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In recent times, the proliferation of big data has sparked interest in harnessing it to bolster knowledge management within organizations. Conventional data management approaches encounter difficulties in handling the extensive volume, diversity, and speed of big data, necessitating the exploration of new technologies and frameworks. Companies are in pursuit of a unified system capable of both storing and analyzing various types of big data to extract real-time insights and facilitate efficient decision-making. Cloud computing emerges as a practical solution owing to its scalability and cost-effectiveness in managing large data volumes. This chapter introduces a Cloud-based conceptual model aimed at exploring the integration of Big Data Analytics and knowledge management, traditionally regarded 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. The model delineates the data analytics lifecycle as applied to industrial settings and presents case studies illustrating 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 imperative for informed decision-making and operational enhancement across industries.