Assessing Data Analytics Capabilities in Retail Organizations: Insights into Mining, Predictive Analytics and Machine Learning.

• Autores: Pariona Luque, Rosario Blanca, Pacheco, Alex Abelardo, Vegas-Gallo, Edwin, Castanho, Rui Alexandre, Lema, Fabián, Huaman-Valle, Angela, Añaños-Bedriñana, Marco A., Marín, Wilson, Felix-Poicon, Edwin Carlos Lenin, Loures, Ana
• Formato: artículo
• Fecha de Publicación: 2024
• Institución: Universidad Nacional Autónoma de Chota
• Repositorio: UNACH-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.unach.edu.pe:20.500.14142/825
• Enlace del recurso: https://repositorio.unach.edu.pe/handle/20.500.14142/825; https://doi.org/10.37394/23207.2024.21.126
• Nivel de acceso: acceso abierto
• Materia: data analytics; https://purl.org/pe-repo/ocde/ford#2.07.05

Nowadays, implementing data analytics is necessary to improve the collection, evaluation, analysis, and organization of data that allow the discovery of patterns, correlations, and trends that improve knowledge management, development of strategies, and decision-making in the organization. Therefore, this study aims to provide an accurate and detailed assessment of the current state of data analytics in the retail sector, identifying specific areas of improvement to strengthen knowledge management in organizations. The research is applied with a quantitative approach and non-experimental design at a descriptive and propositional level. The survey technique was used, and as a data collection instrument, a questionnaire addressed to 351 employees of companies in the retail sector concerning the variable data analysis with the dimensions of data extraction, predictive analysis, and machine learning and the variable management of the knowledge with the dimensions knowledge creation and knowledge storage. The results show that 52.99% of collaborators indicate that the level of data extraction is terrible, 57.83% indicate that the level of predictive analysis is wrong, and 54.99% express that the level of machine learning is average, which contributes to the implementation of innovative resources and solutions that promote the inclusion of a high-tech approach to address information management problems and contribution to the development of knowledge in an institution.