Abstract
The exponential increase in information due to technological advances and the development of communications has created the need to make decisions based on the data analysis. This trend has opened the doors to new approaches to data understanding and decision-making. On the one hand, companies need to follow data analytic methodologies to manage large volumes of information with big data tools. On the other hand, there are non-profit small and medium-sized enterprises (SMEs) that make efforts to address data analytics according to their different sources and types. They find challenges such as lack of knowledge in methodological and software tools, which allow timely deployment for decision-making. In this paper, we propose a data analytics methodology for non-profit SMEs. The design of this methodology is based on CRISP-DM as a reference framework, is represented by Software Process Engineering Metamodel (SPEM) and is characterized by being simple, flexible, and low implementation costs.
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References
A. Palmer, B. Hartley, The Business and Marketing Environment, 3rd edn. (McGraw-Hill, London, 2000)
E. Soto, La información como recurso estratégico generador de conocimientos, Soportes audiovisuales e Informaticos (Universidad de la Laguna, Spain, 2004), pp. 58–60
P. Pytel, A. Hossian, P. Britos, R. Garcia-Martinez, Feasibility and effort estimation models for medium and small size information mining projects. Inf. Syst. 47, 1–14 (2015). https://doi.org/10.1016/j.is.2014.06.004. (Elsevier, Argentina)
R. Mullins, Y. Duan, D. Hamblin, P. Burrell, H. Jin, Z. Ewa, B. Aleksander, A web based intelligent training system for SMEs. Electron. J. e-Learn. 5(1), 39–48 (2007). (ERIC, Germany, Poland, Portugal, Slovakia, United Kingdom)
G. Lawton, Users take a close look at visual analytics. Computer 42(2), 19–22 (2009). https://doi.org/10.1109/mc.2009.61. (California)
H. Florez, Inteligencia de negocios como apoyo a la toma de decisiones en la gerencia. Rev. Vincul. 9(2), 11–23 (2012). (Bogota)
T. Guarda, M. Santos, F. Pinto, M. Augusto, C. Silva, Business intelligence as a competitive advantage for SMEs. Int. J. Trade Econ. Financ. 4(4), 187–190 (2013). https://doi.org/10.7763/ijtef.2013.v4.283. (Portugal)
W. Franco, D. Samiento, G. Serrano, G. Suarez, Entidades sin animo de lucro (Consejo Tecnico de la Contaduria Publica, 2015). http://www.ctcp.gov.co
Conferencia Colombiana de ONG, Quiénes conforman el sector de las Entidades Sin Ánimo de Lucro ESAL en Colombia (2016). http://ccong.org.co/
B. Ogbuokiri, C. Udanor, M. Agu, Implementing bigdata analytics for small and medium enterprise (SME) regional growth. IOSR J. Comput. Eng. Ver. IV 17(6), 2278–2661 (2015). https://doi.org/10.9790/0661-17643543. (Nigeria)
Sinnetic, PYMES se desaceleran en transformación digital e innovación por responder a múltiples requerimientos estatales, vol. 18 (Sinnetic, Colombia, 2017), pp. 1–3
A. Gonzalez, Big data y analítica en Colombia: A un paso de despegar (2014). https://searchdatacenter.techtarget.com
KDnuggets, What Main Methodology are you Using for Your Analytics, Data Mining, or Data Science Projects? (KDnuggets, 2014) https://www.kdnuggets.com/polls/2014/analytics-data-mining-data-science-methodology.html
H. Achmad, V. Sabur, A. Pritasari, H. Reinaldo, Data mining and sharing to create usable knowledge, implementation in small business in Indonesia. Sains Humanika 2, 69–75 (2016). (Indonesia)
M. Dittert, R. Härting, C. Reichstein, C. Bayer, A Data Analytics Framework for Business in Small and Medium-Sized Organizations, vol. 73 (Springer, Germany, 2018), pp. 1–13. https://doi.org/10.1007/978-3-319-59424-8
V. Menéndez, M. Castellanos, Software process engineering metamodel (SPEM). Rev. Latinoam. Ing. Softw. 3(2), 92–100 (2008). https://doi.org/10.18294/relais.2015.92-100
DataCamp, The Data Science Industry: Who Does What (2018). https://www.datacamp.com/community/tutorials/data-science-industry-infographic
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Montalvo-Garcia, J., Quintero, J.B., Manrique-Losada, B. (2020). CRISP-DM/SMEs: A Data Analytics Methodology for Non-profit SMEs. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Fourth International Congress on Information and Communication Technology. Advances in Intelligent Systems and Computing, vol 1041. Springer, Singapore. https://doi.org/10.1007/978-981-15-0637-6_38
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DOI: https://doi.org/10.1007/978-981-15-0637-6_38
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