Data Mart model to improve the productivity of private companies, Case study in real estate company

Abstract

The article stems from Sosa(2017) research and uses a Data Mart to improve productivity in companies. According to Tavera and Ríos (2021), industry 4.0 is a set of technologies that companies require to drive rapid responses in dynamic contexts, these technologies include OLAP (online analytical processing), ETL (extraction, transformation and loading), business intelligence. The research has a quantitative approach, in which a Data Mart model is built with the aim of improving decision-making and productivity in private companies, taking the real estate sector as a case study, an area with the greatest projection in the country. The conclusions indicate that the elaboration of the model has served for a significant reduction of times and improvements in business productivity, therefore, if it is replicated, a positive contribution is expected in companies.