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Crisp-dm/smes: A data analytics methodology for non-profit smes

dc.creatorMontalvo-Garcia J.
dc.creatorQuintero J.B.
dc.creatorManrique-Losada B.
dc.date2020
dc.date.accessioned2020-04-29T14:53:56Z
dc.date.available2020-04-29T14:53:56Z
dc.identifier.isbn9789811506369
dc.identifier.issn21945357
dc.identifier.urihttp://hdl.handle.net/11407/5769
dc.descriptionThe 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. © Springer Nature Singapore Pte Ltd. 2020.
dc.language.isoeng
dc.publisherSpringer
dc.relation.isversionofhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85077121191&doi=10.1007%2f978-981-15-0637-6_38&partnerID=40&md5=a89f08fd32d01417af48f5de19fd53b7
dc.sourceAdvances in Intelligent Systems and Computing
dc.subjectCRISP-DM
dc.subjectData analytics
dc.subjectNon-profit SMEs
dc.subjectCost engineering
dc.subjectDecision making
dc.subjectProfitability
dc.subjectSoftware design
dc.subjectCRISP-DM
dc.subjectExponential increase
dc.subjectImplementation cost
dc.subjectNon-profit
dc.subjectReference frameworks
dc.subjectSmall and medium-sized enterprise
dc.subjectSoftware process engineering metamodel
dc.subjectTechnological advances
dc.subjectData Analytics
dc.titleCrisp-dm/smes: A data analytics methodology for non-profit smes
dc.typeConference Papereng
dc.rights.accessrightsinfo:eu-repo/semantics/restrictedAccess
dc.publisher.programIngeniería de Sistemas
dc.identifier.doi10.1007/978-981-15-0637-6_38
dc.relation.citationvolume1041
dc.relation.citationstartpage449
dc.relation.citationendpage457
dc.publisher.facultyFacultad de Ingenierías
dc.affiliationMontalvo-Garcia, J., Seventh-day Adventist Church of Colombia, Cra 84 #33aa-169, Medellin, Colombia; Quintero, J.B., Universidad de Medellin, Cra 87 #30-65, Medellin, Colombia; Manrique-Losada, B., Universidad de Medellin, Cra 87 #30-65, Medellin, Colombia
dc.relation.referencesPalmer, A., Hartley, B., (2000) The Business and Marketing Environment, , 3rd edn. (McGraw-Hill, London
dc.relation.referencesSoto, E., La información como recurso estratégico generador de conocimientos (2004) Soportes Audio-Visuales E Informaticos, pp. 58-60. , Universidad de la Laguna, Spain,), pp
dc.relation.referencesPytel, P., Hossian, A., Britos, P., Garcia-Martinez, R., Feasibility and effort estimation models for medium and small size information mining projects (2015) Inf. Syst., 47, pp. 1-14. , https://doi.org/10.1016/j.is.2014.06.004
dc.relation.referencesMullins, R., Duan, Y., Hamblin, D., Burrell, P., Jin, H., Ewa, Z., Aleksander, B., A web based intelligent training system for SMEs (2007) Electron. J. E-Learn., 5 (1), pp. 39-48. , (ERIC, Germany, Poland, Portugal, Slovakia, United Kingdom)
dc.relation.referencesLawton, G., Users take a close look at visual analytics (2009) Computer, 42 (2), pp. 19-22. , https://doi.org/10.1109/mc.2009.61, (California)
dc.relation.referencesFlorez, H., Inteligencia de negocios como apoyo a la toma de decisiones en la gerencia (2012) Rev. Vincul., 9 (2), pp. 11-23
dc.relation.referencesGuarda, T., Santos, M., Pinto, F., Augusto, M., Silva, C., Business intelligence as a competitive advantage for SMEs (2013) Int. J. Trade Econ. Financ., 4 (4), pp. 187-190. , https://doi.org/10.7763/ijtef.2013.v4.283, Portugal
dc.relation.referencesFranco, W., Samiento, D., Serrano, G., Suarez, G., (2015) Entidades Sin Animo De Lucro, , http://www.ctcp.gov.co, Consejo Tecnico de la Contaduria Publica
dc.relation.references(2016) Conferencia Colombiana De ONG, Quiénes Conforman El Sector De Las Entidades Sin Ánimo De Lucro ESAL En Colombia, , http://ccong.org.co/
dc.relation.referencesOgbuokiri, B., Udanor, C., Agu, M., Implementing bigdata analytics for small and medium enterprise (SME) regional growth (2015) IOSR J. Comput. Eng. Ver. IV, 17 (6), pp. 2278-2661. , https://doi.org/10.9790/0661-17643543, (Nigeria)
dc.relation.references(2017) Sinnetic, PYMES Se Desaceleran En transformación Digital E innovación Por Responder a múlti-ples Requerimientos Estatales, 18, pp. 1-3. , (Sinnetic, Colombia,), pp
dc.relation.referencesGonzalez, A., (2014) Big Data Y analítica En Colombia: A Un Paso De Despegar, , https://searchdatacenter.techtarget.com
dc.relation.references(2014) Kdnuggets, What Main Methodology are You Using for Your Analytics, Data Mining, Or Data Science Projects? (Kdnuggets, , https://www.kdnuggets.com/polls/2014/analytics-data-mining-data-science-methodology.html
dc.relation.referencesAchmad, H., Sabur, V., Pritasari, A., Reinaldo, H., Data mining and sharing to create usable knowledge, implementation in small business in Indonesia (2016) Sains Humanika, 2, pp. 69-75. , Indonesia
dc.relation.referencesDittert, M., Härting, R., Reichstein, C., Bayer, C., (2018) A Data Analytics Framework for Business in Small and Medium-Sized Organizations, 73, pp. 1-13. , https://doi.org/10.1007/978-3-319-59424-8, Springer, Germany,), pp
dc.relation.referencesMenéndez, V., Castellanos, M., Software process engineering metamodel (SPEM) (2008) Rev. Lati-Noam. Ing. Softw., 3 (2), pp. 92-100. , https://doi.org/10.18294/relais.2015.92-100
dc.relation.references(2018) The Data Science Industry: Who Does What, , https://www.datacamp.com/community/tutorials/data-science-industry-infographic
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dc.type.driverinfo:eu-repo/semantics/article


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