Operational Intelligence: An Integrated Machine Learning Approach to Enterprise Support Ticket Analytics

Efficient management of organizational support operations requires platforms that can integrate diverse datasets, visualize workload patterns, and provide actionable insights for decision-making. Traditional ticketing systems often lack the ability to automatically categorize information, analyze resolution times, and generate predictive recommendations for staffing. To address these limitations, this work presents Operational Intelligence: An Integrated Machine Learning Approach to Enterprise Support Ticket Analytics, a system designed to unify operational data analysis with interactive visualization and automated reporting. The platform leverages a modular architecture built on Streamlit and Plotly, enabling real-time dashboards for metrics such as ticket inflow, resolution duration, SLA breach rates, and workload distribution across agents. Semantic mapping functions ensure that uploaded datasets are automatically classified into dates, categories, numeric values, and textual fields, reducing manual preprocessing. Additional features include heatmaps, Sankey flows, and word clouds that provide intuitive representations of workload dynamics and root causes. Predictive modules estimate staffing requirements based on ticket inflow and agent capacity, while sentiment analysis offers supplementary insights into customer mood trends. By combining structured analytics, visualization, and reporting, the system contributes to operational intelligence in service management. It emphasizes practical utility and transparency over heavy reliance on artificial intelligence, ensuring adaptability across enterprise environments. The proposed framework demonstrates how hybrid data-driven platforms can bridge the gap between raw operational records and actionable insights, supporting organizations in improving efficiency, resource planning, and decision-making.