Integrating Data Science and Cloud Computing for Intelligent Decision Support Systems

Abstract

In the era of big data and cloud computing, integrating Data Science with Cloud Computing has emerged as a powerful paradigm for enhancing decision support systems. This integration leverages advanced analytical techniques and scalable cloud infrastructure to transform raw data into actionable insights, enabling organizations to make informed and timely decisions. This paper explores the convergence of Data Science and Cloud Computing, highlighting the synergies between these fields and their impact on developing intelligent decision support systems (DSS). We review the key methodologies, including data collection, preprocessing, and analysis, and discuss how cloud-based platforms facilitate the deployment and scaling of these methods. Case studies are presented to illustrate practical applications and the benefits of integrating these technologies in various sectors such as finance, healthcare, and supply chain management. The paper also addresses challenges and future directions, emphasizing the need for robust data governance, security, and privacy measures. By synthesizing current research and practical implementations, this study provides a comprehensive overview of how Data Science and Cloud Computing together enhance the efficacy and efficiency of decision support systems.