Optimizing IT Incident and Problem Management Through Data Analytics and ITIL-Aligned Digital Workflows
DOI:
https://doi.org/10.32628/CSEIT2511666Keywords:
IT Service Management, Incident and Problem Management, ITIL v4; Data Analytics, Digital Workflow AutomationAbstract
This study examines how data analytics and ITIL-aligned digital workflows can be leveraged to optimize IT incident and problem management in complex, digitally transformed enterprise environments. As organizations face increasing incident volumes, recurring service disruptions, and heightened service availability expectations, traditional reactive and manual IT service management approaches have proven insufficient. The study adopts a mixed-methods research design combining quantitative analysis of ITSM operational data with qualitative insights from IT service professionals to evaluate the impact of analytics-driven practices on incident detection, resolution efficiency, and problem prevention. The findings demonstrate that the systematic application of descriptive, diagnostic, and predictive analytics significantly improves mean time to detect (MTTD), mean time to resolve (MTTR), prioritization accuracy, and incident recurrence rates. When embedded within ITIL-aligned digital workflows, analytics outputs are consistently translated into actionable decisions through automated routing, escalation, and remediation processes. This integration enhances service quality, operational reliability, governance visibility, and cross-team coordination, while supporting continuous improvement and value realization in line with the ITIL Service Value System. The study contributes to both theory and practice by extending ITIL implementation through analytics-enabled decision-making and proposing a structured framework for analytics-driven IT service management optimization. Practical implications highlight the need for organizations to invest in integrated analytics, workflow automation, and robust ITSM data governance to achieve resilient and scalable service operations. The study concludes by identifying limitations related to data availability and organizational context and suggests future research directions focused on AI-driven AIOps integration and longitudinal analysis of analytics maturity and service performance outcomes.
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