Abstract:

The increasing volume, velocity, and variety of data have made Big Data an essential component for enabling data-driven decision-making across industries. This study explores how optimizing Big Data analytics can significantly enhance business intelligence and strategic planning. However, traditional data processing methods often struggle with real-time analysis, lack of scalability, and fragmented data sources, leading to delayed and less accurate decision-making. These limitations hinder organizations from harnessing the full potential of their data assets. To address these challenges, we propose a comprehensive framework integrating real-time data streaming into a centralized data lake, enabling dynamic dashboards powered by Big Data Analytics (BDA). This architecture supports predictive business intelligence by processing structured and unstructured data at scale, delivering insights in real time. The proposed method is applied in various business environments to assess performance metrics, customer behavior, and market trends with greater speed and accuracy. By continuously ingesting and analyzing data from multiple sources, the system facilitates timely and informed decisions. Findings from the implementation reveal a marked improvement in decision accuracy, operational efficiency, and responsiveness to market changes. The framework also demonstrates superior scalability and adaptability compared to traditional systems, making it a robust solution for modern enterprises aiming for competitive advantage through data-driven strategies.