Data-Driven Decision Making in Digital Enterprises

  • Authors

    • Dr. Ibrahim Yusuf Professor, University of Lagos, Nigeria Author
    • Dr. Grace Ndlovu Associate Professor, University of Pretoria, South Africa Author

    DOI:

    https://doi.org/10.67228/3071642X/IJCFDE-2022PI1U7V

    Published 04-05-2022

  • Data-Driven Decision Making, Digital Enterprises, Big Data Analytics, Business Intelligence, Artificial Intelligence, Predictive Analytics, Digital Transformation, Data Governance

    Issue

    Vol. 5 No. 1 (2022)

    Section

    Articles

    How to Cite

    Yusuf, I., & Ndlovu, G. (2022). Data-Driven Decision Making in Digital Enterprises. International Journal of Commerce, Finance and Digital Economy, 5(1), 01-14. https://doi.org/10.67228/3071642X/IJCFDE-2022PI1U7V

  • Abstract

    The rapid advancement of digital technologies has enabled organizations to generate and analyze vast amounts of data, making Data-Driven Decision Making (DDDM) a critical component of modern digital enterprises. DDDM leverages data analytics, business intelligence, machine learning, and predictive modeling to support evidence-based decision-making, improving operational efficiency, customer satisfaction, innovation, and competitive advantage. Organizations integrate data from enterprise systems, customer platforms, IoT devices, cloud environments, and digital transactions to gain actionable business insights and optimize strategic decisions. Despite its benefits, DDDM implementation faces challenges such as data quality issues, privacy concerns, system integration complexities, and organizational resistance. Successful adoption requires robust data governance, advanced analytical infrastructure, and a data-driven organizational culture aligned with business objectives. This study examines the role of DDDM in digital enterprises by exploring key technologies, implementation strategies, and analytical frameworks that support effective decision-making. A conceptual methodology is proposed to illustrate the transformation of organizational data into actionable business intelligence. The study also presents a quantitative evaluation of decision effectiveness across operational efficiency, customer satisfaction, revenue growth, and risk management. The findings indicate that organizations adopting data-driven strategies achieve improved decision accuracy, enhanced operational performance, and stronger competitive positioning. Furthermore, predictive analytics and real-time data processing significantly increase organizational responsiveness to dynamic market conditions. The study concludes that DDDM is a key driver of sustainable growth and innovation, with emerging technologies such as artificial intelligence and autonomous analytics expected to further transform enterprise decision-making.

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