<![CDATA[This study investigates the impact of business analytics and big data on predictive maintenance and asset management practices within the energy industry in Indonesia. A quantitative research approach, utilizing a survey methodology, was employed to gather data from stakeholders representing various sectors of the energy industry. The study analyzed the relationships between business analytics, big data, predictive maintenance, and asset management using structural equation modeling (SEM) with Partial Least Squares (PLS) regression. The results indicate significant positive relationships between the utilization of business analytics and big data and various performance metrics, including asset reliability, operational efficiency, and cost savings. Furthermore, organizational factors such as leadership support and data quality were found to play a crucial role in facilitating the adoption and implementation of predictive maintenance strategies. The findings underscore the transformative potential of data-driven maintenance strategies in enhancing operational efficiency, reducing downtime, and improving asset reliability within the Indonesian energy industry.]]>
