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This paper presents a Business Intelligence (BI) dashboard optimization model designed to enhance real-time performance tracking and forecasting accuracy for improved decision-making in modern organizations. As businesses increasingly rely on data-driven insights to stay competitive, effective dashboard systems that integrate real-time data processing and accurate forecasting models are essential. This study explores the evolution of BI dashboards, the importance of real-time tracking, and the integration of forecasting tools to enable organizations to make timely, data-driven decisions. By leveraging machine learning algorithms and cloud-based solutions, the proposed model optimizes performance tracking, integrates various data sources, and offers predictive capabilities that help businesses forecast future trends with greater precision. The research highlights the challenges of implementing BI dashboards, such as data integration, user training, and technological constraints, and proposes solutions to address these issues, ensuring that the model is both scalable and user-friendly. The findings demonstrate that the optimized BI dashboard model enhances operational efficiency, supports better decision-making, and improves forecasting accuracy, particularly in dynamic environments like retail, finance, and manufacturing. The study provides actionable recommendations for organizations seeking to implement the model and suggests areas for future research, including the integration of advanced machine learning techniques and external data sources to enhance forecasting accuracy further.
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