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Advanced Analytics Approaches for Business Intelligence Applications

Author(s) Prof. Shah Miah
Country Australia
Abstract Advanced analytics has become a fundamental component of modern business intelligence, enabling organizations to transform large and complex datasets into actionable managerial insights. The integration of descriptive, diagnostic, predictive, and prescriptive analytics with artificial intelligence, machine learning, data mining, visualization, and automated decision-support technologies has expanded the capabilities of conventional business intelligence systems. This study examines advanced analytics approaches for business intelligence applications through a conceptual and literature-based research design. The analysis focuses on predictive modeling, machine learning, data mining, real-time analytics, customer analytics, financial intelligence, operational intelligence, and prescriptive decision support. An integrated framework is developed connecting data acquisition, data integration, advanced analytical processing, insight generation, business intelligence visualization, managerial decision-making, implementation, and performance feedback. Advanced analytics can support organizations in identifying patterns, forecasting demand, detecting risks, optimizing resources, understanding customers, and improving strategic planning.
However, successful implementation depends on data quality, analytical capabilities, technological infrastructure, organizational readiness, data governance, cybersecurity, and employee expertise. Organizations may encounter challenges related to fragmented data, analytical complexity, high implementation costs, privacy concerns, model interpretability, and resistance to data-driven organizational change. The study emphasizes that advanced analytics should be integrated with organizational strategy and managerial expertise rather than treated solely as a technical capability. Future research should examine explainable analytics, artificial intelligence-enabled business intelligence, real-time decision systems, automated analytics, augmented intelligence, responsible data governance, and industry-specific analytical frameworks. The proposed framework provides a conceptual foundation for organizations seeking to strengthen business intelligence through advanced analytics and improve evidence-based decision-making, operational efficiency, organizational responsiveness, and sustainable competitive development.
Keywords Advanced Analytics, Business Intelligence, Predictive Analytics, Machine Learning, Data Mining, Business Analytics, Decision Support, Data Visualization, Artificial Intelligence, Organizational Performance
Field Engineering
Published In Volume 6, Issue 6, November-December 2024
Published On 2024-12-30
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