The Training of Analytical Talent: A Shared Task between University and Industry

Authors

  • Edith Johana Medina Hernández Universidad Nacional de Colombia

DOI:

https://doi.org/10.15665/dem.v19i1.2671

Keywords:

Analytics, Technological Tools, University Training, Data Science

Abstract

The aim of this article is to reflect on the importance of using data and technological tools in order to generate analytical capacity in organizations since they are decisive in the strategic solution of business problems, customers’ knowledge, and the adaptability competitive market, which is increasingly becoming more digital. In order to make analytics at the business-level, human talent with knowledge and skills in data science and digital domains is also required, who can take advantage of the potential of predictive modeling techniques in order to achieve data-based decision making. In this sense, the training of the analytical talent is a shared task between university and industry, and given the fact that currently the demand is greater than the supply, it is pertinent to argue about under which conditions, they would allow to close the existing gap.

Author Biography

Edith Johana Medina Hernández, Universidad Nacional de Colombia

Profesora Auxiliar de la Facultad de Ciencias en la Universidad Nacional de Colombia, Sede Medellín.

Doctoranda en Estadística Multivariante Aplicada de la Universidad de Salamanca, España.

References

Ali, B. & Siniak, N. (2021). The need for big data analytics in decision-making in today’s world. Conference The impact of industry 4.0 on job creation 2020. Publishing House Alexander Dubček University in Trenčín. Slovak Republic.

Augustine, F. K., Woodside, J., Mendoza, M., & Chambers, V. (2020). Analytics, Accounting And Big Data: Enhancing Accounting Education. Journal of Management & Engineering Integration 13 (1), 1-8.

Álvarez Jareño, J. A, & Coll-Serrano, V. (2018). “Científico de datos”, la profesión del presente. Métodos de Información 9 (16), 113-129. DOI: http://dx.doi.org/10.5557/IIMEI9-N16-113129

Biskupovic, C. & Brinck, G. (2018). La etnografía frente a los desafíos actuales de las ciencias sociales. Temas sociológicos 23, 9-31. DOI: https://doi.org/10.29344/07196458.23.1848

Coelho da Silveira, C., Marcolin, C., da Silva, M., & Domingos, J. (2020). What is a Data Scientist? Analysis of core soft and technical competencies in job postings. Revista Inovação, Projetos e Tecnologias 8 (1), 25-39. DOI: https://doi.org/10.5585/iptec.v8i1.17263

Colina Vargas, A. M. (2019). El gobierno de datos: un referente entre el gobierno de TI y la inteligencia de negocios. Revista Científica Ecociencia, 6(1), 1–19. DOI: https://doi.org/10.21855/ecociencia.61.186

Davenport, T. (2020). Beyond Unicorns: Educating, Classifying, and Certifying Business Data Scientists. Harvard Data Science Review, 2(2). DOI: https://doi.org/10.1162/99608f92.55546b4a

Della, M. & Esposito, F. (2020). How universities fill the talent gap: The data scientist in the Italian case. African Journal of Business Management, 14(2), 53-64. DOI: https://doi.org/10.5897/AJBM2019.8885.

Ho, A., Nguyen, A., Pafford, J. L & Slater, R. (2019). A Data Science Approach to Defining a Data Scientist. SMU Data Science Review, 2(3), Article 4.

Hong, T., Gao, D. W., Laing, T., Kruchten D. & Calzada, J. (2018). Training Energy Data Scientists: Universities and Industry Need to Work Together to Bridge the Talent Gap. IEEE Power and Energy Magazine, 16(3), 66-73. DOI: https://doi.org/10.1109/MPE.2018.2798759

Irizarry, R. A. (2020). The Role of Academia in Data Science Education. Harvard Data Science Review, 2(1). DOI: https://doi.org/10.1162/99608f92.dd363929

Lemus-Delgado, D. y Pérez Navarro, R. (2020). Ciencias de datos y estudios globales: aportaciones y desafíos metodológicos. Colombia Internacional (102), 41-62. DOI: https://doi.org/10.7440/colombiaint102.2020.03

Lope Salvador, V., Mamaqi, X. & Vidal Bordes, F. J. (2020). La Inteligencia Artificial. Revista Icono 14, 18(1), 58-88. DOI: https://doi.org/10.7195/ri14.v18i1.1434

Luces, M. (2019). Competencias del Ingeniero en Informática en la Cuarta Revolución Industrial. Revista Venezolana de Computación, 6(2), 1-9.

Márquez Díaz, J. (2020). Inteligencia artificial y Big Data como soluciones frente a la COVID-19. Revista de Bioética y Derecho (50), 315-331. DOI: https://doi.org/10.1344/rbd2020.50.31643

Mariani, M. & Fosso W., S. (2020). Exploring how consumer goods companies innovate in the digital age: The role of big data analytics companies. Journal of Business Research 121, 338-352. DOI: https://doi.org/10.1016/j.jbusres.2020.09.012

Mason, H. & Patil, D. (2015). Data Driven. Sebastopol, O’Reilly Media, Inc.

Mckinsey Global Institute. (2016). The Age of Analytics: Competing in a Data-Driven World. London: Mckinsey.

MINTIC Colombia (2021). Ciudadanía digital. https://ciudadaniadigital.gov.co/627/w3-propertyvalue-12324.html

Moreno Cely, G. A. & Gutiérrez Rodríguez, R. E. (2020). Estudio prospectivo de la tecnología en la educación superior en Colombia al 2050. Universidad & Empresa, 22(38), 160-182. DOI: http://dx.doi.org/10.12804/revistas.urosario.edu.co/empresa/a.7583

Nadikattu, R. R. (2020). Research on data science, data analytics and big data. International Journal of Engineering, Science and Mathematics, 91(5), 99-105. DOI: http://dx.doi.org/10.2139/ssrn.3622844

Naydenova I., Kovacheva, Z. & Kaloyanova, K. (2021). Important Data Quality Accents for Data Analytics and Decision Making. 1st IFSA Winter Conference on Automation, Robotics & Communications for Industry 4.0 - ARCI’ 2021. Chamonix-Mont-Blanc, France.

Padilla, O., Buenaventura, C., Isaacs, S., Perdomo, M. & Pérez, Y. (2013). Propuesta para la creación de empresa Emprender Consultores. Consultoría Integral en el Proceso de Globalización de las Pymes. http://hdl.handle.net/10882/5970.

Peláez Valencia, L. (2020). Los nuevos programas de ingenierías que demanda la Industria 4.0. Entre Ciencia e Ingeniería, 14 (27), 7-8. DOI: https://doi.org/10.31908/19098367.1717

Prasanna, M.R., Swapna, M. y Venkataramana, K. (2017). Business Intelligence and Analytics in Big Data. International Journal of Scientific & Engineering Research, 8(5), 205-2010.

Pujol M., N., Porven R., J. (2018). Ciencia de datos: una revisión del estado del arte. UCE Ciencia. Revista de postgrado, 6(3), 1-10.

Ranjan, J. & Foropon, C. (2021). Big Data Analytics in Building the Competitive Intelligence of Organizations. International Journal of Information Management 56, 1-13. DOI: https://doi.org/10.1016/j.ijinfomgt.2020.102231.

Thompson, J., & Rogers, S. (2017). Analytics: How to Win with Intelligence. Basking Ridge: Technics Publications.

Treviño-Reyes, R., Rivera-Rodríguez, F., Garza-Alonso, J. (2020). La analítica de datos como ventaja competitiva en las organizaciones. VINCULATEGICA EFAN, 6(2), 1063-1074.

Vega, J. (2020). Datos, Ciencia e Ingeniería. Ingeniare. Revista chilena de ingeniería, 28(1), 2-3. DOI: http://dx.doi.org/10.4067/S0718-33052020000100002

Vogelsang, A. & Borg, M. (2019). Requirements Engineering for Machine Learning: Perspectives from Data Scientists. 2019 IEEE 27th International Requirements Engineering Conference Workshops (REW). Jeju Island, Korea (South), 245-251. DOI: https://doi.org/10.1109/REW.2019.00050.

Willis A. J. (2019). Statistics = Analytics? Quality Engineering, 32 (2), 133-144. DOI: https://doi.org/10.1080/08982112.2019.1633670

Published

2021-04-30

How to Cite

Medina Hernández, E. J. (2021). The Training of Analytical Talent: A Shared Task between University and Industry. Dimensión Empresarial, 19(1), 92-106. https://doi.org/10.15665/dem.v19i1.2671

Issue

Section

FREE ASSAY ON SCIENCE TOPICS