Futuro del Análisis de Datos: Una Revisión Sistemática de Integración y Estrategias Innovadoras

El futuro del análisis de datos representa un camino creciente, con la combinación de nuevas tecnologías y métodos: Inteligencia Artificial (IA) y el aprendizaje automático, cambiando la interpretación de muchos datos. Este campo en expansión reta los límites convencionales del análisis y fomenta un...

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Autores:: Amézquita Núñez, Juan David

Tipo de recurso:: Trabajo de grado de pregrado

Fecha de publicación:: 2024

Institución:: Universidad Santo Tomás

Repositorio:: Repositorio Institucional USTA

Idioma:: spa

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dc.title.spa.fl_str_mv	Futuro del Análisis de Datos: Una Revisión Sistemática de Integración y Estrategias Innovadoras
title	Futuro del Análisis de Datos: Una Revisión Sistemática de Integración y Estrategias Innovadoras
spellingShingle	Futuro del Análisis de Datos: Una Revisión Sistemática de Integración y Estrategias Innovadoras Data analysis Technological revolution Artificial Inteligence Machine Learning Análisis de datos Revolución tecnológica Inteligencia Artificial Machine Learning
title_short	Futuro del Análisis de Datos: Una Revisión Sistemática de Integración y Estrategias Innovadoras
title_full	Futuro del Análisis de Datos: Una Revisión Sistemática de Integración y Estrategias Innovadoras
title_fullStr	Futuro del Análisis de Datos: Una Revisión Sistemática de Integración y Estrategias Innovadoras
title_full_unstemmed	Futuro del Análisis de Datos: Una Revisión Sistemática de Integración y Estrategias Innovadoras
title_sort	Futuro del Análisis de Datos: Una Revisión Sistemática de Integración y Estrategias Innovadoras
dc.creator.fl_str_mv	Amézquita Núñez, Juan David
dc.contributor.advisor.none.fl_str_mv	Guío Ávila, Henry Alfonso
dc.contributor.author.none.fl_str_mv	Amézquita Núñez, Juan David
dc.contributor.orcid.Spa.fl_str_mv	https://orcid.org/0000-0003-1343-4302
dc.contributor.googlescholar.Spa.fl_str_mv	https://scholar.google.com/citations?hl=es&user=gqWnDVQAAAAJ
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dc.contributor.corporatename.spa.fl_str_mv	Universidad Santo Tomás
dc.subject.keyword.spa.fl_str_mv	Data analysis Technological revolution Artificial Inteligence Machine Learning
topic	Data analysis Technological revolution Artificial Inteligence Machine Learning Análisis de datos Revolución tecnológica Inteligencia Artificial Machine Learning
dc.subject.proposal.spa.fl_str_mv	Análisis de datos Revolución tecnológica Inteligencia Artificial Machine Learning
description	El futuro del análisis de datos representa un camino creciente, con la combinación de nuevas tecnologías y métodos: Inteligencia Artificial (IA) y el aprendizaje automático, cambiando la interpretación de muchos datos. Este campo en expansión reta los límites convencionales del análisis y fomenta un enfoque holístico y multidisciplinario que no favorece la toma de decisiones fundamentada en datos, sino que se centra en cuestiones éticas y preocupaciones de seguridad. A medida que se avanza, el análisis de datos demostrará ser una herramienta esencial para el progreso en todos los aspectos de la sociedad y los negocios. El análisis de datos se está transformando en una disciplina que no solo impulsa la innovación tecnológica, sino que también fomenta un cambio cultural hacia la responsabilidad y la transparencia. La integración de la Inteligencia Artificial y el Machine Learning está redefiniendo los paradigmas de la privacidad y la ética, exigiendo un nuevo marco que equilibre el poder de los datos con los derechos individuales. A medida que esta disciplina evoluciona, se convierte en el núcleo de una sociedad informada y consciente, donde cada byte de información es una oportunidad para mejorar la vida humana y fortalecer las estructuras empresariales. Se utilizaron para las búsquedas la base de datos de Scopus.
publishDate	2024
dc.date.accessioned.none.fl_str_mv	2024-06-19T20:40:18Z
dc.date.available.none.fl_str_mv	2024-06-19T20:40:18Z
dc.date.issued.none.fl_str_mv	2024
dc.type.local.spa.fl_str_mv	Trabajo de grado
dc.type.version.none.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.coar.none.fl_str_mv	http://purl.org/coar/resource_type/c_7a1f
dc.type.drive.none.fl_str_mv	info:eu-repo/semantics/bachelorThesis
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dc.identifier.citation.spa.fl_str_mv	Amézquita,J.(2024).Futuro del Análisis de Datos: Una Revisión Sistemática de Integración y Estrategias Innovadoras. [Trabajo de Grado, Universidad Santo Tomás].Repositorio Institucional.
dc.identifier.uri.none.fl_str_mv	http://hdl.handle.net/11634/55676
dc.identifier.reponame.spa.fl_str_mv	reponame:Repositorio Institucional Universidad Santo Tomás
dc.identifier.instname.spa.fl_str_mv	instname:Universidad Santo Tomás
dc.identifier.repourl.spa.fl_str_mv	repourl:https://repository.usta.edu.co
identifier_str_mv	Amézquita,J.(2024).Futuro del Análisis de Datos: Una Revisión Sistemática de Integración y Estrategias Innovadoras. [Trabajo de Grado, Universidad Santo Tomás].Repositorio Institucional. reponame:Repositorio Institucional Universidad Santo Tomás instname:Universidad Santo Tomás repourl:https://repository.usta.edu.co
url	http://hdl.handle.net/11634/55676
dc.language.iso.spa.fl_str_mv	spa
language	spa
dc.relation.references.spa.fl_str_mv	ONU, “Influencia de las tecnologías digitales.” https://www.un.org/es/un75/impact-digital-technologies M. Garouani, A. Ahmad, M. Bouneffa, M. Hamlich, G. Bourguin, and A. Lewandowski, “Towards big industrial data mining through explainable automated machine learning,” Int. J. Adv. Manuf. Technol., vol. 120, no. 1–2, pp. 1169–1188, 2022, doi: 10.1007/s00170-022-08761-9. Z. Ge, Z. Song, S. X. Ding, and B. Huang, “Data Mining and Analytics in the Process Industry: The Role of Machine Learning,” IEEE Access, vol. 5, pp. 20590–20616, 2017, doi: 10.1109/ACCESS.2017.2756872. J. Luo, W. Zhuo, S. Liu, and B. Xu, “The Optimization of Carbon Emission Prediction in Low Carbon Energy Economy under Big Data,” IEEE Access, vol. 12, no. December 2023, pp. 14690–14702, 2024, doi: 10.1109/ACCESS.2024.3351468. C. Chang, W. Shi, Y. Wang, Z. Zhang, X. Huang, and Y. Jiao, “The path from task-specific to general purpose artificial intelligence for medical diagnostics: A bibliometric analysis,” Comput. Biol. Med., vol. 172, no. February, p. 108258, 2024, doi: 10.1016/j.compbiomed.2024.108258. J. Kneifel, R. Roj, H. B. Woyand, R. Theiß, and P. Dültgen, “An IIoT-Device for Acquisition and Analysis of High-Frequency Data Processed by Artificial Intelligence,” Internet of Things, vol. 4, no. 3, pp. 244–264, 2023, doi: 10.3390/iot4030013. S. Trilles, S. S. Hammad, and D. Iskandaryan, “Anomaly detection based on Artificial Intelligence of Things: A Systematic Literature Mapping,” Internet of Things (Netherlands), vol. 25, no. October 2023, p. 101063, 2024, doi: 10.1016/j.iot.2024.101063. O. Cristina, “Análisis de inteligencia artificial: Cómo convertir datos en insights.” https://www.questionpro.com/blog/es/analisis-de-inteligencia artificial/ A. Muruganantham, P. T. Nguyen, E. L. Lydia, K. Shankar, W. Hashim, and A. Maseleno, “Big data analytics and intelligence: A perspective for health care,” Int. J. Eng. Adv. Technol., vol. 8, no. 6 Special Issue, pp. 861–864, 2019, doi: 10.35940/ijeat.F1162.0886S19. R. Dwivedi, S. Nerur, and V. Balijepally, “Exploring artificial intelligence and big data scholarship in information systems: A citation, bibliographic coupling, and co-word analysis,” Int. J. Inf. Manag. Data Insights, vol. 3, no. 2, 2023, doi: 10.1016/j.jjimei.2023.100185. 10.1016/j.jjimei.2023.100185. Z. Gan and S. B. Tsai, “Research on the Optimization Method of Visual Effect of Outdoor Interactive Advertising Assisted by New Media Technology and Big Data Analysis,” Math. Probl. [12] [29] tendencias-de-analisis-de-datos-que-debes-tener-en-cuenta Eng., vol. 2021, 2021, doi: 10.1155/2021/5341523. S. O. Uwagbole, W. J. Buchanan, and L. Fan, “An applied pattern-driven corpus to predictive analytics in mitigating SQL injection attack,” Proc. - 2017 7th Int. Conf. Emerg. Secur. Technol. EST 2017, pp. 12–17, 2017, doi: 10.1109/EST.2017.8090392. G. Deng, M. Xie, C. Feng, T. Liu, and X. Zha, “Flight test data processing and analysis platform based on new generation information technology Design and Application,” 2022 Int. Conf. Sensing, Meas. Data Anal. Era Artif. Intell. ICSMD 2022 - Proc., pp. 1–5, 2022, doi: 10.1109/ICSMD57530.2022.10058336. J. Li, “New Way of News Dissemination Based on Big Data Analysis and Visualization Technology,” Proc. - 2022 Int. Conf. Artif. Intell. Things Crowdsensing, AIoTCs 2022, pp. 436–440, 2022, doi: 10.1109/AIoTCs58181.2022.00074. W. Wei, “Automatic Design of Microcontroller System Simulation Based on Artificial Intelligence Technology and Data Intelligence Analysis,” Procedia Comput. Sci., vol. 228, pp. 966–973, 2023, doi: 10.1016/j.procs.2023.11.127. T. Wang, Y. Ma, Z. Wang, X. Wu, and L. Li, “User Behavior Analysis Based on Big Data and Artificial Intelligence,” Front. Artif. Intell. Appl., vol. 373, pp. 760–765, 2023, doi: 10.3233/FAIA230881. T. C. Radoi, “Artificial Intelligence in Data Analysis for Open Source Investigations,” 15th Int. Conf. Electron. Comput. Artif. Intell. ECAI 2023 - Proc., pp. 1–6, 2023, doi: 10.1109/ECAI58194.2023.10193894. Z. Wen, S. Han, Y. Yu, X. Xiang, S. Lin, and X. Xu, “Empowering robust biometric authentication: The fusion of deep learning and security image analysis,” Appl. Soft Comput., vol. 154, no. December 2023, p. 111286, 2024, doi: 10.1016/j.asoc.2024.111286. A. I. Tolulope, M. Isaac, O. Timileyin, S. Seth, and O. Kingsley, “Artificial intelligence research in Nigeria: Topic modelling and scientometric analysis,” IAES Int. J. Artif. Intell., vol. 13, no. 1, pp. 597–609, 2024, doi: 10.11591/ijai.v13.i1.pp597-609. A. Nambiar, S. Harikrishnaa, and S. Sharanprasath, “Model agnostic explainable artificial intelligence tools for severity prediction and symptom analysis on Indian COVID-19 data,” Front. Artif. Intell., vol. 6, 2023, doi: 10.3389/frai.2023.1272506. L. Sujatha, V. P. Parandhaman, S. Bhat, A. Kalnawat, N. Nirmala Devi, and P. M. Murali, “Analysis of Artificial intelligence Financial Innovation System based on Big Data Technology,” 2nd Int. Conf. Autom. Comput. Renew. Syst. ICACRS 2023 - Proc., pp. 383–389, 2023, doi: 10.1109/ICACRS58579.2023.10404844. B. Ramos-Cruz, J. Andreu-Perez, and L. Martínez, “The cybersecurity mesh: A comprehensive survey of involved artificial intelligence methods, cryptographic protocols and challenges for future research,” Neurocomputing, vol. 581, no. February, p. 127427, 2024, doi: 10.1016/j.neucom.2024.127427. V. Khilenko et al., “Increasing the Speed of Banking Cybersecurity Systems Based on Intelligent Data Analysis and Artificial Intelligence Algorithms for Predicting Cyberattacks. I,” Cybern. Syst. Anal., vol. 59, no. 4, pp. 519–525, 2023, doi: 10.1007/s10559-023-00587-x. B. Shrestha, S. Cho, and C. Seo, “Special Issue on Data Analysis and Artificial Intelligence for IoT,” Appl. Sci., vol. 13, no. 11, pp. 1–5, 2023, doi: 10.3390/app13116401. S. Issn, “Revisión sistemática de teorías de integración de sistemas de,” 2018. X. Wang et al., “VIS+AI: integrating visualization with artificial intelligence for efficient data analysis,” Front. Comput. Sci., vol. 17, no. 6, 2023, doi: 10.1007/s11704-023-2691-y. K. Panetta, “Las 10 tendencias principales de datos y análisis de Gartner para 2021,” 2021. https://www.gartner.es/es/articulos/las-10-tendencias principales-de-datos-y-analisis-de-gartner-para-2021 L. Goasduff, “12 tendencias de análisis de datos que debes tener en cuenta,” 2022. https://www.gartner.mx/tendencias-de-analisis-de-datos-que-debes-tener-en-cuentaes/articulos/12- P. R. Crespo, “5 pasos para implementar una estrategia de data y analítica exitosa,” 2024. https://dataiq.com.ar/blog/5-pasos-para implementar-una-estrategia-de-data-y-analitica-exitosa/ Y. Kong, Y. Yu, and X. Cui, “Requirement of densely distributed PV grid-connected data analysis technology in the future new power system,” Proc. - 2023 38th Youth Acad. Annu. Conf. Chinese Assoc. Autom. YAC 2023, pp. 1298–1302, 2023, doi: 10.1109/YAC59482.2023.10401470. doi: 10.1109/YAC59482.2023.10401470. M. Gezimati and G. Singh, “Terahertz Data Extraction and Analysis Based on Deep Learning Techniques for Emerging Applications,” IEEE Access, vol. 12, no. January, pp. 21174 21198, 2024, doi: 10.1109/ACCESS.2024.3360930. M. K. Nallakaruppan, S. R. K. Somayaji, S. Fuladi, F. Benedetto, S. K. Ulaganathan, and G. Yenduri, “Enhancing Security of Host-Based Intrusion Detection Systems for the Internet of Things,” IEEE Access, vol. 12, no. December 2023, pp. 31788–31797, 2024, doi: 10.1109/ACCESS.2024.3355794. pp. 31788–31797, 2024, doi: 10.1109/ACCESS.2024.3355794. Y. Huang, Z. Cheng, Q. Zhou, Y. Xiang, and R. Zhao, “Data mining algorithm for cloud network information based on artificial intelligence decision mechanism,” IEEE Access, vol. 8, pp. 53394–53407, 2020, doi: 10.1109/ACCESS.2020.2981632. Z. Wang, C., Zhang, Y., Ding, H., “Applied Mathematics and Nonlinear Sciences,” Appl. Math. Nonlinear Sci., vol. 8, no. 2, pp. 3383–3392, 2023. C. Machello, M. Bazli, A. Rajabipour, H. M. Rad, M. Arashpour, and A. Hadigheh, “Using machine learning to predict the long-term performance of fibre-reinforced polymer structures: A state-of-the-art review,” Constr. Build. Mater., vol. 408, no. December 2022, p. 133692, 2023, doi: 10.1016/j.conbuildmat.2023.133692. “Metodología de la investigación 7ma Ed - Hernandez Sampieri C. Ortega, “¿Qué es la metodología de la investigación?,” 2024. https://www.questionpro.com/blog/es/metodologia-de-la investigacion/ A. Amirteimoori, T. Allahviranloo, M. Zadmirzaei, and F. 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spelling	Guío Ávila, Henry AlfonsoAmézquita Núñez, Juan Davidhttps://orcid.org/0000-0003-1343-4302https://scholar.google.com/citations?hl=es&user=gqWnDVQAAAAJhttps://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001519211Universidad Santo Tomás2024-06-19T20:40:18Z2024-06-19T20:40:18Z2024Amézquita,J.(2024).Futuro del Análisis de Datos: Una Revisión Sistemática de Integración y Estrategias Innovadoras. [Trabajo de Grado, Universidad Santo Tomás].Repositorio Institucional.http://hdl.handle.net/11634/55676reponame:Repositorio Institucional Universidad Santo Tomásinstname:Universidad Santo Tomásrepourl:https://repository.usta.edu.coEl futuro del análisis de datos representa un camino creciente, con la combinación de nuevas tecnologías y métodos: Inteligencia Artificial (IA) y el aprendizaje automático, cambiando la interpretación de muchos datos. Este campo en expansión reta los límites convencionales del análisis y fomenta un enfoque holístico y multidisciplinario que no favorece la toma de decisiones fundamentada en datos, sino que se centra en cuestiones éticas y preocupaciones de seguridad. A medida que se avanza, el análisis de datos demostrará ser una herramienta esencial para el progreso en todos los aspectos de la sociedad y los negocios. El análisis de datos se está transformando en una disciplina que no solo impulsa la innovación tecnológica, sino que también fomenta un cambio cultural hacia la responsabilidad y la transparencia. La integración de la Inteligencia Artificial y el Machine Learning está redefiniendo los paradigmas de la privacidad y la ética, exigiendo un nuevo marco que equilibre el poder de los datos con los derechos individuales. A medida que esta disciplina evoluciona, se convierte en el núcleo de una sociedad informada y consciente, donde cada byte de información es una oportunidad para mejorar la vida humana y fortalecer las estructuras empresariales. Se utilizaron para las búsquedas la base de datos de Scopus.The future of data analysis represents a growing path, The integration of new technologies and methods, such as Artificial Intelligence and Machine Learning, is altering the interpretation of several data. This growing field challenges traditional analytical boundaries and promotes an integrated, multidisciplinary approach that does not promote data-driven decision making, but rather focuses on ethical issues and security concerns. As we move forward, data analytics will turn out to be a crucial instrument for progress in all aspects of society and business. Data analytics is becoming a discipline that not only drives technological innovation, but also fosters a cultural shift toward accountability and transparency. The incorporation of artificial intelligence and Machine Learning is redefining the paradigms of privacy and ethics, demanding a new framework that balances the power of data with individual rights. As this discipline evolves, it becomes the core of an informed and conscious society, where every bite of information is an opportunity to improve human life and strengthen business structures, Scopus databases were used for searches.Ingeniero InformáticoPregradoapplication/pdfspaUniversidad Santo TomásIngeniería InformáticaFacultad de Ingeniería de SistemasAtribución-NoComercial-SinDerivadas 2.5 Colombiahttp://creativecommons.org/licenses/by-nc-nd/2.5/co/Abierto (Texto Completo)info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Futuro del Análisis de Datos: Una Revisión Sistemática de Integración y Estrategias InnovadorasData analysisTechnological revolutionArtificial InteligenceMachine LearningAnálisis de datosRevolución tecnológicaInteligencia ArtificialMachine LearningTrabajo de gradoinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_7a1finfo:eu-repo/semantics/bachelorThesisCRAI-USTA TunjaONU, “Influencia de las tecnologías digitales.” https://www.un.org/es/un75/impact-digital-technologiesM. Garouani, A. Ahmad, M. Bouneffa, M. Hamlich, G. Bourguin, and A. Lewandowski, “Towards big industrial data mining through explainable automated machine learning,” Int. J. Adv. Manuf. Technol., vol. 120, no. 1–2, pp. 1169–1188, 2022, doi: 10.1007/s00170-022-08761-9.Z. Ge, Z. Song, S. X. Ding, and B. Huang, “Data Mining and Analytics in the Process Industry: The Role of Machine Learning,” IEEE Access, vol. 5, pp. 20590–20616, 2017, doi: 10.1109/ACCESS.2017.2756872.J. Luo, W. Zhuo, S. Liu, and B. Xu, “The Optimization of Carbon Emission Prediction in Low Carbon Energy Economy under Big Data,” IEEE Access, vol. 12, no. December 2023, pp. 14690–14702, 2024, doi: 10.1109/ACCESS.2024.3351468.C. Chang, W. Shi, Y. Wang, Z. Zhang, X. Huang, and Y. Jiao, “The path from task-specific to general purpose artificial intelligence for medical diagnostics: A bibliometric analysis,” Comput. Biol. Med., vol. 172, no. February, p. 108258, 2024, doi: 10.1016/j.compbiomed.2024.108258.J. Kneifel, R. Roj, H. B. Woyand, R. Theiß, and P. Dültgen, “An IIoT-Device for Acquisition and Analysis of High-Frequency Data Processed by Artificial Intelligence,” Internet of Things, vol. 4, no. 3, pp. 244–264, 2023, doi: 10.3390/iot4030013.S. Trilles, S. S. Hammad, and D. Iskandaryan, “Anomaly detection based on Artificial Intelligence of Things: A Systematic Literature Mapping,” Internet of Things (Netherlands), vol. 25, no. October 2023, p. 101063, 2024, doi: 10.1016/j.iot.2024.101063.O. Cristina, “Análisis de inteligencia artificial: Cómo convertir datos en insights.” https://www.questionpro.com/blog/es/analisis-de-inteligencia artificial/A. Muruganantham, P. T. Nguyen, E. L. Lydia, K. Shankar, W. Hashim, and A. Maseleno, “Big data analytics and intelligence: A perspective for health care,” Int. J. Eng. Adv. Technol., vol. 8, no. 6 Special Issue, pp. 861–864, 2019, doi: 10.35940/ijeat.F1162.0886S19.R. Dwivedi, S. Nerur, and V. 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