Theoretical perspectives on predictive analytics in it service management: Enhancing service quality

Abstract

This paper explores the theoretical perspectives underpinning the application of predictive analytics in IT service management (ITSM) to enhance service quality. It begins with an introduction to predictive analytics in the context of ITSM and the significance of improving service quality in IT operations. Theoretical frameworks such as Systems Theory, Information Theory, Decision Theory, and Machine Learning Theory are discussed to provide a comprehensive understanding of the underlying principles guiding predictive analytics in ITSM. The paper examines the practical applications, challenges, and benefits of predictive analytics in ITSM, emphasizing its role in anticipatory problem resolution, proactive service improvements, and predictive maintenance. Case studies and examples of successful implementations are presented to illustrate real-world applications and best practices. Additionally, future directions and emerging trends in predictive analytics technology are explored, along with their potential impact on ITSM practices and ethical considerations. Overall, this paper contributes to the theoretical foundation and practical insights for leveraging predictive analytics to enhance service quality in ITSM.