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...
- 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
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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 |
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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 |