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

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...

Full description

Autores:
Tipo de recurso:
Fecha de publicación:
2020
Institución:
Universidad de Medellín
Repositorio:
Repositorio UDEM
Idioma:
eng
OAI Identifier:
oai:repository.udem.edu.co:11407/5769
Acceso en línea:
http://hdl.handle.net/11407/5769
Palabra clave:
CRISP-DM
Data analytics
Non-profit SMEs
Cost engineering
Decision making
Profitability
Software design
CRISP-DM
Exponential increase
Implementation cost
Non-profit
Reference frameworks
Small and medium-sized enterprise
Software process engineering metamodel
Technological advances
Data Analytics
Rights
License
http://purl.org/coar/access_right/c_16ec
id REPOUDEM2_974a20fe73e714e1f883adac1bee1215
oai_identifier_str oai:repository.udem.edu.co:11407/5769
network_acronym_str REPOUDEM2
network_name_str Repositorio UDEM
repository_id_str
dc.title.none.fl_str_mv Crisp-dm/smes: A data analytics methodology for non-profit smes
title Crisp-dm/smes: A data analytics methodology for non-profit smes
spellingShingle Crisp-dm/smes: A data analytics methodology for non-profit smes
CRISP-DM
Data analytics
Non-profit SMEs
Cost engineering
Decision making
Profitability
Software design
CRISP-DM
Exponential increase
Implementation cost
Non-profit
Reference frameworks
Small and medium-sized enterprise
Software process engineering metamodel
Technological advances
Data Analytics
title_short Crisp-dm/smes: A data analytics methodology for non-profit smes
title_full Crisp-dm/smes: A data analytics methodology for non-profit smes
title_fullStr Crisp-dm/smes: A data analytics methodology for non-profit smes
title_full_unstemmed Crisp-dm/smes: A data analytics methodology for non-profit smes
title_sort Crisp-dm/smes: A data analytics methodology for non-profit smes
dc.subject.none.fl_str_mv CRISP-DM
Data analytics
Non-profit SMEs
Cost engineering
Decision making
Profitability
Software design
CRISP-DM
Exponential increase
Implementation cost
Non-profit
Reference frameworks
Small and medium-sized enterprise
Software process engineering metamodel
Technological advances
Data Analytics
topic CRISP-DM
Data analytics
Non-profit SMEs
Cost engineering
Decision making
Profitability
Software design
CRISP-DM
Exponential increase
Implementation cost
Non-profit
Reference frameworks
Small and medium-sized enterprise
Software process engineering metamodel
Technological advances
Data Analytics
description 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. © Springer Nature Singapore Pte Ltd. 2020.
publishDate 2020
dc.date.accessioned.none.fl_str_mv 2020-04-29T14:53:56Z
dc.date.available.none.fl_str_mv 2020-04-29T14:53:56Z
dc.date.none.fl_str_mv 2020
dc.type.eng.fl_str_mv Conference Paper
dc.type.coarversion.fl_str_mv http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.driver.none.fl_str_mv info:eu-repo/semantics/article
dc.identifier.isbn.none.fl_str_mv 9789811506369
dc.identifier.issn.none.fl_str_mv 21945357
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/11407/5769
dc.identifier.doi.none.fl_str_mv 10.1007/978-981-15-0637-6_38
identifier_str_mv 9789811506369
21945357
10.1007/978-981-15-0637-6_38
url http://hdl.handle.net/11407/5769
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.isversionof.none.fl_str_mv https://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.relation.citationvolume.none.fl_str_mv 1041
dc.relation.citationstartpage.none.fl_str_mv 449
dc.relation.citationendpage.none.fl_str_mv 457
dc.relation.references.none.fl_str_mv Palmer, A., Hartley, B., (2000) The Business and Marketing Environment, , 3rd edn. (McGraw-Hill, London
Soto, E., La información como recurso estratégico generador de conocimientos (2004) Soportes Audio-Visuales E Informaticos, pp. 58-60. , Universidad de la Laguna, Spain,), pp
Pytel, 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
Mullins, 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)
Lawton, 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)
Florez, H., Inteligencia de negocios como apoyo a la toma de decisiones en la gerencia (2012) Rev. Vincul., 9 (2), pp. 11-23
Guarda, 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
Franco, W., Samiento, D., Serrano, G., Suarez, G., (2015) Entidades Sin Animo De Lucro, , http://www.ctcp.gov.co, Consejo Tecnico de la Contaduria Publica
(2016) Conferencia Colombiana De ONG, Quiénes Conforman El Sector De Las Entidades Sin Ánimo De Lucro ESAL En Colombia, , http://ccong.org.co/
Ogbuokiri, 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)
(2017) Sinnetic, PYMES Se Desaceleran En transformación Digital E innovación Por Responder a múlti-ples Requerimientos Estatales, 18, pp. 1-3. , (Sinnetic, Colombia,), pp
Gonzalez, A., (2014) Big Data Y analítica En Colombia: A Un Paso De Despegar, , https://searchdatacenter.techtarget.com
(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
Achmad, 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
Dittert, 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
Menéndez, 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
(2018) The Data Science Industry: Who Does What, , https://www.datacamp.com/community/tutorials/data-science-industry-infographic
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_16ec
rights_invalid_str_mv http://purl.org/coar/access_right/c_16ec
dc.publisher.none.fl_str_mv Springer
dc.publisher.program.none.fl_str_mv Ingeniería de Sistemas
dc.publisher.faculty.none.fl_str_mv Facultad de Ingenierías
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv Advances in Intelligent Systems and Computing
institution Universidad de Medellín
repository.name.fl_str_mv Repositorio Institucional Universidad de Medellin
repository.mail.fl_str_mv repositorio@udem.edu.co
_version_ 1814159111851343872
spelling 20202020-04-29T14:53:56Z2020-04-29T14:53:56Z978981150636921945357http://hdl.handle.net/11407/576910.1007/978-981-15-0637-6_38The 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.engSpringerIngeniería de SistemasFacultad de Ingenieríashttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85077121191&doi=10.1007%2f978-981-15-0637-6_38&partnerID=40&md5=a89f08fd32d01417af48f5de19fd53b71041449457Palmer, A., Hartley, B., (2000) The Business and Marketing Environment, , 3rd edn. (McGraw-Hill, LondonSoto, E., La información como recurso estratégico generador de conocimientos (2004) Soportes Audio-Visuales E Informaticos, pp. 58-60. , Universidad de la Laguna, Spain,), ppPytel, 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.2014.06.004Mullins, 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)Lawton, 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)Florez, H., Inteligencia de negocios como apoyo a la toma de decisiones en la gerencia (2012) Rev. Vincul., 9 (2), pp. 11-23Guarda, 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, PortugalFranco, W., Samiento, D., Serrano, G., Suarez, G., (2015) Entidades Sin Animo De Lucro, , http://www.ctcp.gov.co, Consejo Tecnico de la Contaduria Publica(2016) Conferencia Colombiana De ONG, Quiénes Conforman El Sector De Las Entidades Sin Ánimo De Lucro ESAL En Colombia, , http://ccong.org.co/Ogbuokiri, 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)(2017) Sinnetic, PYMES Se Desaceleran En transformación Digital E innovación Por Responder a múlti-ples Requerimientos Estatales, 18, pp. 1-3. , (Sinnetic, Colombia,), ppGonzalez, A., (2014) Big Data Y analítica En Colombia: A Un Paso De Despegar, , https://searchdatacenter.techtarget.com(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.htmlAchmad, 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. , IndonesiaDittert, 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,), ppMenéndez, 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(2018) The Data Science Industry: Who Does What, , https://www.datacamp.com/community/tutorials/data-science-industry-infographicAdvances in Intelligent Systems and ComputingCRISP-DMData analyticsNon-profit SMEsCost engineeringDecision makingProfitabilitySoftware designCRISP-DMExponential increaseImplementation costNon-profitReference frameworksSmall and medium-sized enterpriseSoftware process engineering metamodelTechnological advancesData AnalyticsCrisp-dm/smes: A data analytics methodology for non-profit smesConference Paperinfo:eu-repo/semantics/articlehttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_2df8fbb1Montalvo-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, Colombiahttp://purl.org/coar/access_right/c_16ecMontalvo-Garcia J.Quintero J.B.Manrique-Losada B.11407/5769oai:repository.udem.edu.co:11407/57692020-05-27 15:48:16.766Repositorio Institucional Universidad de Medellinrepositorio@udem.edu.co