Evaluación de la capacidad de análisis de datos de las pymes desarrolladoras de software de la ciudad de Bogotá

Ilustraciones y tablas

Autores:
Angulo Romero, Carlos Aurelio
Tipo de recurso:
Fecha de publicación:
2021
Institución:
Universidad Nacional de Colombia
Repositorio:
Universidad Nacional de Colombia
Idioma:
spa
OAI Identifier:
oai:repositorio.unal.edu.co:unal/80142
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/80142
https://repositorio.unal.edu.co/
Palabra clave:
000 - Ciencias de la computación, información y obras generales
Computer software development
Desarrollo de programas para computador
Capacidad de análisis
Pymes
Analytics Maturity (TDWI)
Analysis capacity
Data analysis
Análisis de datos
Data processing
Procesamiento de datos
Rights
openAccess
License
Atribución-CompartirIgual 4.0 Internacional
id UNACIONAL2_e0076ead71367f1b171ac513c513a40b
oai_identifier_str oai:repositorio.unal.edu.co:unal/80142
network_acronym_str UNACIONAL2
network_name_str Universidad Nacional de Colombia
repository_id_str
dc.title.spa.fl_str_mv Evaluación de la capacidad de análisis de datos de las pymes desarrolladoras de software de la ciudad de Bogotá
dc.title.translated.eng.fl_str_mv Evaluation of the data analysis capacity of software development SMEs in the city of Bogota
title Evaluación de la capacidad de análisis de datos de las pymes desarrolladoras de software de la ciudad de Bogotá
spellingShingle Evaluación de la capacidad de análisis de datos de las pymes desarrolladoras de software de la ciudad de Bogotá
000 - Ciencias de la computación, información y obras generales
Computer software development
Desarrollo de programas para computador
Capacidad de análisis
Pymes
Analytics Maturity (TDWI)
Analysis capacity
Data analysis
Análisis de datos
Data processing
Procesamiento de datos
title_short Evaluación de la capacidad de análisis de datos de las pymes desarrolladoras de software de la ciudad de Bogotá
title_full Evaluación de la capacidad de análisis de datos de las pymes desarrolladoras de software de la ciudad de Bogotá
title_fullStr Evaluación de la capacidad de análisis de datos de las pymes desarrolladoras de software de la ciudad de Bogotá
title_full_unstemmed Evaluación de la capacidad de análisis de datos de las pymes desarrolladoras de software de la ciudad de Bogotá
title_sort Evaluación de la capacidad de análisis de datos de las pymes desarrolladoras de software de la ciudad de Bogotá
dc.creator.fl_str_mv Angulo Romero, Carlos Aurelio
dc.contributor.advisor.none.fl_str_mv Sánchez-Torres, Jenny Marcela
dc.contributor.author.none.fl_str_mv Angulo Romero, Carlos Aurelio
dc.contributor.researchgroup.spa.fl_str_mv GRIEGO (Grupo Investigación en Gestión y Organizaciones)
dc.subject.ddc.spa.fl_str_mv 000 - Ciencias de la computación, información y obras generales
topic 000 - Ciencias de la computación, información y obras generales
Computer software development
Desarrollo de programas para computador
Capacidad de análisis
Pymes
Analytics Maturity (TDWI)
Analysis capacity
Data analysis
Análisis de datos
Data processing
Procesamiento de datos
dc.subject.lemb.none.fl_str_mv Computer software development
Desarrollo de programas para computador
dc.subject.proposal.spa.fl_str_mv Capacidad de análisis
Pymes
dc.subject.proposal.eng.fl_str_mv Analytics Maturity (TDWI)
Analysis capacity
dc.subject.unesco.none.fl_str_mv Data analysis
Análisis de datos
Data processing
Procesamiento de datos
description Ilustraciones y tablas
publishDate 2021
dc.date.accessioned.none.fl_str_mv 2021-09-09T14:51:28Z
dc.date.available.none.fl_str_mv 2021-09-09T14:51:28Z
dc.date.issued.none.fl_str_mv 2021
dc.type.spa.fl_str_mv Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv Text
dc.type.redcol.spa.fl_str_mv http://purl.org/redcol/resource_type/TM
status_str acceptedVersion
dc.identifier.uri.none.fl_str_mv https://repositorio.unal.edu.co/handle/unal/80142
dc.identifier.instname.spa.fl_str_mv Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv https://repositorio.unal.edu.co/
url https://repositorio.unal.edu.co/handle/unal/80142
https://repositorio.unal.edu.co/
identifier_str_mv Universidad Nacional de Colombia
Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv spa
language spa
dc.relation.references.spa.fl_str_mv Alderson, P., Green, S., & Higgins, J. (2004). Cochrane reviewers’ handbook, version 4.2.2. (The Cochrane Collaboration (ed.)). https://www.mv.helsinki.fi/home/hemila/karlowski/handbook_4_2_2_Karlowski.pdf
Bean, R., & Davenport, T. (2019). Companies are failing in their efforts to become data-driven. Harvard Business Review.
Bedeley, R. T., & Nemati, H. (2014). Big Data Analytics: A key capability for competitive advantage. 20th Americas Conference on Information Systems, AMCIS 2014. https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1536&context=amcis2014
Bihani, P., & Patil, S. (2014). A comparative study of data analysis techniques. International Journal of Emerging Trends & Technology in Computer Science, 3(2), 95–101. www.ijettcs.org
Bonilla, J., & Rao, B. (2015). Decoding data analytics capabilities from topic modeling on press releases. Portland International Conference on Management of Engineering and Technology, 2015-Septe, 1959–1968. https://doi.org/10.1109/PICMET.2015.7273249
Bryman, A., & Cramer, D. (1992). Quantitative data analysis for social scientists. Estudios Geográficos, 53(207), 347. http://search.proquest.com/openview/71ba8781f88a269938b860d448e6e8d2/1?pq-origsite=gscholar&cbl=1818083
Cates, J. E., Gill, S. S., & Zeituny, N. (2005). The Ladder of Business Intelligence (LOBI): a framework for enterprise IT planning and architecture. International Journal of Business Information Systems, 1(1/2), 220. https://doi.org/10.1504/IJBIS.2005.007408
Coleman, S. (2016). Data-mining opportunities for small and medium enterprises with official statistics in the UK. Journal of Official Statistics. https://doi.org/10.1515/JOS-2016-0044
Creswell, J. W., & Creswell, J. D. (2017). Research design: Qualitative, quantitative, and mixed methods approaches. Sage publications.
Davenport, T., & Harris, J. G. (2017). Competing on Analytics: The New Science of Winning. Harvard Business School Press.
Denning, D. E., Nicholson, W., Sande, G., & Shoshani, A. (1984). Research topics in statistical database management. IEEE Database Eng. Bull., 7(1), 4–9.
Dinter, B. (2012). The maturing of a business intelligence maturity model. AMCIS 2012 Proceedings. http://elibrary.aisnet.org/Default.aspx?url=https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1083&context=amcis2012
Du, X., Liu, B., & Zhang, J. (2019). Application of Business Intelligence Based on Big Data in E-commerce Data Analysis. Journal of Physics: Conference Series, 1395(1), 012011. https://doi.org/10.1088/1742-6596/1395/1/012011
Dulcé, H. J. (2016). Datos, información y conocimiento. Respuestas, 21(1), 4. https://revistas.ufps.edu.co/index.php/respuestas/article/download/642/646
Eason, K. D. (1989). Information technology and organisational change. CRC Press.
Eckerson, W. (2007). TDWI benchmark guide: interpreting benchmark scores using TDWI’s maturity model. TDWI Research, 3–14.
Garmaki, M., & Boughzala, I. (2016). Conceptualization of Big data analytics Capability based on IT capability: Primary findings. 21st Symposium of the Association Information and Management 2016, AIM 2016.
Grossman, R. L. (2018). A framework for evaluating the analytic maturity of an organization. International Journal of Information Management, 38(1), 45–51. https://doi.org/10.1016/j.ijinfomgt.2017.08.005
Gupta, M., & George, J. F. (2016). Toward the development of a big data analytics capability. Information & Management, 53(8), 1049–1064.
Halper, F. (2020). TDWI Analytics Maturity Model Guide: Assessment Guide.
Halper, F., & Stodder, D. (2014). TDWI Analytics Maturity Model Guide. In TDWI Research. https://www3.microstrategy.com/getmedia/9b914607-084f-4869-ae64-e0b3f9e003de/TDWI_Analytics-Maturity-Guide_2014-2015.pdf
Hao, S., Zhang, H., & Song, M. (2019). Big data, big data analytics capability, and sustainable innovation performance. Sustainability, 11(24), 7145.
Hatta, N. N. M., Miskon, S., Ali, N. M., Abdullah, N. S., Ahmad, N., Hashim, H., Alias, R. A., & Maarof, M. A. (2015). Business intelligence system adoption theories in SMES: A literature review. ARPN Journal of Engineering and Applied Sciences.
Hicks, S. C., & Peng, R. D. (2019). Elements and Principles for Characterizing Variation between Data Analyses. ArXiv Preprint ArXiv:1903.07639. http://arxiv.org/abs/1903.07639
Hirvonen, J., & Majuri, M. (2020). Digital capabilities in manufacturing SMEs. Procedia Manufacturing, 51, 1283–1289. https://doi.org/10.1016/j.promfg.2020.10.179
Humphrey, W. S. (1989). Managing the software process. Addison-Wesley Longman Publishing Co., Inc.
Irwin, S. (2008). Data analysis and interpretation: emergent issues in linking qualitative and quantitative evidence. In Guilford Publications (Ed.), Handbook of emergent methods in social research (pp. 415–435).
Islam, M. (2020). Data Analysis: Types, Process, Methods, Techniques and Tools. International Journal on Data Science and Technology, 6(1), 10. https://doi.org/10.11648/j.ijdst.20200601.12
Joyanes, L. (2013). Big Data, Análisis de grandes volúmenes de datos en organizaciones (Alfaomega Grupo Editor (ed.)).
Kamioka, T., Hosoya, R., & Tapanainen, T. (2017). Effects of User IT Capabilities and Organized Big Data Analytics on Competitive Advantage. PACIS, 36.
Khan, K. S., Ter Riet, G., Glanville, J., Sowden, A. J., & Kleijnen, J. (2001). Undertaking systematic reviews of research on effectiveness: CRD’s guidance for carrying out or commissioning reviews.
Kitchenham, B. (2004). Procedures for performing systematic reviews. Keele University,UK and National ICT Australia. https://doi.org/10.1.1.122.3308
Kumar, V., Goyal, P., & Vandana, R. J. G. (2017). Stakeholder Classification: A Sustainability Marketing Perspective. EVIDENCE BASED MANAGEMENT, 111.
Ladley, J. (2010). Making enterprise information management (EIM) work for business: A guide to understanding information as an asset. Morgan Kaufmann.
Letón, M., & Pedromingo, A. (2001). Introducción al Análisis de Datos en Meta-Análisis. Ediciones Díaz de Santos.
Luhn, H. P. (1958). A Business Intelligence System. IBM Journal of Research and Development, 2(4), 314–319. https://doi.org/10.1147/rd.24.0314
Martínez, C. (2014). Técnicas e Instrumentos de Recogida y Análisis de Datos. Editorial UNED.
Marulanda, C. E., López, M., & Mejía, M. H. (2013). Minería de datos en gestión del conocimiento de pymes de Colombia. Revista Virtual Universidad Católica Del Norte, 1(38), 158-170–170. https://revistavirtual.ucn.edu.co/index.php/RevistaUCN/article/view/821/1339
Masood, T., & Sonntag, P. (2020). Industry 4.0: Adoption challenges and benefits for SMEs. Computers in Industry, 121, 103261. https://doi.org/10.1016/j.compind.2020.103261
Maxwell, J. A., & Chmiel, M. (2014). Notes toward a theory of qualitative data analysis. In The SAGE handbook of qualitative data analysis (pp. 21–34). Sage Thousand Oaks, CA.
Meyer, S. L. (1975). Data Analysis For Scientists And Engineers (Wiley (ed.)). https://doi.org/10.1007/978-3-319-03762-2__1
Mikalef, P., Boura, M., Lekakos, G., & Krogstie, J. (2018). Complementarities between information governance and big data analytics capabilities on innovation. 26th European Conference on Information Systems: Beyond Digitization - Facets of Socio-Technical Change, ECIS 2018.
Mikalef, P., Krogstie, J., Pappas, I. O., & Pavlou, P. (2020). Exploring the relationship between big data analytics capability and competitive performance: The mediating roles of dynamic and operational capabilities. Information and Management, 57(2). https://doi.org/10.1016/j.im.2019.05.004
Nagappan, M., Sam, S., Sangeetha, S., Nithya Priya, S., Suguna, N., & Scholar, U. G. (2019). Heart Disease Prediction Using Data Mining Technique. In Shodhshauryam, International Scientific Refereed Research Journal © 2019 SHISRRJ (Vol. 2, Issue 10). www.shisrrj.com
Olszak, C. M. (2013). Assessment of business intelligence maturity in the selected organizations. 2013 Federated Conference on Computer Science and Information Systems, 951–958.
Partners New Vantage, L. (2019). Big data and AI executive survey 2019. Data and Innovation. How Big Data and AI are Accelerating Business Transformation. http://newvantage.com/wp-content/uploads/2018/12/Big-Data-Executive-Survey-2019-Findings-Updated-010219-1.pdf
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Bean, R., & Davenport, T. (2019). Companies are failing in their efforts to become data-driven. Harvard Business Review.
Bedeley, R. T., & Nemati, H. (2014). Big Data Analytics: A key capability for competitive advantage. 20th Americas Conference on Information Systems, AMCIS 2014. https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1536&context=amcis2014
Bihani, P., & Patil, S. (2014). A comparative study of data analysis techniques. International Journal of Emerging Trends & Technology in Computer Science, 3(2), 95–101. www.ijettcs.org
Bonilla, J., & Rao, B. (2015). Decoding data analytics capabilities from topic modeling on press releases. Portland International Conference on Management of Engineering and Technology, 2015-Septe, 1959–1968. https://doi.org/10.1109/PICMET.2015.7273249
Bryman, A., & Cramer, D. (1992). Quantitative data analysis for social scientists. Estudios Geográficos, 53(207), 347. http://search.proquest.com/openview/71ba8781f88a269938b860d448e6e8d2/1?pq-origsite=gscholar&cbl=1818083
Cates, J. E., Gill, S. S., & Zeituny, N. (2005). The Ladder of Business Intelligence (LOBI): a framework for enterprise IT planning and architecture. International Journal of Business Information Systems, 1(1/2), 220. https://doi.org/10.1504/IJBIS.2005.007408
Coleman, S. (2016). Data-mining opportunities for small and medium enterprises with official statistics in the UK. Journal of Official Statistics. https://doi.org/10.1515/JOS-2016-0044
Creswell, J. W., & Creswell, J. D. (2017). Research design: Qualitative, quantitative, and mixed methods approaches. Sage publications.
Davenport, T., & Harris, J. G. (2017). Competing on Analytics: The New Science of Winning. Harvard Business School Press.
Denning, D. E., Nicholson, W., Sande, G., & Shoshani, A. (1984). Research topics in statistical database management. IEEE Database Eng. Bull., 7(1), 4–9.
Dinter, B. (2012). The maturing of a business intelligence maturity model. AMCIS 2012 Proceedings. http://elibrary.aisnet.org/Default.aspx?url=https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1083&context=amcis2012
Du, X., Liu, B., & Zhang, J. (2019). Application of Business Intelligence Based on Big Data in E-commerce Data Analysis. Journal of Physics: Conference Series, 1395(1), 012011. https://doi.org/10.1088/1742-6596/1395/1/012011
Dulcé, H. J. (2016). Datos, información y conocimiento. Respuestas, 21(1), 4. https://revistas.ufps.edu.co/index.php/respuestas/article/download/642/646
Eason, K. D. (1989). Information technology and organisational change. CRC Press.
Eckerson, W. (2007). TDWI benchmark guide: interpreting benchmark scores using TDWI’s maturity model. TDWI Research, 3–14.
Garmaki, M., & Boughzala, I. (2016). Conceptualization of Big data analytics Capability based on IT capability: Primary findings. 21st Symposium of the Association Information and Management 2016, AIM 2016.
Grossman, R. L. (2018). A framework for evaluating the analytic maturity of an organization. International Journal of Information Management, 38(1), 45–51. https://doi.org/10.1016/j.ijinfomgt.2017.08.005
Gupta, M., & George, J. F. (2016). Toward the development of a big data analytics capability. Information & Management, 53(8), 1049–1064.
Halper, F. (2020). TDWI Analytics Maturity Model Guide: Assessment Guide.
Halper, F., & Stodder, D. (2014). TDWI Analytics Maturity Model Guide. In TDWI Research. https://www3.microstrategy.com/getmedia/9b914607-084f-4869-ae64-e0b3f9e003de/TDWI_Analytics-Maturity-Guide_2014-2015.pdf
Hao, S., Zhang, H., & Song, M. (2019). Big data, big data analytics capability, and sustainable innovation performance. Sustainability, 11(24), 7145.
Hatta, N. N. M., Miskon, S., Ali, N. M., Abdullah, N. S., Ahmad, N., Hashim, H., Alias, R. A., & Maarof, M. A. (2015). Business intelligence system adoption theories in SMES: A literature review. ARPN Journal of Engineering and Applied Sciences.
Hicks, S. C., & Peng, R. D. (2019). Elements and Principles for Characterizing Variation between Data Analyses. ArXiv Preprint ArXiv:1903.07639. http://arxiv.org/abs/1903.07639
Hirvonen, J., & Majuri, M. (2020). Digital capabilities in manufacturing SMEs. Procedia Manufacturing, 51, 1283–1289. https://doi.org/10.1016/j.promfg.2020.10.179
Humphrey, W. S. (1989). Managing the software process. Addison-Wesley Longman Publishing Co., Inc.
Irwin, S. (2008). Data analysis and interpretation: emergent issues in linking qualitative and quantitative evidence. In Guilford Publications (Ed.), Handbook of emergent methods in social research (pp. 415–435).
Islam, M. (2020). Data Analysis: Types, Process, Methods, Techniques and Tools. International Journal on Data Science and Technology, 6(1), 10. https://doi.org/10.11648/j.ijdst.20200601.12
Joyanes, L. (2013). Big Data, Análisis de grandes volúmenes de datos en organizaciones (Alfaomega Grupo Editor (ed.)).
Kamioka, T., Hosoya, R., & Tapanainen, T. (2017). Effects of User IT Capabilities and Organized Big Data Analytics on Competitive Advantage. PACIS, 36.
Khan, K. S., Ter Riet, G., Glanville, J., Sowden, A. J., & Kleijnen, J. (2001). Undertaking systematic reviews of research on effectiveness: CRD’s guidance for carrying out or commissioning reviews.
Kitchenham, B. (2004). Procedures for performing systematic reviews. Keele University,UK and National ICT Australia. https://doi.org/10.1.1.122.3308
Kumar, V., Goyal, P., & Vandana, R. J. G. (2017). Stakeholder Classification: A Sustainability Marketing Perspective. EVIDENCE BASED MANAGEMENT, 111.
Ladley, J. (2010). Making enterprise information management (EIM) work for business: A guide to understanding information as an asset. Morgan Kaufmann.
Letón, M., & Pedromingo, A. (2001). Introducción al Análisis de Datos en Meta-Análisis. Ediciones Díaz de Santos.
Luhn, H. P. (1958). A Business Intelligence System. IBM Journal of Research and Development, 2(4), 314–319. https://doi.org/10.1147/rd.24.0314
Martínez, C. (2014). Técnicas e Instrumentos de Recogida y Análisis de Datos. Editorial UNED.
Marulanda, C. E., López, M., & Mejía, M. H. (2013). Minería de datos en gestión del conocimiento de pymes de Colombia. Revista Virtual Universidad Católica Del Norte, 1(38), 158-170–170. https://revistavirtual.ucn.edu.co/index.php/RevistaUCN/article/view/821/1339
Masood, T., & Sonntag, P. (2020). Industry 4.0: Adoption challenges and benefits for SMEs. Computers in Industry, 121, 103261. https://doi.org/10.1016/j.compind.2020.103261
Maxwell, J. A., & Chmiel, M. (2014). Notes toward a theory of qualitative data analysis. In The SAGE handbook of qualitative data analysis (pp. 21–34). Sage Thousand Oaks, CA.
Meyer, S. L. (1975). Data Analysis For Scientists And Engineers (Wiley (ed.)). https://doi.org/10.1007/978-3-319-03762-2__1
Mikalef, P., Boura, M., Lekakos, G., & Krogstie, J. (2018). Complementarities between information governance and big data analytics capabilities on innovation. 26th European Conference on Information Systems: Beyond Digitization - Facets of Socio-Technical Change, ECIS 2018.
Mikalef, P., Krogstie, J., Pappas, I. O., & Pavlou, P. (2020). Exploring the relationship between big data analytics capability and competitive performance: The mediating roles of dynamic and operational capabilities. Information and Management, 57(2). https://doi.org/10.1016/j.im.2019.05.004
Nagappan, M., Sam, S., Sangeetha, S., Nithya Priya, S., Suguna, N., & Scholar, U. G. (2019). Heart Disease Prediction Using Data Mining Technique. In Shodhshauryam, International Scientific Refereed Research Journal © 2019 SHISRRJ (Vol. 2, Issue 10). www.shisrrj.com
Olszak, C. M. (2013). Assessment of business intelligence maturity in the selected organizations. 2013 Federated Conference on Computer Science and Information Systems, 951–958.
Partners New Vantage, L. (2019). Big data and AI executive survey 2019. Data and Innovation. How Big Data and AI are Accelerating Business Transformation. http://newvantage.com/wp-content/uploads/2018/12/Big-Data-Executive-Survey-2019-Findings-Updated-010219-1.pdf
Prieto, R., Meneses, C., & Vega, V. (2015). Análisis comparativo de modelos de madurez en inteligencia de negocio. Ingeniare. Revista Chilena de Ingeniería, 23(3), 361–371. https://doi.org/10.4067/S0718-33052015000300005
Ramamurthy, A. (2017). Effective information management - A - big- data driven road map for enterprise decision making. Water Environment Federation Technical Exhibition and Conference 2017, WEFTEC 2017, 3, 1854–1865.
Ramesh, G. S., Rajini Kanth, T. V., & Vasumathi, D. (2020). A Comparative Study of Data Mining Tools and Techniques for Business Intelligence (Springer (ed.); pp. 163–173). https://doi.org/10.1007/978-981-13-8253-6_15
Ramsay, J. O. (2006). Functional Data Analysis. In Encyclopedia of Statistical Sciences. John Wiley & Sons, Inc. https://doi.org/10.1002/0471667196.ess3138
Reio, T. G. (2016). Nonexperimental research: strengths, weaknesses and issues of precision. European Journal of Training and Development, 40(8/9), 676–690. https://doi.org/10.1108/EJTD-07-2015-0058
Rutkowski, L., Jaworski, M., & Duda, P. (2020). Basic Concepts of Data Stream Mining (pp. 13–33). https://doi.org/10.1007/978-3-030-13962-9_2
Sampieri, H. (2018). Metodología de la investigación: las rutas cuantitativa, cualitativa y mixta (M. Hill (ed.)).
Schab, E., Rivera, R., Bracco, L., Coto, F., Cristaldo, P., Ramos, L., Rapesta, N., Pablo Núñez, J., Retamar, S., Casanova, C., De Battista, A., & Herrera, N. E. (2018). Minería de Datos y Visualización de Información. XX Workshop de Investigadores En Ciencias de La Computaci´on. http://hdl.handle.net/20.500.12272/3567
Sen, D., Ozturk, M. &, & Vayvay, O. (2016). An Overview of Big Data for Growth in SMEs. Procedia - Social and Behavioral Sciences, 235(2016), 159–167. https://doi.org/10.1016/j.sbspro.2016.11.011
Shaaban, E., Helmy, Y., Khedr, A., Nasr, M., & others. (2011). Business intelligence maturity models: Toward new integrated model. The International Arab Conference on Information Technology (ACIT 2011), Organized by Naif Arab University for Security Science (NAUSS), Riyadh, Saudi Arabia, 11–14.
Shuradze, G., & Wagner, H. T. (2016). Governing for agility and innovation in data-rich environments: The role of data analytics capabilities. 24th European Conference on Information Systems, ECIS 2016.
Song, M., Zhang, H., & Heng, J. (2020). Creating sustainable innovativeness through big data and big data analytics capability: From the perspective of the information processing theory. Sustainability (Switzerland), 12(5). https://doi.org/10.3390/su12051984
Tan, P.-N., Steinbach, M., & Kumar, V. (2006). Introduction to data mining. Pearson Education India.
Tukey, J., & Wilk, M. (1966). Data analysis and statistics: an expository overview. Proceedings of the November 7-10, 1966, Fall Joint Computer Conference, 695–709. https://dl.acm.org/doi/abs/10.1145/1464291.1464366
Villa, A., & Taurino, T. (2019). SME Innovation and Development in the Context of Industry 4.0. Procedia Manufacturing, 39, 1415–1420. https://doi.org/10.1016/j.promfg.2020.01.311
Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J., Dubey, R., & Childe, S. J. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356–365.
Weber, C., Königsberger, J., Kassner, L., & Mitschang, B. (2017). M2DDM – A Maturity Model for Data-Driven Manufacturing. Procedia CIRP, 63, 173–178. https://doi.org/10.1016/j.procir.2017.03.309
Xiao, X., Tian, Q., & Mao, H. (2020). How the Interaction of Big Data Analytics Capabilities and Digital Platform Capabilities Affects Service Innovation: A Dynamic Capabilities View. IEEE Access, 8, 18778–18796.
Yasmin, M., Tatoglu, E., Kilic, H. S., Zaim, S., & Delen, D. (2020). Big data analytics capabilities and firm performance: An integrated MCDM approach. Journal of Business Research, 114, 1–15. https://doi.org/10.1016/j.jbusres.2020.03.028
Ye, F. Y. (2017). Measuring knowledge: A quantitative approach to knowledge theory. In Understanding Complex Systems (Issue 9789811059353, pp. 155–162). Springer Verlag. https://doi.org/10.1007/978-981-10-5936-0_13
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dc.publisher.faculty.spa.fl_str_mv Facultad de Ciencias Económicas
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spelling Atribución-CompartirIgual 4.0 Internacionalhttp://creativecommons.org/licenses/by-sa/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Sánchez-Torres, Jenny Marcela8513ae3ae64cd02d1aa4596da6720b1a600Angulo Romero, Carlos Aureliob2cecf931d0fec52ef6c47982b3267beGRIEGO (Grupo Investigación en Gestión y Organizaciones)2021-09-09T14:51:28Z2021-09-09T14:51:28Z2021https://repositorio.unal.edu.co/handle/unal/80142Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/Ilustraciones y tablasThe new Information and Communication Technologies have led to significant changes in the commercial and organizational dynamics of companies in the world, especially in terms of how data are analyzed for decision making. Due to the above, this master's thesis addresses the evaluation of the data analysis capacity of SMEs that operate in the software development sector in the city of Bogota, seeking to characterize the diagnostic models used in the environment, to determine the levels of maturity of the parameter observed in the entities studied, according to the dimensions of Organization, Infrastructure, Resource Management, Analysis and Governance. Thus, recommendations are made to strengthen this analytical capacity. To this end, a descriptive methodology was used, with a mixed approach, combining quantitative and qualitative techniques, with a non-experimental research design. Among the results obtained, it was possible to identify the level of maturity of the data analysis capacity of the companies that participated in the research, with reference points such as discrimination by income received by them. A detailed study of the findings was also carried out in relation to the organizational dimensions considered. Finally, actions are suggested that could be useful for closing the development gaps identified.Las nuevas Tecnología de la Información y Comunicación han motivado cambios significativos en la dinámica comercial y organizacional de las empresas en el mundo, sobre todo en lo referente a la forma como se analizan los datos para la toma de decisiones. Debido a lo anterior, el presente trabajo de maestría aborda la evaluación de la capacidad de análisis de datos de las pymes que se desenvuelven en el sector de desarrollo de software de la ciudad de Bogotá, buscando caracterizar los modelos de diagnóstico empleados en el entorno, para determinar los niveles de madurez del parámetro observado en las entidades estudiadas, en función de las dimensiones de Organización, Infraestructura, Gestión de recursos, Análisis y Gobernanzas. Así las cosas, se plantean recomendaciones para fortalecer dicha capacidad de análisis. Para tal fin, se empleó una metodología de tipo descriptivo, de enfoque mixto, que combina técnicas cuantitativas y cualitativas, con un diseño de investigación no experimental. Dentro de los resultados obtenidos fue posible identificar el nivel de madurez de la capacidad de análisis de datos de las empresas que participaron en la investigación, con puntos de referencia como la discriminación por ingresos recibidos por las mismas. De igual manera se realizó un estudio minucioso de los hallazgos en relación con las dimensiones organizacionales consideradas. Finalmente se sugieren acciones que podrían ser de utilidad para el cierre de las brechas de desarrollo identificadas. (Texto tomado de la fuente).Incluye anexosMaestríaMagíster en AdministraciónDescriptiva, de enfoque mixto, que combina técnicas cuantitativas y cualitativas, con un diseño de investigación no experimental.Estrategia y Organizacionesxiv, 129 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ciencias Económicas - Maestría en AdministraciónEscuela de Administración y Contaduría PúblicaFacultad de Ciencias EconómicasBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá000 - Ciencias de la computación, información y obras generalesComputer software developmentDesarrollo de programas para computadorCapacidad de análisisPymesAnalytics Maturity (TDWI)Analysis capacityData analysisAnálisis de datosData processingProcesamiento de datosEvaluación de la capacidad de análisis de datos de las pymes desarrolladoras de software de la ciudad de BogotáEvaluation of the data analysis capacity of software development SMEs in the city of BogotaTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMBogotáAlderson, P., Green, S., & Higgins, J. (2004). Cochrane reviewers’ handbook, version 4.2.2. (The Cochrane Collaboration (ed.)). https://www.mv.helsinki.fi/home/hemila/karlowski/handbook_4_2_2_Karlowski.pdfBean, R., & Davenport, T. (2019). Companies are failing in their efforts to become data-driven. Harvard Business Review.Bedeley, R. T., & Nemati, H. (2014). Big Data Analytics: A key capability for competitive advantage. 20th Americas Conference on Information Systems, AMCIS 2014. https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1536&context=amcis2014Bihani, P., & Patil, S. (2014). A comparative study of data analysis techniques. International Journal of Emerging Trends & Technology in Computer Science, 3(2), 95–101. www.ijettcs.orgBonilla, J., & Rao, B. (2015). Decoding data analytics capabilities from topic modeling on press releases. Portland International Conference on Management of Engineering and Technology, 2015-Septe, 1959–1968. https://doi.org/10.1109/PICMET.2015.7273249Bryman, A., & Cramer, D. (1992). Quantitative data analysis for social scientists. Estudios Geográficos, 53(207), 347. http://search.proquest.com/openview/71ba8781f88a269938b860d448e6e8d2/1?pq-origsite=gscholar&cbl=1818083Cates, J. E., Gill, S. S., & Zeituny, N. (2005). The Ladder of Business Intelligence (LOBI): a framework for enterprise IT planning and architecture. International Journal of Business Information Systems, 1(1/2), 220. https://doi.org/10.1504/IJBIS.2005.007408Coleman, S. (2016). Data-mining opportunities for small and medium enterprises with official statistics in the UK. Journal of Official Statistics. https://doi.org/10.1515/JOS-2016-0044Creswell, J. W., & Creswell, J. D. (2017). Research design: Qualitative, quantitative, and mixed methods approaches. Sage publications.Davenport, T., & Harris, J. G. (2017). Competing on Analytics: The New Science of Winning. Harvard Business School Press.Denning, D. E., Nicholson, W., Sande, G., & Shoshani, A. (1984). Research topics in statistical database management. IEEE Database Eng. Bull., 7(1), 4–9.Dinter, B. (2012). The maturing of a business intelligence maturity model. AMCIS 2012 Proceedings. http://elibrary.aisnet.org/Default.aspx?url=https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1083&context=amcis2012Du, X., Liu, B., & Zhang, J. (2019). Application of Business Intelligence Based on Big Data in E-commerce Data Analysis. Journal of Physics: Conference Series, 1395(1), 012011. https://doi.org/10.1088/1742-6596/1395/1/012011Dulcé, H. J. (2016). Datos, información y conocimiento. Respuestas, 21(1), 4. https://revistas.ufps.edu.co/index.php/respuestas/article/download/642/646Eason, K. D. (1989). Information technology and organisational change. CRC Press.Eckerson, W. (2007). TDWI benchmark guide: interpreting benchmark scores using TDWI’s maturity model. TDWI Research, 3–14.Garmaki, M., & Boughzala, I. (2016). Conceptualization of Big data analytics Capability based on IT capability: Primary findings. 21st Symposium of the Association Information and Management 2016, AIM 2016.Grossman, R. L. (2018). A framework for evaluating the analytic maturity of an organization. International Journal of Information Management, 38(1), 45–51. https://doi.org/10.1016/j.ijinfomgt.2017.08.005Gupta, M., & George, J. F. (2016). Toward the development of a big data analytics capability. Information & Management, 53(8), 1049–1064.Halper, F. (2020). TDWI Analytics Maturity Model Guide: Assessment Guide.Halper, F., & Stodder, D. (2014). TDWI Analytics Maturity Model Guide. In TDWI Research. https://www3.microstrategy.com/getmedia/9b914607-084f-4869-ae64-e0b3f9e003de/TDWI_Analytics-Maturity-Guide_2014-2015.pdfHao, S., Zhang, H., & Song, M. (2019). Big data, big data analytics capability, and sustainable innovation performance. Sustainability, 11(24), 7145.Hatta, N. N. M., Miskon, S., Ali, N. M., Abdullah, N. S., Ahmad, N., Hashim, H., Alias, R. A., & Maarof, M. A. (2015). Business intelligence system adoption theories in SMES: A literature review. ARPN Journal of Engineering and Applied Sciences.Hicks, S. C., & Peng, R. D. (2019). Elements and Principles for Characterizing Variation between Data Analyses. ArXiv Preprint ArXiv:1903.07639. http://arxiv.org/abs/1903.07639Hirvonen, J., & Majuri, M. (2020). Digital capabilities in manufacturing SMEs. Procedia Manufacturing, 51, 1283–1289. https://doi.org/10.1016/j.promfg.2020.10.179Humphrey, W. S. (1989). Managing the software process. Addison-Wesley Longman Publishing Co., Inc.Irwin, S. (2008). Data analysis and interpretation: emergent issues in linking qualitative and quantitative evidence. In Guilford Publications (Ed.), Handbook of emergent methods in social research (pp. 415–435).Islam, M. (2020). Data Analysis: Types, Process, Methods, Techniques and Tools. 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Making enterprise information management (EIM) work for business: A guide to understanding information as an asset. Morgan Kaufmann.Letón, M., & Pedromingo, A. (2001). Introducción al Análisis de Datos en Meta-Análisis. Ediciones Díaz de Santos.Luhn, H. P. (1958). A Business Intelligence System. IBM Journal of Research and Development, 2(4), 314–319. https://doi.org/10.1147/rd.24.0314Martínez, C. (2014). Técnicas e Instrumentos de Recogida y Análisis de Datos. Editorial UNED.Marulanda, C. E., López, M., & Mejía, M. H. (2013). Minería de datos en gestión del conocimiento de pymes de Colombia. Revista Virtual Universidad Católica Del Norte, 1(38), 158-170–170. https://revistavirtual.ucn.edu.co/index.php/RevistaUCN/article/view/821/1339Masood, T., & Sonntag, P. (2020). Industry 4.0: Adoption challenges and benefits for SMEs. Computers in Industry, 121, 103261. https://doi.org/10.1016/j.compind.2020.103261Maxwell, J. A., & Chmiel, M. (2014). Notes toward a theory of qualitative data analysis. In The SAGE handbook of qualitative data analysis (pp. 21–34). Sage Thousand Oaks, CA.Meyer, S. L. (1975). Data Analysis For Scientists And Engineers (Wiley (ed.)). https://doi.org/10.1007/978-3-319-03762-2__1Mikalef, P., Boura, M., Lekakos, G., & Krogstie, J. (2018). Complementarities between information governance and big data analytics capabilities on innovation. 26th European Conference on Information Systems: Beyond Digitization - Facets of Socio-Technical Change, ECIS 2018.Mikalef, P., Krogstie, J., Pappas, I. O., & Pavlou, P. (2020). Exploring the relationship between big data analytics capability and competitive performance: The mediating roles of dynamic and operational capabilities. Information and Management, 57(2). https://doi.org/10.1016/j.im.2019.05.004Nagappan, M., Sam, S., Sangeetha, S., Nithya Priya, S., Suguna, N., & Scholar, U. G. (2019). Heart Disease Prediction Using Data Mining Technique. In Shodhshauryam, International Scientific Refereed Research Journal © 2019 SHISRRJ (Vol. 2, Issue 10). www.shisrrj.comOlszak, C. M. (2013). Assessment of business intelligence maturity in the selected organizations. 2013 Federated Conference on Computer Science and Information Systems, 951–958.Partners New Vantage, L. (2019). Big data and AI executive survey 2019. Data and Innovation. How Big Data and AI are Accelerating Business Transformation. http://newvantage.com/wp-content/uploads/2018/12/Big-Data-Executive-Survey-2019-Findings-Updated-010219-1.pdfPrieto, R., Meneses, C., & Vega, V. (2015). Análisis comparativo de modelos de madurez en inteligencia de negocio. Ingeniare. Revista Chilena de Ingeniería, 23(3), 361–371. https://doi.org/10.4067/S0718-33052015000300005Ramamurthy, A. (2017). Effective information management - A - big- data driven road map for enterprise decision making. Water Environment Federation Technical Exhibition and Conference 2017, WEFTEC 2017, 3, 1854–1865.Ramesh, G. S., Rajini Kanth, T. V., & Vasumathi, D. (2020). A Comparative Study of Data Mining Tools and Techniques for Business Intelligence (Springer (ed.); pp. 163–173). https://doi.org/10.1007/978-981-13-8253-6_15Ramsay, J. O. (2006). Functional Data Analysis. In Encyclopedia of Statistical Sciences. John Wiley & Sons, Inc. https://doi.org/10.1002/0471667196.ess3138Reio, T. G. (2016). Nonexperimental research: strengths, weaknesses and issues of precision. European Journal of Training and Development, 40(8/9), 676–690. https://doi.org/10.1108/EJTD-07-2015-0058Rutkowski, L., Jaworski, M., & Duda, P. (2020). Basic Concepts of Data Stream Mining (pp. 13–33). https://doi.org/10.1007/978-3-030-13962-9_2Sampieri, H. (2018). Metodología de la investigación: las rutas cuantitativa, cualitativa y mixta (M. Hill (ed.)).Schab, E., Rivera, R., Bracco, L., Coto, F., Cristaldo, P., Ramos, L., Rapesta, N., Pablo Núñez, J., Retamar, S., Casanova, C., De Battista, A., & Herrera, N. E. (2018). Minería de Datos y Visualización de Información. XX Workshop de Investigadores En Ciencias de La Computaci´on. http://hdl.handle.net/20.500.12272/3567Sen, D., Ozturk, M. &, & Vayvay, O. (2016). An Overview of Big Data for Growth in SMEs. Procedia - Social and Behavioral Sciences, 235(2016), 159–167. https://doi.org/10.1016/j.sbspro.2016.11.011Shaaban, E., Helmy, Y., Khedr, A., Nasr, M., & others. (2011). Business intelligence maturity models: Toward new integrated model. The International Arab Conference on Information Technology (ACIT 2011), Organized by Naif Arab University for Security Science (NAUSS), Riyadh, Saudi Arabia, 11–14.Shuradze, G., & Wagner, H. T. (2016). Governing for agility and innovation in data-rich environments: The role of data analytics capabilities. 24th European Conference on Information Systems, ECIS 2016.Song, M., Zhang, H., & Heng, J. (2020). Creating sustainable innovativeness through big data and big data analytics capability: From the perspective of the information processing theory. Sustainability (Switzerland), 12(5). https://doi.org/10.3390/su12051984Tan, P.-N., Steinbach, M., & Kumar, V. (2006). Introduction to data mining. Pearson Education India.Tukey, J., & Wilk, M. (1966). Data analysis and statistics: an expository overview. Proceedings of the November 7-10, 1966, Fall Joint Computer Conference, 695–709. https://dl.acm.org/doi/abs/10.1145/1464291.1464366Villa, A., & Taurino, T. (2019). SME Innovation and Development in the Context of Industry 4.0. Procedia Manufacturing, 39, 1415–1420. https://doi.org/10.1016/j.promfg.2020.01.311Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J., Dubey, R., & Childe, S. J. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356–365.Weber, C., Königsberger, J., Kassner, L., & Mitschang, B. (2017). M2DDM – A Maturity Model for Data-Driven Manufacturing. Procedia CIRP, 63, 173–178. https://doi.org/10.1016/j.procir.2017.03.309Xiao, X., Tian, Q., & Mao, H. (2020). How the Interaction of Big Data Analytics Capabilities and Digital Platform Capabilities Affects Service Innovation: A Dynamic Capabilities View. IEEE Access, 8, 18778–18796.Yasmin, M., Tatoglu, E., Kilic, H. S., Zaim, S., & Delen, D. (2020). Big data analytics capabilities and firm performance: An integrated MCDM approach. Journal of Business Research, 114, 1–15. https://doi.org/10.1016/j.jbusres.2020.03.028Ye, F. Y. (2017). Measuring knowledge: A quantitative approach to knowledge theory. In Understanding Complex Systems (Issue 9789811059353, pp. 155–162). Springer Verlag. https://doi.org/10.1007/978-981-10-5936-0_13Alderson, P., Green, S., & Higgins, J. (2004). Cochrane reviewers’ handbook, version 4.2.2. (The Cochrane Collaboration (ed.)). https://www.mv.helsinki.fi/home/hemila/karlowski/handbook_4_2_2_Karlowski.pdfBean, R., & Davenport, T. (2019). Companies are failing in their efforts to become data-driven. Harvard Business Review.Bedeley, R. T., & Nemati, H. (2014). Big Data Analytics: A key capability for competitive advantage. 20th Americas Conference on Information Systems, AMCIS 2014. https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1536&context=amcis2014Bihani, P., & Patil, S. (2014). A comparative study of data analysis techniques. International Journal of Emerging Trends & Technology in Computer Science, 3(2), 95–101. www.ijettcs.orgBonilla, J., & Rao, B. (2015). Decoding data analytics capabilities from topic modeling on press releases. 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