Intelligent Analytics Models for Real-Time Decision Support

Michael Thompson

Intelligent Analytics Models for Real-Time Decision Support

Author(s)	Prof. Michael Thompson
Country	Canada
Abstract	The rapid growth of digital technologies has significantly increased the volume, velocity, and complexity of information available to organizations. As business environments become increasingly dynamic, conventional decision-support systems based primarily on historical reporting may be insufficient for situations requiring immediate interpretation and rapid managerial action. Intelligent analytics models provide an advanced approach by integrating real-time data processing, predictive analytics, machine learning, artificial intelligence, pattern recognition, and decision-support mechanisms. This study examines intelligent analytics models for real-time decision support and develops a conceptual framework connecting continuous data acquisition, preprocessing, intelligent analysis, prediction, decision generation, and performance evaluation. A conceptual and descriptive research methodology is adopted through a structured review and synthesis of scholarly literature addressing real-time analytics, business intelligence, decision-support systems, machine learning, streaming data, and intelligent computing. The study identifies real-time data availability, analytical accuracy, processing speed, model adaptability, explainability, data quality, infrastructure scalability, and user interaction as important factors affecting decision-support effectiveness. The proposed framework integrates descriptive, diagnostic, predictive, and prescriptive analytical capabilities into a continuous decision-support cycle. The study further demonstrates that real-time decision support requires close coordination between technological infrastructure and managerial decision processes. Intelligent analytical systems can support applications in finance, healthcare, manufacturing, transportation, retail, cybersecurity, supply-chain management, and digital services. However, challenges related to data quality, model reliability, latency, security, privacy, interpretability, computational cost, and organizational adoption may limit successful implementation. The study concludes that intelligent analytics models can significantly strengthen real-time decision support when they are designed around organizational objectives, continuous data flows, appropriate analytical methods, and transparent decision processes. The findings provide a conceptual foundation for developing adaptive and scalable intelligent decision-support environments capable of responding effectively to rapidly changing information conditions.
Keywords	Intelligent Analytics, Real-Time Analytics, Decision Support Systems, Artificial Intelligence, Machine Learning, Predictive Analytics, Real-Time Decision-Making, Data Processing, Business Intelligence, Intelligent Computing
Field	Engineering
Published In	Volume 2, Issue 2, March-April 2020
Published On	2020-03-24

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American International Journal of Advanced Research and Development

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Intelligent Analytics Models for Real-Time Decision Support

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