Bibliographic citations
Soto, J., Barrios, S. (2024). Dashboard de inteligencia de negocios para monitorear los indicadores de la gestión comercial de una empresa de seguros [Trabajo de Suficiencia Profesional, Universidad Peruana de Ciencias Aplicadas (UPC)]. http://hdl.handle.net/10757/683481
Soto, J., Barrios, S. Dashboard de inteligencia de negocios para monitorear los indicadores de la gestión comercial de una empresa de seguros [Trabajo de Suficiencia Profesional]. PE: Universidad Peruana de Ciencias Aplicadas (UPC); 2024. http://hdl.handle.net/10757/683481
@misc{renati/1039603,
title = "Dashboard de inteligencia de negocios para monitorear los indicadores de la gestión comercial de una empresa de seguros",
author = "Barrios Sanchez, Stannis Giomar",
publisher = "Universidad Peruana de Ciencias Aplicadas (UPC)",
year = "2024"
}
The automotive sector was negatively impacted by the COVID-19 pandemic, resulting in a decline in the acquisition of vehicle insurance. However, the sector has managed to recover, showing remarkable growth in recent years. Currently, the automotive insurance market presents significant potential, making it essential to offer products that meet customer needs. To achieve this, it is crucial to interpret and track data appropriately, ensuring the monitoring of indicators to facilitate decisions based on customer requirements. This Professional Sufficiency Project is implemented in the vehicle product area of a financial sector company. The identified problem is the difficulty in monitoring commercial management indicators, which hampers decision-making due to non-centralized information and inter-departmental dependencies, leading to a one-week wait to obtain necessary data. We propose the implementation of a Business Intelligence dashboard to monitor the indicators of the mentioned company. To do this, we use the Pentaho tool to consolidate data scattered across different areas through an ETL process and Power BI for its construction and publication. Additionally, End-to-End and integrity tests were conducted to verify that the dashboard data is consistent with previously obtained data. We minimized human error by automating data processing, allowing for faster monitoring of indicators, as the processing time was reduced by 86%.
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