Diagnostic Analytics for Enterprise Reporting Platforms

Enterprise reporting platforms support organizational analysis through automated reports and analytical dashboards that process operational and financial data. Despite their widespread use in business intelligence environments, limited research examines the internal operational behavior of these platforms. Most studies address predictive analytics, enterprise data management, or system monitoring rather than analytical diagnosis of reporting activities. This study proposes a diagnostic analytics framework for evaluating performance within enterprise reporting systems. The framework examines report generation logs, query execution records, and system interaction data to interpret reporting behavior and identify abnormal execution patterns. The methodological process includes log data collection, preprocessing, feature extraction, and statistical anomaly detection using report execution time metrics. Several diagnostic indicators support the analysis, including query processing duration, concurrent user activity, data processing volume, and execution failure frequency. Analytical results show that most reports operate within normal execution ranges, while a smaller group demonstrates unusually long execution durations. These events correspond with high database workload and complex query operations. The results indicate that operational log data provide meaningful insight into reporting platform performance. The proposed framework offers a structured analytical approach for identifying reporting delays and evaluating system efficiency within enterprise reporting environments.