American International Journal of Advanced Research and Development

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Advanced Analytics Frameworks for Business Intelligence Development

Author(s) Prof. Daniel Lim
Country Singapore
Abstract The rapid expansion of digital technologies has transformed business intelligence from traditional reporting and descriptive information systems into advanced analytical environments capable of supporting predictive, prescriptive, and real-time decision-making. Organizations increasingly require analytical frameworks that can integrate large and heterogeneous datasets, advanced statistical methods, machine learning, artificial intelligence, visualization, and decision-support capabilities. This study examines advanced analytics frameworks for business intelligence development and proposes an integrated conceptual model connecting data acquisition, data integration, analytical processing, knowledge generation, decision support, organizational action, and performance evaluation. A conceptual and descriptive research design is adopted through a structured synthesis of scholarly literature addressing business intelligence, business analytics, data warehousing, big data, decision support systems, artificial intelligence, organizational intelligence, and analytical decision-making. Existing research indicates that modern BI&A systems are increasingly concerned with converting diverse organizational data into deeper insights and actionable decision support rather than simply producing static reports. The analysis identifies advanced analytics capability, data quality, technological infrastructure, organizational readiness, analytical culture, management support, visualization, governance, and user engagement as important determinants of successful BI development.
The proposed framework integrates descriptive, diagnostic, predictive, and prescriptive analytics within a continuous business intelligence cycle. The study further examines the contribution of advanced analytics to strategic planning, operational management, customer intelligence, risk assessment, performance monitoring, and resource optimization. The findings suggest that organizations can obtain greater value from BI systems when analytical outputs are directly connected with organizational objectives, key performance indicators, business processes, and managerial decisions. Goal-oriented BI research similarly emphasizes connecting organizational goals, KPIs, situations, processes, and analytics to improve decision-making. The study concludes that advanced analytics frameworks can strengthen business intelligence by transforming fragmented data resources into integrated knowledge and actionable organizational intelligence.
Keywords Business Intelligence, Advanced Analytics, Business Analytics, Decision Support, Data Analytics, Predictive Analytics, Prescriptive Analytics, Artificial Intelligence, Organizational Intelligence, Business Performance
Field Engineering
Published In Volume 2, Issue 2, March-April 2020
Published On 2020-03-16
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