Implementación de un sistema de reportería y visualización de datos con funciones de recomendación y predicción, utilizando técnicas de business intelligence (BI) e inteligencia artificial (IA), en la Dirección de Investigación y Desarrollo (DIDE) de la Universidad Técnica de Ambato (UTA)

Abstract

This thesis focused on the implementation of a data reporting and visualization system with recommendation and prediction functions, using Business Intelligence and Artificial Intelligence techniques, at the Research and Development Directorate of the Technical University of Ambato. The identified problem was related to the manual management of scientific information, data traceability, and centralization. The research was conducted using an applied, field-based, and non-experimental approach, considering five staff members involved in information management processes as the population. A structured interview with a Likert scale and a complementary semistructured interview were used for the diagnostic process, allowing the identification of eight problems and ten institutional needs. As a result, a web platform based on Scrum + XP was developed, integrating scientific article management, bulk upload, institutional workflow, external integration, ETL processes, an extensible OLTP model, a constellation-type data warehouse, dashboards, and exportable reports. Furthermore, an AI module was implemented with monthly scientific output forecasting, forecasting by faculty and research area, data quality recommendations, editorial recommendations, and AI readiness analysis using weighted indicators. The evaluation showed 80 successful reporting operations, a 100% success rate, an average time of 10.53 seconds, and an estimated reduction of 99.66%. The post-implementation structured interview based on CSUQ received a score of 4.59/5, 100% acceptance, and a Cronbach's alpha of 0.973. It is concluded that the system strengthens information management and supports data-driven decision-making.