandru Adrian TOLE, 2013. "Big Data Challenges," Database Systems Journal, Academy of Economic Studies - Bucharest, Romania, vol. 4(3), pages 31-40, October.
    4. Alnoor Bhimani & Leslie Willcocks, 2014. "Digitisation, 'Big Data' and the transformation of accounting information," Accounting and Business Research, Taylor & Francis Journals, vol. 44(4), pages 469-490, August.
    5. Ritu Agarwal & Vasant Dhar, 2014. "Editorial —Big Data, Data Science, and Analytics: The Opportunity and Challenge for IS Research," Information Systems Research, INFORMS, vol. 25(3), pages 443-448, September.
    6. Yingxu Wang & Victor J. Wiebe, 2014. "Big Data Analytics on the Characteristic Equilibrium of Collective Opinions in Social Networks," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), IGI Global, vol. 8(3), pages 29-44, July.
    7. Z Irani, 2010. "Investment evaluation within project management: an information systems perspective," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(6), pages 917-928, June.
    8. Robert J. David & Shin‐Kap Han, 2004. "A systematic assessment of the empirical support for transaction cost economics," Strategic Management Journal, Wiley Blackwell, vol. 25(1), pages 39-58, January.
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