Integrating Deep Data Analysis and AI Technology to Empower Business Decision Making through Executive Dashboards

Main Article Content

John Steven Rodríguez Vera
MSc. Andrea Leonor Bermeo Rosero

Abstract

The companies are increasingly interested in using artificial intelligence tools to analyze large amounts of data and obtain valuable information for decision-making. One such tool is deep data analytics, which uses machine learning algorithms to analyze large data sets and uncover complex patterns and relationships. The purpose of this article is to provide recommendations for creating admin dashboards with in- depth data analysis. A management dashboard is a tool that gives managers and executives a real-time overview of business performance to make informed and quick decisions.


The proposal is based on the use of advanced data analysis techniques, such as deep learning and natural language processing, to extract valuable insights from large enterprise data sets. This information is displayed on a dashboard that provides a complete view of the company's performance in terms of sales, finances, human resources, and more. This proposal will be of interest to companies seeking to improve their ability to make informed and data-based decisions. By using artificial intelligence tools such as deep data analytics, companies can derive relevant insights from large data sets that would otherwise be difficult to analyze effectively.


Keywords: “Artificial intelligence”, “Deep data analysis” y “Dashboard”


 

Article Details

How to Cite
Rodríguez Vera , J. S. ., & Bermeo Rosero, A. L. . (2023). Integrating Deep Data Analysis and AI Technology to Empower Business Decision Making through Executive Dashboards. Revista Sapientia Technological, 4(1). https://doi.org/10.58515/010RSPT (Original work published June 9, 2023)
Section
Artículos

References

Alpaydin, E. (2020). Introducción al aprendizaje automático. MIT Press.

Carvalho, J. P., Santos, M. Y., & Cortez, P. (2019). Una arquitectura de análisis de big data para la industria 4.0. Computers & Industrial Engineering, 137, 106074. https://doi.org/10.1016/j.cie.2019.106074

Côrte-Real, N., Oliveira, T., & Ruivo, P. (2020). Evaluación del valor empresarial del análisis de big data en empresas europeas. Journal of Business Research, 112, 72- 83. https://doi.org/10.1016/j.jbusres.2020.02.008

Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Entrenamiento previo de transformadores bidireccionales profundos para la comprensión del lenguaje. En Actas de la Conferencia 2019 del Capítulo Norteamericano de la Asociación para la Lingüística Computacional: Tecnologías del Lenguaje Humano, Volumen 1 (Artículos largos y cortos), 4171-4186. https://doi.org/10.18653/v1/N19- 1423

Maney, K. (2016). El impacto de la transformación digital: Un tornado EF5. Revista de Economía Digital.

National Geographic España. (2023). National Geographic. https://www.nationalgeographicla.com/ciencia/2023/01/que-es-chatgpt-y-para-que- sirve

Zhang, Y., Lemoine, J., & Choo, K. K. R. (2018). Transporte inteligente basado en datos: una encuesta de técnicas de aprendizaje profundo para la predicción del tráfico. IEEE Transactions on Intelligent Transportation Systems, 19(8), 2332-2345. https://doi.org/10.1109/TITS.2017.2777296