Data-Driven Fiscal Health Monitoring: Utilizing Data Analytics and Visualization as an Early Warning System for Local Governments to Achieve Financial Accountability

This study addresses the critical issue of local government fiscal accountability in Indonesia, exemplified by Riau Province's significant budget deficit. It aims to design and implement a data-driven fiscal health monitoring dashboard as an early warning system (EWS) to enhance transparency and informed decision-making. Employing a mixed-methods sequential explanatory design and the CRISP-DM framework, this research develops a comprehensive dashboard using Microsoft Power BI. Financial data (budget realization and balance sheets) from all Indonesian local governments (2015–2023) were extracted from the Ministry of Finance's portal. Data Analysis Expressions (DAX) were used to calculate financial health indicators based on Ritonga's (2014) six-dimension model of local government financial condition. The study successfully developed an interactive dashboard that synthesizes complex financial data into accessible visualizations. Application to Riau Province reveals its overall fiscal health is categorized as “Adequate” compared to other Sumatran provinces. The analysis provides granular insights across all six dimensions—short-term and long-term solvency, budget solvency, financial flexibility, financial independence, and service solvency highlighting specific areas of strength and vulnerability, such as significant fluctuations in short-term solvency and service capacity. This research fills a critical gap by moving beyond static ratio analysis to implement a dynamic, integrated, and practical analytics-based EWS. It contributes to both practice and literature by demonstrating the application of advanced business intelligence tools in public sector financial monitoring, offering a replicable model for improving fiscal transparency and accountability.