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Design of a Big Data Analytics-Based Decision Support System for Enterprises

Author

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  • Shuxian Li

    (Qinhuangdao Vocational and Technical College, China)

  • Xue Gao

    (Qinhuangdao Vocational and Technical College, China)

  • Na Wang

    (Qinhuangdao Vocational and Technical College, China)

  • Xiaojie Zhang

    (Qinhuangdao Vocational and Technical College, China)

Abstract

This study proposed a novel big data analytics-based decision support system for enterprises that addressed key challenges such as data silos, dynamic adaptability, and real-time processing. By integrating multisource data collection, intelligent analysis (e.g., machine/deep learning), and closed-loop feedback mechanisms, the system enhances decision accuracy, throughput, and cross-departmental collaboration. Experimental results demonstrated significant improvements in response speed (e.g., 67.1% revenue growth rate increase) and operational efficiency compared to traditional models. However, limitations in security, interface compatibility, and deployment costs for small and medium-sized enterprises were identified. The research provides a scalable framework for data-driven enterprise management, contributing to theoretical and practical advancements in decision support system design.

Suggested Citation

  • Shuxian Li & Xue Gao & Na Wang & Xiaojie Zhang, 2026. "Design of a Big Data Analytics-Based Decision Support System for Enterprises," International Journal of Information System Modeling and Design (IJISMD), IGI Global Scientific Publishing, vol. 17(1), pages 1-18, January.
    • Handle: RePEc:igg:jismd0:v:17:y:2026:i:1:p:1-18
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