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The Role of Data Science in Driving Business Intelligence and Predictive Analytics: Applications, Challenges, and Future Directions

Year: 2025
Type: preprint
Abstract: Data science has emerged as the backbone of modern business intelligence (BI) and predictive analytics, transforming raw data into actionable insights, competitive advantages, and operational efficiency. Organizations across finance, healthcare, logistics, and retail rely on advanced statistical methods, machine learning (ML), natural language processing (NLP), and visualization tools to enhance decision-making. The intelligence hidden in data is unlocked through an understanding of domain-specific dynamics, system complexities, and behavioral insights. Predictive analytics strengthens strategic planning by forecasting risks, reducing costs, and driving innovation, while BI provides the frameworks and architectures to operationalize insights in real time. This paper presents a comprehensive study of data science in BI and predictive analytics, structured across applications, methodologies, challenges, and future directions. It explores the evolution of BI tools, predictive modeling techniques, data collection and preprocessing, statistical foundations, and ML model implementation. Experiments include case studies in fraud detection, retail demand forecasting, and healthcare analytics, with visual dashboards demonstrating how actionable insights can be operationalized. The research also addresses challenges such as data quality, ethics, model bias, and scalability, proposing future research pathways including explainable AI, edge analytics, and AI-powered BI platforms. By bridging theoretical and practical perspectives, this paper provides academics, practitioners, and industry leaders with both conceptual frameworks and applied examples of how data science is revolutionizing business intelligence and predictive analytics.
Language: English
Open Access status: gold
OpenAlex ID: W4413638698
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