THE IMPACT OF MACHINE LEARNING ON PRESCRIPTIVE ANALYTICS FOR OPTIMIZED BUSINESS DECISION-MAKING

Abstract

This study investigates into the integration of Machine Learning (ML) with Prescriptive Analytics, showcasing the enhancement of decision-making processes in business through this combination. By analyzing contemporary methodologies and practical applications, it delves into how ML algorithms significantly improve the precision, efficiency, and forecasting capabilities of prescriptive analytics. Highlighting case studies across a variety of sectors, the research underscores the competitive edge businesses can gain by adopting these sophisticated analytical tools. Moreover, it addresses the array of technical and organizational hurdles that arise with the implementation of ML-enhanced prescriptive analytics, such as challenges in data handling, system integration, and the demand for specialized skills. Leveraging the latest advancements and insights from experts, the paper offers a compilation of best practices and strategic methodologies to effectively overcome these obstacles. Conclusively, it emphasizes the critical role of continuous innovation in ML and prescriptive analytics, encouraging firms to adopt these cutting-edge technologies to maintain a competitive stance in the fast-evolving, data-centric business landscape.