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On big data-guided upstream business research and its knowledge management

Author

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  • Nimmagadda, Shastri L.
  • Reiners, Torsten
  • Wood, Lincoln C.

Abstract

The emerging Big Data integration imposes diverse challenges, compromising the sustainable business research practice. Heterogeneity, multi-dimensionality, velocity, and massive volumes that challenge Big Data paradigm may preclude the effective data and system integration processes. Business alignments get affected within and across joint ventures as enterprises attempt to adapt to changes in industrial environments rapidly. In the context of the Oil and Gas industry, we design integrated artefacts for a resilient multidimensional warehouse repository. With access to several decades of resource data in upstream companies, we incorporate knowledge-based data models with spatial-temporal dimensions in data schemas to minimize ambiguity in warehouse repository implementation. The design considerations ensure uniqueness and monotonic properties of dimensions, maintaining the connectivity between artefacts and achieving the business alignments. The multidimensional attributes envisage Big Data analysts a scope of business research with valuable new knowledge for decision support systems and adding further business values in geographic scales.

Suggested Citation

  • Nimmagadda, Shastri L. & Reiners, Torsten & Wood, Lincoln C., 2018. "On big data-guided upstream business research and its knowledge management," Journal of Business Research, Elsevier, vol. 89(C), pages 143-158.
    • Handle: RePEc:eee:jbrese:v:89:y:2018:i:c:p:143-158
    DOI: 10.1016/j.jbusres.2018.04.029
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    References listed on IDEAS

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    1. 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.
    2. Stefan Debortoli & Oliver Müller & Jan Brocke, 2014. "Comparing Business Intelligence and Big Data Skills," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 6(5), pages 289-300, October.
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    1. Akyildirim, Erdinc & Sensoy, Ahmet & Gulay, Guzhan & Corbet, Shaen & Salari, Hajar Novin, 2021. "Big data analytics, order imbalance and the predictability of stock returns," Journal of Multinational Financial Management, Elsevier, vol. 62(C).
    2. Saumyaranjan Sahoo & Anil Kumar & Arvind Upadhyay, 2023. "How do green knowledge management and green technology innovation impact corporate environmental performance? Understanding the role of green knowledge acquisition," Business Strategy and the Environment, Wiley Blackwell, vol. 32(1), pages 551-569, January.

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