Usos del Big Data en las empresas: Un instrumento de prestigio y de supervivencia hoy
DOI:
https://doi.org/10.23857/dc.v10i2.3843Palabras clave:
Big Data, marketing, cadena de suministro, análisis predictivoResumen
El Big Data se ha convertido en un activo estratégico indispensable en el entorno empresarial actual, permitiendo a las empresas optimizar operaciones, comprender a los clientes, identificar oportunidades de negocio y anticipar tendencias del mercado. Sin embargo, su implementación conlleva desafíos relacionados con la infraestructura tecnológica, las habilidades especializadas y la protección de la privacidad de los datos.
El artículo define y conceptualiza el Big Data, resaltando sus características como volumen, velocidad y variedad, así como la importancia de la veracidad, el valor y la variabilidad de los datos. Se explora su relevancia en la toma de decisiones empresariales, analizando su evolución y aplicación en sectores como el marketing, la gestión de la cadena de suministro y la toma de decisiones estratégicas. Las aplicaciones estratégicas del Big Data incluyen análisis predictivo, personalización de productos, optimización de procesos, mejora de la experiencia del cliente y detección de fraudes.
Para implementar Big Data con éxito, es crucial seleccionar herramientas y tecnologías adecuadas, desarrollar capacidades internas para el análisis de datos e integrar sistemas con la infraestructura existente. A pesar de los desafíos que enfrenta, la adopción de Big Data puede mejorar la competitividad empresarial, la eficiencia operativa, la productividad, la innovación y la agilidad empresarial. Es fundamental contar con estrategias efectivas de gestión del cambio y fomentar una cultura empresarial orientada a la toma de decisiones basada en datos para aprovechar al máximo el potencial del Big Data en las empresas.
Citas
Adebo, P. (2018). BIG DATA IN BUSINESS. IJARCSSE, 8, 160–162. https://doi.org/10.23956/IJARCSSE.V8I1.543
Ahmed, H., & Ismail, M. A. (2020). Towards a Novel Framework for Automatic Big Data Detection. IEEE Access, 8, 186304–186322. https://doi.org/10.1109/ACCESS.2020.3030562
Ajah, I. A., & Nweke, H. F. (2019). Big Data and Business Analytics: Trends, Platforms, Success Factors and Applications. Big Data Cogn. Comput., 3, 32. https://doi.org/10.3390/BDCC3020032
Almgerbi, M., De Mauro, A., Kahlawi, A., & Poggioni, V. (2021). A Systematic Review of Data Analytics Job Requirements and Online-Courses. Journal of Computer Information Systems, 62(3), 422–434. https://doi.org/10.1080/08874417.2021.1971579
Alshamaila, Y., Papagiannidis, S., & Li, F. (2013). Cloud computing adoption by SMEs in the north east of England: A multi-perspective framework. Journal of Enterprise Information Management, 26(3), 250–275. https://doi.org/10.1108/17410391311325225
Anmaç, S. (2023). A COMPARISON OF PRODUCT PERSONALIZATION AND PRODUCT CUSTOMIZATION: A CONCEPTUAL FRAMEWORK. Turkish Business Journal. https://doi.org/10.51727/tbj.1306824
Ardagna, C., Bellandi, V., Bezzi, M., Ceravolo, P., Damiani, E., & Hébert, C. (2018). Model-Based Big Data Analytics-as-a-Service: Take Big Data to the Next Level. IEEE Transactions on Services Computing, 14, 516–529. https://doi.org/10.1109/TSC.2018.2816941
Arena, F., & Pau, G. (2020). An overview of big data analysis. Bulletin of Electrical Engineering and Informatics, 9, 1646–1653. https://doi.org/10.11591/EEI.V9I4.2359
B?nic?, L., & Hagiu, A. (2015). BIG DATA IN BUSINESS ENVIRONMENT. Scientific Bulletin – Economic Sciences, 14, 79–86. https://consensus.app/papers/data-business-environment-b?nic?/57a5bbd9c8fd589192982c40ea447bd9
Baquero, A. V., Palacios, R. C., & Molloy, O. (2013). Business Process Analytics Using a Big Data Approach. IT Professional, 15, 29–35. https://doi.org/10.1109/MITP.2013.60
Bihl, T. J., Young, W. A., & Weckman, G. (2016). Defining, Understanding, and Addressing Big Data. International Journal of Business Analytics, 3, 1–32. https://doi.org/10.4018/IJBAN.2016040101
Blackburn, R., Lurz, K., Priese, B., Göb, R., & Darkow, I.-L. (2015). A predictive analytics approach for demand forecasting in the process industry. Int. Trans. Oper. Res., 22, 407–428. https://doi.org/10.1111/itor.12122
De Mauro, A., Greco, M., & Grimaldi, M. (2015). What is big data? A consensual definition and a review of key research topics. AIP Conference Proceedings, 1644, 97–104. https://doi.org/10.1063/1.4907823
Desai, A., Mathkar, M., Nisar, M., & Thampi, G. (2022). Customizing Insurance Product Based On Customer Data Leveraging Learning Algorithms. 2022 IEEE Fourth International Conference on Advances in Electronics, Computers and Communications (ICAECC), 1–7. https://doi.org/10.1109/ICAECC54045.2022.9716594
Di Francesco, P., Malavolta, I., & Lago, P. (2017). Research on architecting microservices: trends, focus, and potential for industrial adoption. Proceedings of the IEEE International Conference on Software Architecture, 21–30. https://doi.org/10.1109/ICSA.2017.10
Do?an, A., & Ar?, E. S. (2022). Big Data and Enterprise Applications. In Research Anthology on Big Data Analytics, Architectures, and Applications. https://doi.org/10.4018/978-1-6684-3662-2.ch039
Frizzo-Barker, J., Chow-White, P. A., Mozafari, M., & Ha, D. (2016). An empirical study of the rise of big data in business scholarship. International Journal of Information Management, 36, 403–413. https://doi.org/10.1016/J.IJINFOMGT.2016.01.006
George, G., Haas, M., & Pentland, A. (2014). Big Data and Management. Academy of Management Journal, 57, 321–326. https://doi.org/10.5465/AMJ.2014.4002
Gröger, C., Schwarz, H., & Mitschang, B. (2014). Prescriptive Analytics for Recommendation-Based Business Process Optimization. 25–37. https://doi.org/10.1007/978-3-319-06695-0_3
Grover, P., & Kar, A. (2017). Big Data Analytics: A Review on Theoretical Contributions and Tools Used in Literature. Global Journal of Flexible Systems Management, 18, 203–229. https://doi.org/10.1007/s40171-017-0159-3
Kanchana, R., & ShashikumarD., R. (2017). Big Data: Tools and Applications. International Journal of Advance Research, Ideas and Innovations in Technology, 3. https://consensus.app/papers/data-tools-applications-kanchana/cdbedd1a0d9f547b88572dd52b32634b
Kettunen, P., Ämmälä, M., & Partanen, J. (2015). Towards Predictable B2B Customer Satisfaction and Experience Management with Continuous Improvement Assets and Rich Feedback. 205–211. https://doi.org/10.1007/978-3-319-18612-2_18
Kotter, J. P. (1996). Leading change: Why transformation efforts fail. Harvard Business Review, 73(2), 59–67. https://consensus.app/papers/leading-change-why-transformation-efforts-fail-kotter/fe4521f505a7e0bc14859ed7c73601c1
Kshetri, N. (2014). Big data’s impact on privacy, security and consumer welfare. Telecommunications Policy, 38(11), 1134–1145. https://doi.org/10.1016/j.telpol.2014.10.002
Lehmann, D., Fekete, D., & Vossen, G. (2017). Technology Selection for Big Data and Analytical Applications. Open Journal of Big Data, 3, 1–25. https://consensus.app/papers/technology-selection-data-analytical-applications-lehmann/603a5820ac725418bd48d4de05b20ae8
Lishchynska, L., & Dobrovolska, N. (2022). Prospective Software Tools for Data Analysis in Business. Herald of Khmelnytskyi National University. https://doi.org/10.31891/2307-5732-2022-305-1-78-79
McAfee, A. P., & Brynjolfsson, E. (2012). Big data: the management revolution. Harvard Business Review, 90(10), 60–66, 68, 128. https://consensus.app/papers/data-management-revolution-mcafee/c9b86d405c6456ed8a56d259fd033c2f
Popescu, C. (2018). Improvements in business operations and customer experience through data science and Artificial Intelligence. Proceedings of the International Conference on Business Excellence, 12, 804–815. https://doi.org/10.2478/picbe-2018-0072
Rana, R., Paliwal, N., & Singhal, A. (2023). A Study of Business Insight Tool Using Big Data Analytics. 2023 International Conference on Computational Intelligence, Communication Technology and Networking (CICTN), 617–624. https://doi.org/10.1109/cictn57981.2023.10140726
Raut, R. D., Mangla, S., Narwane, V., Gardas, B. B., Priyadarshinee, P., & Narkhede, B. (2019). Linking Big Data Analytics and Operational Sustainability Practices for Sustainable Business Management. Journal of Cleaner Production. https://doi.org/10.1016/J.JCLEPRO.2019.03.181
Riahi, Y., & Riahi, S. (2018). Big Data and Big Data Analytics: concepts, types and technologies. International Journal of Research and Engineering, 5. https://doi.org/10.21276/IJRE.2018.5.9.5
Ruzgas, T., & Bagdonavi?ien?, J. D. (2017). Business Intelligence for Big Data Analytics. International Journal of Computer Applications Technology and Research, 6, 1–8. https://doi.org/10.7753/IJCATR0601.1001
Sadeghi, M., Zamanifar, K., & Fathi, M. (2017). An approach to interoperability of big data frameworks using semantic web technologies. Future Generation Computer Systems, 67, 29–42. https://doi.org/10.1016/j.future.2016.11.012
Sahal, R., Breslin, J., & Ali, M. (2020). Big data and stream processing platforms for Industry 4.0 requirements mapping for a predictive maintenance use case. Journal of Manufacturing Systems, 54, 138–151. https://doi.org/10.1016/j.jmsy.2019.11.004
Sandoval, L. (2015). Design of Business Intelligence Applications Using Big Data Technology. 2015 IEEE Thirty Fifth Central American and Panama Convention (CONCAPAN XXXV), 1–6. https://doi.org/10.1109/CONCAPAN.2015.7428454
Sun, Y., Shi, Y., & Zhang, Z. (2019). Finance Big Data: Management, Analysis, and Applications. International Journal of Electronic Commerce, 23, 11–29. https://doi.org/10.1080/10864415.2018.1512270
Vandure, S. L., Ramannavar, M. M., & Sidnal, N. (2014). Trend Projection using Predictive Analytics. International Journal of Computer Applications, 97, 39–45. https://doi.org/10.5120/17119-7807
Wilson, H., Macdonald, E. K., & Konus, U. (2012). Better customer insight - in real time?: a new tool radically improves marketing research. Harvard Business Review, 2012, 102–108. https://consensus.app/papers/better-customer-time-tool-radically-improves-marketing-wilson/ebbd3aab02bb5a7fa44355d578c4dfcc
Xiang, W., Deng, H., & Peng, S. (2022). Application of Big Data Technology in Business Management. Proceedings of the SPIE, 12331, 123315H-123315H – 5. https://doi.org/10.1117/12.2653082
Yin, S., & Kaynak, O. (2015). Big Data for Modern Industry: Challenges and Trends [Point of View]. Proceedings of the IEEE, 103, 143–146. https://doi.org/10.1109/JPROC.2015.2388958.
Descargas
Publicado
Cómo citar
Número
Sección
Licencia
Derechos de autor 2024 Jair Oswaldo Bedoya Benavides, Carol Dayana Góngora Saavedra, Maritza Elizabeth García Lino, Lilian Roció Ruiz Sepa
Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
Authors retain copyright and guarantee the Journal the right to be the first publication of the work. These are covered by a Creative Commons (CC BY-NC-ND 4.0) license that allows others to share the work with an acknowledgment of the work authorship and the initial publication in this journal.
