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Intelligent Data Analytics Using Machine Learning for Smart Decision Systems

Author(s) Dr. Rajani Chulyadyo
Country Nepal
Abstract The rapid growth of digital information has created an increasing need for intelligent analytical approaches capable of transforming large and complex datasets into meaningful decision-support information. Traditional analytical methods may struggle to process high-dimensional, heterogeneous, and continuously changing datasets within modern organizational environments. Machine learning provides advanced capabilities for identifying hidden patterns, predicting future outcomes, detecting anomalies, and generating data-driven insights that can strengthen decision-making processes. This study examines the role of intelligent data analytics using machine learning in the development of smart decision systems.
A qualitative descriptive research design was adopted through a structured review and conceptual analysis of literature concerning machine learning, data analytics, predictive modeling, intelligent decision support systems, and data-driven decision-making. The findings indicate that machine learning can enhance decision systems by improving predictive capability, analytical efficiency, pattern recognition, adaptive decision support, and operational responsiveness. However, challenges related to data quality, model interpretability, computational requirements, privacy, security, bias, scalability, and human trust remain important barriers. The study concludes that effective smart decision systems require an integrated framework combining reliable data, appropriate machine learning algorithms, continuous evaluation, explainability, human oversight, and organizational alignment.
Keywords Intelligent Data Analytics, Machine Learning, Smart Decision Systems, Predictive Analytics, Decision Support Systems, Artificial Intelligence, Data-Driven Decision Making, Intelligent Systems.
Field Engineering
Published In Volume 1, Issue 2, March-April 2019
Published On 2019-03-02
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