Advanced Data Analytics Methods for Complex Problem Solving

František Babic

Advanced Data Analytics Methods for Complex Problem Solving

Author(s)	Prof. František Babic
Country	Slovakia
Abstract	Complex problems in contemporary technological, organizational, scientific, and social environments are increasingly characterized by large data volumes, interconnected variables, uncertain conditions, and rapidly changing operational requirements. Conventional analytical approaches may be insufficient when decision-makers need to identify hidden relationships, predict future outcomes, detect anomalies, and evaluate multiple interacting factors simultaneously. Advanced data analytics methods provide a comprehensive analytical foundation for addressing such complexity through statistical learning, machine learning, predictive modeling, data mining, optimization, and visualization. This study examines advanced data analytics methods for complex problem solving and evaluates their contribution to pattern identification, predictive intelligence, decision support, process optimization, and evidence-based reasoning. A qualitative descriptive research design based on secondary academic literature was adopted. The analysis focuses on descriptive analytics, diagnostic analytics, predictive analytics, prescriptive analytics, machine learning, clustering, classification, anomaly detection, and optimization techniques. Findings indicate that advanced analytics can improve complex problem solving by integrating heterogeneous datasets, identifying non-obvious patterns, forecasting potential outcomes, and supporting alternative solution evaluation. Machine-learning approaches are particularly useful for high-dimensional and nonlinear problems, whereas statistical techniques provide interpretability and structured estimation. Prescriptive analytics further extends analytical capability by identifying potential actions under specified constraints. However, effective application depends on data quality, model validity, computational infrastructure, domain knowledge, explainability, cybersecurity, and responsible data governance. The study concludes that advanced analytics should be implemented through integrated analytical frameworks rather than isolated algorithms. A combination of reliable data management, appropriate model selection, validation, human interpretation, and continuous monitoring can strengthen the effectiveness of advanced data analytics in solving complex real-world problems.
Keywords	Advanced Data Analytics, Complex Problem Solving, Machine Learning, Predictive Analytics, Data Mining, Statistical Learning, Decision Support, Optimization, Intelligent Analytics, Data Science
Field	Engineering
Published In	Volume 2, Issue 4, July-August 2020
Published On	2020-08-30

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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

Advanced Data Analytics Methods for Complex Problem Solving

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