Building a Tableau-Driven Decision Analytics Framework for Real-Time IT Performance and Operations Management

The increasing complexity of enterprise IT infrastructures necessitates robust, real-time analytics frameworks for performance and operations management. Tableau, as a leading business intelligence (BI) and visualization platform, offers the capability to integrate diverse data streams into interactive dashboards that enhance decision-making and operational agility. This review explores the development of a Tableau-driven decision analytics framework that consolidates key performance indicators (KPIs) across network operations, system uptime, application performance, and incident response metrics. The framework leverages data extraction, transformation, and loading (ETL) processes to ensure data consistency and integrates predictive analytics to forecast system failures and optimize resource utilization. Emphasis is placed on how Tableau’s visualization layers, combined with APIs and real-time connectors, enable IT managers to transform complex datasets into actionable insights. The study further examines best practices in dashboard architecture, governance, and security, ensuring alignment with ITIL, DevOps, and service-level management principles. By reviewing empirical findings and industry use cases, this paper highlights how Tableau enhances transparency, operational visibility, and strategic responsiveness in IT ecosystems. The proposed decision analytics framework contributes to establishing proactive IT performance management systems, minimizing downtime, and improving service delivery efficiency across digital enterprises.