A Data Science Approach to Predictive Analytics: Using PowerBI and SQL for Business Intelligence

Abstract

Predictive analytics plays a crucial role in enhancing decision-making in today’s data-driven business environment. This study explores a comprehensive data science approach to predictive analytics by integrating SQL and Power BI to develop effective business intelligence tools. SQL serves as the foundation for data extraction, transformation, and storage, ensuring data integrity and accessibility. Meanwhile, Power BI enhances data visualization, dashboarding, and real-time insights, allowing businesses to interpret complex datasets effortlessly. The paper delves into the methodologies involved in data modeling, statistical analysis, and machine learning, which help uncover patterns, predict future outcomes, and enable data-driven decision-making. By leveraging SQL's structured querying capabilities alongside Power BI’s interactive analytics, businesses can gain valuable insights, improve operational efficiency, and refine strategic planning. Furthermore, this study highlights the synergy between structured data processing and dynamic visualization, demonstrating how organizations can harness predictive analytics to stay competitive, optimize processes, and drive innovation.