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Integrated Understanding of Big Data, Big Data Analysis, and Business Intelligence: A Case Study of Logistics

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  • Dong-Hui Jin

    (Seoul Business School, aSSIST, 46 Ewhayeodae 2-gil, Seodaemun-gu, Seoul 03767, Korea)

  • Hyun-Jung Kim

    (Seoul Business School, aSSIST, 46 Ewhayeodae 2-gil, Seodaemun-gu, Seoul 03767, Korea)

Abstract

Efficient decision making based on business intelligence (BI) is essential to ensure competitiveness for sustainable growth. The rapid development of information and communication technology has made collection and analysis of big data essential, resulting in a considerable increase in academic studies on big data and big data analysis (BDA). However, many of these studies are not linked to BI, as companies do not understand and utilize the concepts in an integrated way. Therefore, the purpose of this study is twofold. First, we review the literature on BI, big data, and BDA to show that they are not separate methods but an integrated decision support system. Second, we explore how businesses use big data and BDA practically in conjunction with BI through a case study of sorting and logistics processing of a typical courier enterprise. We focus on the company’s cost efficiency as regards to data collection, data analysis/simulation, and the results from actual application. Our findings may enable companies to achieve management efficiency by utilizing big data through efficient BI without investing in additional infrastructure. It could also give them indirect experience, thereby reducing trial and error in order to maintain or increase competitiveness.

Suggested Citation

  • Dong-Hui Jin & Hyun-Jung Kim, 2018. "Integrated Understanding of Big Data, Big Data Analysis, and Business Intelligence: A Case Study of Logistics," Sustainability, MDPI, vol. 10(10), pages 1-15, October.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:10:p:3778-:d:176815
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    References listed on IDEAS

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    Cited by:

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    2. Koopo Kwon & Sungchan Jun & Yong-Jae Lee & Sanghei Choi & Chulung Lee, 2022. "Logistics Technology Forecasting Framework Using Patent Analysis for Technology Roadmap," Sustainability, MDPI, vol. 14(9), pages 1-30, April.
    3. Raeesi, Ramin & Sahebjamnia, Navid & Mansouri, S. Afshin, 2023. "The synergistic effect of operational research and big data analytics in greening container terminal operations: A review and future directions," European Journal of Operational Research, Elsevier, vol. 310(3), pages 943-973.
    4. Michael Song & Haili Zhang & Jinjin Heng, 2020. "Creating Sustainable Innovativeness through Big Data and Big Data Analytics Capability: From the Perspective of the Information Processing Theory," Sustainability, MDPI, vol. 12(5), pages 1-23, March.
    5. Abdulmajeed Alqhatani & Muhammad Shoaib Ashraf & Javed Ferzund & Ahmad Shaf & Hamad Ali Abosaq & Saifur Rahman & Muhammad Irfan & Samar M. Alqhtani, 2022. "360° Retail Business Analytics by Adopting Hybrid Machine Learning and a Business Intelligence Approach," Sustainability, MDPI, vol. 14(19), pages 1-20, September.
    6. Koopo Kwon & Jaeryong So, 2023. "Future Smart Logistics Technology Based on Patent Analysis Using Temporal Network," Sustainability, MDPI, vol. 15(10), pages 1-17, May.

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