Automatización de un proceso ETL para la industria farmacéutica mediante un modelo predictivo basado en Power BI

Abstract

This study presents an analysis of the automation of the ETL process in the pharmaceutical industry and its integration with predictive models in Power BI. The research was carried out to determine the technological impact of a digital solution in a business scheme, with the use of indicators to know its performance during management decision making and its improvement of the waiting time in data processing. The proposed methodology covered from data collection to the connection with Power BI and the evaluation of the efficiency of the developed system. Python was used for data extraction and transformation, ensuring consistency and efficiency in the handling of large sets of information, in conjunction with data loading, was performed in a DataWarehouse, and the connection with Power BI allowed a detailed analysis of patterns and trends. And finally, the implementation of predictive models of the platform was integrated, which provided indicators on the demand for drugs in the industry. The results obtained highlighted significant improvements in operational efficiency by reducing manual tasks, allowing healthcare personnel to focus on more specific, non-repetitive tasks. Extensive testing demonstrated the functionality and accuracy of the system, supporting its positive impact on operational agility and management decision making.