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Big Data Analytics Frameworks for Intelligent Decision-Making Systems

Author(s) Dr. Daniel Richardson
Country United States
Abstract The rapid growth of digital technologies has generated enormous volumes of structured, semi-structured, and unstructured data across organizational, industrial, governmental, healthcare, financial, and technological environments. Transforming these extensive datasets into meaningful knowledge requires analytical frameworks capable of integrating data management, advanced analytics, machine learning, predictive modeling, visualization, and intelligent decision support.
Big data analytics frameworks provide a systematic foundation for processing large-scale information and converting complex data patterns into actionable intelligence. This study examines the role of big data analytics frameworks in intelligent decision-making systems, with particular emphasis on data integration, analytical intelligence, predictive decision support, real-time processing, and adaptive organizational decision-making. A qualitative descriptive research design based on secondary scholarly literature was employed, and thematic analysis was used to identify recurring patterns associated with big data analytics and intelligent decision systems.
The findings indicate that integrated analytics frameworks can improve decision quality by enabling organizations to identify hidden patterns, predict future conditions, monitor operational changes, optimize resource allocation, and evaluate alternative strategies. The analysis further indicates that combining descriptive, diagnostic, predictive, and prescriptive analytics with machine learning can strengthen decision-making capabilities across complex environments.
However, implementation remains affected by data quality, infrastructure requirements, privacy, cybersecurity, interoperability, analytical complexity, model interpretability, and organizational readiness. The study argues that effective big data analytics frameworks should not focus exclusively on data volume or computational capacity but should establish a complete pathway from data acquisition to validated decision intelligence. Human expertise remains essential for contextual interpretation, ethical judgment, and strategic decision approval. The study concludes that big data analytics frameworks can provide a powerful foundation for intelligent decision-making when they integrate scalable data infrastructure, advanced analytical methods, responsible data governance, continuous validation, and human-centered decision support.
Keywords Big Data Analytics, Intelligent Decision-Making, Data Science, Machine Learning, Predictive Analytics, Decision Support Systems, Data Integration, Business Intelligence, Intelligent Systems, Data-Driven Decision-Making.
Field Engineering
Published In Volume 1, Issue 6, November-December 2019
Published On 2019-11-01
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