Data-Driven Decision Making: Empowering Businesses through Advanced Analytics and Machine Learning


  • Orlando Troisi, Gennaro Maione Department of Management and Innovation Systems, University of Salerno,Fisciano, SA


Data-driven decision making, Advanced analytics, Machine learning, Predictive modeling, Clustering, Anomaly detection, Business intelligence, Automation, Optimization, Competitive advantage


Data-driven decision making has become paramount in today's competitive business landscape, leveraging advanced analytics and machine learning to extract insights from vast datasets. This paper explores the significance of data-driven approaches in empowering businesses to make informed decisions swiftly and effectively. By harnessing the power of advanced analytics techniques such as predictive modeling, clustering, and anomaly detection, organizations can uncover valuable patterns and trends within their data. Machine learning algorithms further enhance decision-making processes by automating repetitive tasks, predicting future outcomes, and optimizing operations. Through real-world examples and case studies, this paper highlights the transformative impact of data-driven decision making across various industries, from finance and marketing to healthcare and manufacturing. Embracing a data-centric mindset enables businesses to stay agile, responsive, and competitive in an increasingly data-driven world.