A Tableau-Based Business Intelligence Dashboard for Multidimensional Analysis of Natural Disaster Impacts in Indonesia

Abstract

Indonesia is highly vulnerable to natural disasters and generates large volumes of disaster data each year. However, this information is still presented predominantly in tabular reports, which limits multidimensional analysis and weakens its value for evidence-based decision-making. Existing studies emphasize descriptive visualization without integrating a data warehouse architecture to support comprehensive disaster analytics. This study therefore develops a Tableau-based Business Intelligence dashboard supported by a data warehouse for multidimensional analysis and forecasting of natural disaster impacts in Indonesia. The data warehouse was designed using Ralph Kimball’s Nine-Step Methodology, and the Extract, Transform, Load (ETL) process was implemented in Pentaho Data Integration. The dataset comprises 21,986 disaster events recorded by the National Disaster Management Agency (BNPB) from January 2021 to September 2025. The dashboard provides interactive visualizations of disaster trends, regional distribution, casualties, damage levels, and the ten provinces with the highest recorded impacts, together with forecasting based on Tableau’s exponential smoothing (ETS) model. The analysis identified West Java as the province with the highest recorded disaster impacts in absolute terms during the study period. The resulting platform integrates data warehousing, multidimensional visualization, and forecasting to support disaster mitigation and response planning.