Opportunities and Challenges in Big Data Analytics for Decision Making: An Integrated Framework

Abstract

Big Data Analytics (BDA) has evolved from a predominantly technical batch function into a socio-technical capability integrating cloud-native platforms, stream processing, Lakehouse architecture, machine learning operations (MLOps), visualization, governance, and managerial judgment. This paper proposes an integrated BDA decision-making framework developed through a structured conceptual synthesis of research on data platforms, analytical capabilities, decision processes, organizational readiness, technology adoption, governance, and responsible artificial intelligence. The framework comprises seven interconnected stages: data sources, ingestion and integration, storage and platform, processing, analytics and artificial intelligence, visualization and interpretation, and decision, action, and learning. Governance, human oversight, organizational readiness, task characteristics, and continuous feedback influence all stages. Key implementation requirements include data quality, interoperability, security, privacy, scalability, cost, explainability, bias, skills, and sustainability. The proposed configurable reference architecture links technical integration, task–analytics fit, governance assurance, human judgment, and organizational readiness with decision quality and organizational outcomes. Organizational size and maturity, sectoral risk, decision criticality, technological context, and regulatory environment are defined as boundary conditions for future empirical validation.