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Effective Decision Support in the Big Data Era: Optimize Organizational Performance via BI&A

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  • Fen Wang

    (Central Washington University, USA)

  • Mahesh S. Raisinghani

    (Texas Woman's University, USA)

  • Manuel Mora

    (Autonomous University of Aguascalientes, Mexico)

  • Jeffrey Forrest

    (Slippery Rock University)

Abstract

This study conducts a review and synthesis of the business intelligence and analytics (BI&A) evolution, applications, frameworks, and emerging trends with the aim to provide a summary of core concepts, a succinct but valuable description of main applications and frameworks, and an account of main recommendations for addressing the big data challenges and opportunities. It develops an integrated and organized view on the BI&A evolution process and presents an integrated BI&A application framework to help organizations adopt or develop the appropriate BI&A solutions to derive the desired impact in the big data era. This paper also elicits a set of practical recommendations to executives and leaders in organizations worldwide for interpreting the BI&A literature and applying the rich body of knowledge for IT practitioners. It traces the BI&A evolution to data-driven discovery and highly proactive and creative decision-making utilizing advanced analytical techniques with unstructured and massive data sources to cope with a highly dynamic global business environment in the big data era.

Suggested Citation

  • Fen Wang & Mahesh S. Raisinghani & Manuel Mora & Jeffrey Forrest, 2022. "Effective Decision Support in the Big Data Era: Optimize Organizational Performance via BI&A," International Journal of Decision Support System Technology (IJDSST), IGI Global, vol. 14(1), pages 1-16, January.
    • Handle: RePEc:igg:jdsst0:v:14:y:2022:i:1:p:1-16
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