Intelligent Information Systems Using Advanced Data Analytics

Lars Kai Hansen

Intelligent Information Systems Using Advanced Data Analytics

Author(s)	Prof. Lars Kai Hansen
Country	Denmark
Abstract	The rapid expansion of digital information has transformed the way organizations collect, process, interpret, and utilize information for operational and strategic decision-making. Conventional information systems often provide structured reporting and transaction-processing capabilities but may have limited capacity to identify complex patterns, predict future conditions, and recommend adaptive actions. Intelligent information systems enhanced with advanced data analytics can address these limitations by integrating artificial intelligence, machine learning, predictive modeling, data mining, real-time processing, and intelligent decision-support mechanisms. This study examines the role of advanced data analytics in developing intelligent information systems and proposes a conceptual framework for improving information processing, predictive capability, operational responsiveness, and decision quality. A conceptual and descriptive research methodology was adopted through a review and thematic synthesis of scholarly literature related to intelligent information systems, Big Data analytics, artificial intelligence, machine learning, business intelligence, predictive analytics, and decision-support technologies. The analysis demonstrates that advanced analytics can transform information systems from primarily reactive repositories into proactive and adaptive intelligence platforms. Descriptive analytics provides visibility into existing information, diagnostic analytics identifies important relationships and causes, predictive analytics forecasts future outcomes, and prescriptive analytics supports action selection and optimization. Machine-learning techniques further enhance intelligent information systems by detecting complex patterns within large and heterogeneous datasets. The proposed framework integrates data acquisition, data management, analytical processing, machine-learning models, intelligent interpretation, decision support, and continuous feedback. Applications include business management, healthcare administration, financial services, education, supply-chain management, smart cities, manufacturing, and digital service platforms. The study also identifies important implementation challenges, including data quality, system interoperability, cybersecurity, privacy, model transparency, infrastructure requirements, and shortages of specialized analytical expertise.
Keywords	Intelligent Information Systems, Advanced Data Analytics, Artificial Intelligence, Machine Learning, Predictive Analytics, Decision Support Systems, Data Mining, Business Intelligence, Intelligent Computing, Information Management
Field	Engineering
Published In	Volume 2, Issue 4, July-August 2020
Published On	2020-08-06

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American International Journal of Advanced Research and Development

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Intelligent Information Systems Using Advanced Data Analytics

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