Data-Driven Risk Monitoring Framework Using Business Intelligence Dashboards in Hospitality Operations

Aim: This study aims to develop and evaluate a data-driven business intelligence (BI) dashboard framework for monitoring and managing operational risks in the hospitality industry. Methods: The study adopts a mixed-methods approach, combining a systematic thematic literature review of 24 peer-reviewed studies published between 2015 and 2026 with a qualitative comparative case analysis of an independent U.S. hotel property. Industry benchmark data from AHLA, CoStar/STR, PwC, D-Edge, and Cloudbeds were integrated to calibrate segment-differentiated risk thresholds. Results: The framework demonstrated measurable improvements in operational risk detection and response efficiency. Undetected occupancy risk events decreased by 14.3%, suggesting that integrated dashboard monitoring enhances the visibility of demand disruptions in otherwise siloed reporting environments. This is evidenced by the observed reduction in response time from 4.7 to 1.8 hours and improvements in risk detection rates, indicating that real-time BI-driven alerts facilitate faster operational decision-making under volatile conditions. RevPAR (Revenue per Available Room) stability improved, with the coefficient of variation declining from 0.22 to 0.16, reflecting enhanced revenue consistency attributable to proactive risk mitigation. The predictive models achieved AUC-ROC values of 0.89 for occupancy risk classification and 0.93 for cancellation prediction, confirming robust discriminative accuracy across risk categories. The study demonstrates that integrating predictive analytics with BI dashboards offers a robust approach to real-time risk monitoring and decision support in hospitality operations, bridging existing gaps between fragmented analytical tools and structured operational risk governance. Conclusion: The findings suggest that hospitality managers should adopt integrated BI dashboard systems and segment-specific risk thresholds to enhance decision-making efficiency and operational resilience. Recommendations: Industry bodies and policymakers should promote standardized data governance frameworks to support the scalability and regulatory compliance of BI-based risk monitoring systems.