Title: Intelligent Big Data Analytics: An Integrated Framework for Data, Architecture, Artificial Intelligence, and Governance
Authors: Dr Neha Paliwal
Volume: 10
Issue: 9
Pages: 192-209
Publication Date: 2026/09/28
Abstract:
The rapid expansion of Internet of Things (IoT) devices, cloud platforms, enterprise applications, social networks, mobile technologies, scientific systems, and artificial intelligence (AI) has transformed Big Data analytics from a predominantly storage and processing challenge into a multidimensional information-management problem. Contemporary analytical environments must simultaneously address data volume, velocity, variety, veracity, quality, computational complexity, scalability, interoperability, privacy, security, explainability, and governance. Traditional approaches based primarily on the 3V or 4V characteristics of Big Data therefore provide an incomplete explanation of modern analytical performance. This study develops an integrated theoretical framework for intelligent Big Data analytics by connecting five major dimensions: data characteristics, data engineering, computational architecture, analytical intelligence, and governance. The framework incorporates distributed computing, cloud analytics, stream processing, edge computing, advanced data platforms, machine learning, deep learning, federated learning, privacy-preserving analytics, explainable artificial intelligence, and AI-assisted data analysis. A structured theoretical-review methodology is employed to synthesize foundational concepts and contemporary research directions. The proposed framework demonstrates that analytical value is not determined by data volume or algorithmic sophistication alone. Instead, it emerges from the interaction between data quality, computational architecture, analytical methods, security, privacy, governance, and domain requirements. The study develops ten research propositions and a multidimensional evaluation model incorporating accuracy, latency, throughput, scalability, resource utilization, cost, data quality, privacy, explainability, robustness, and reproducibility. The analysis indicates that future Big Data research should move toward integrated and intelligent analytical ecosystems rather than isolated technology evaluation. Major future research opportunities include autonomous data engineering, edge-cloud intelligence, federated analytics, multimodal Big Data, explainable AI, privacy-preserving machine learning, sustainable computing, and trustworthy AI. The proposed framework provides a technology-independent foundation for future empirical research and offers researchers and practitioners a systematic approach for designing scalable, reliable, secure, and intelligent Big Data systems.