In recent years, the surge in big data has ignited interest in leveraging it to enhance knowledge management within organizations. Traditional data management methods face difficulties coping with the vast volume, diversity, and velocity of big data, necessitating the exploration of new technologies and frameworks. Companies are seeking a unified system capable of both storing and analysing diverse types of big data to derive real-time insights and facilitate efficient decision-making. Cloud computing emerges as a pragmatic solution due to its scalability and cost-effectiveness in managing large data volumes. This chapter introduces a cloud-based conceptual model designed to explore the integration of big data analytics and knowledge management, typically viewed as separate disciplines. It underscores the importance of deploying advanced data analytics, machine learning, and artificial intelligence to address industrial challenges such as optimizing plant operations, ensuring process safety, and advancing environmental conservation efforts. The model outlines the data analytics life cycle as applied to industrial settings and presents case studies that illustrate various techniques, including predictive maintenance monitoring, text mining, risk mapping, and sustainability analysis. Despite the inherent challenges in implementation, integrating machine analytics, expert insights, and relevant data sources remains essential for informed decision-making and operational enhancement across industries.