Reconceptualizing Evidence-Based Business Decisions in the Era of Data Analytics and Artificial Intelligence
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Business organizations are increasingly required to move beyond intuition-based decision-making toward evidence-based approaches supported by data analytics. Advances in data analytics, machine learning, and predictive modeling have reshaped how firms interpret information, forecast trends, and respond to market dynamics. However, despite growing adoption, challenges related to data quality, organizational readiness, and ethical governance continue to limit the effective use of analytics in strategic decision-making. This study adopts a qualitative-descriptive approach using secondary data analysis and real-world case illustrations across multiple industries, including retail, healthcare, finance, logistics, and technology. Drawing on the Data-Driven Decision-Making (DDDM) framework and business intelligence theory, the paper synthesizes insights from academic literature, industry reports, and documented organizational practices to examine how data analytics supports evidence-based decisions and operational efficiency. The findings demonstrate that data analytics significantly enhances decision accuracy, operational efficiency, and strategic agility. Predictive analytics and machine learning enable organizations to anticipate market trends, personalize customer engagement, reduce operational risks, and optimize resource allocation. Empirical illustrations indicate notable improvements in efficiency, risk reduction, revenue growth, and customer satisfaction when analytics-driven approaches are systematically implemented. However, data quality issues, talent shortages, resistance to change, and data privacy concerns remain critical barriers. The study highlights that the transformative value of data analytics lies not only in technological adoption but also in cultivating a data-driven organizational culture supported by leadership commitment and ethical governance.
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