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

DOI

10.2991/978-94-6463-938-4_42How to use a DOI?

Keywords

Data Analytics; Data Visualization; Early Warning System; Fiscal Health Monitoring; Financial Condition; Local Government; Power BI

Abstract

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.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Fachroh Fiddin
AU  - Teguh Widodo
AU  - Reni Farwitawati
PY  - 2025
DA  - 2025/12/31
TI  - Data-Driven Fiscal Health Monitoring: Utilizing Data Analytics and Visualization as an Early Warning System for Local Governments to Achieve Financial Accountability
BT  - Proceedings of the International Conference on Applied Science and Technology on Social Science 2025 (iCAST-SS 2025
PB  - Atlantis Press
SP  - 360
EP  - 370
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-938-4_42
DO  - 10.2991/978-94-6463-938-4_42
ID  - Fiddin2025
ER  -