Data Mart Design to Increase Transactional Flow of Debit and Credit Card in Peruvian Bodegas

Descripción del Articulo

The objective of this research is to design a Data Mart to identify tactical actions and increase the use of POS (points of sale) in the bodega business sector of Lima, Peru. A quantitative approach, using transaction history data, is applied using the Kimball methodology. This involves the ETL (Ext...

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Detalles Bibliográficos
Autores: Morales-Arevalo, Juan Carlos, Aquise-Gonzales, Erick Manuel, Carpio-Ore, William Yohani, Sáenz, Emmanuel Victor Mendoza, Mazzarri-Rodriguez, Carlos Javier, Remotti-Becerra, Erick Enrique, Plata, Edison Humberto Medina La, Luque-Vega, Luis F.
Formato: artículo
Fecha de Publicación:2025
Institución:Universidad Peruana de Ciencias Aplicadas
Repositorio:UPC-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorioacademico.upc.edu.pe:10757/684683
Enlace del recurso:http://hdl.handle.net/10757/684683
Nivel de acceso:acceso abierto
Materia:Business intelligence
dashboard
data mart
Extract
Load (ETL)
Point of Sale (POS)
Transform
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dc.title.es_PE.fl_str_mv Data Mart Design to Increase Transactional Flow of Debit and Credit Card in Peruvian Bodegas
title Data Mart Design to Increase Transactional Flow of Debit and Credit Card in Peruvian Bodegas
spellingShingle Data Mart Design to Increase Transactional Flow of Debit and Credit Card in Peruvian Bodegas
Morales-Arevalo, Juan Carlos
Business intelligence
dashboard
data mart
Extract
Load (ETL)
Point of Sale (POS)
Transform
title_short Data Mart Design to Increase Transactional Flow of Debit and Credit Card in Peruvian Bodegas
title_full Data Mart Design to Increase Transactional Flow of Debit and Credit Card in Peruvian Bodegas
title_fullStr Data Mart Design to Increase Transactional Flow of Debit and Credit Card in Peruvian Bodegas
title_full_unstemmed Data Mart Design to Increase Transactional Flow of Debit and Credit Card in Peruvian Bodegas
title_sort Data Mart Design to Increase Transactional Flow of Debit and Credit Card in Peruvian Bodegas
author Morales-Arevalo, Juan Carlos
author_facet Morales-Arevalo, Juan Carlos
Aquise-Gonzales, Erick Manuel
Carpio-Ore, William Yohani
Sáenz, Emmanuel Victor Mendoza
Mazzarri-Rodriguez, Carlos Javier
Remotti-Becerra, Erick Enrique
Plata, Edison Humberto Medina La
Luque-Vega, Luis F.
author_role author
author2 Aquise-Gonzales, Erick Manuel
Carpio-Ore, William Yohani
Sáenz, Emmanuel Victor Mendoza
Mazzarri-Rodriguez, Carlos Javier
Remotti-Becerra, Erick Enrique
Plata, Edison Humberto Medina La
Luque-Vega, Luis F.
author2_role author
author
author
author
author
author
author
dc.contributor.author.fl_str_mv Morales-Arevalo, Juan Carlos
Aquise-Gonzales, Erick Manuel
Carpio-Ore, William Yohani
Sáenz, Emmanuel Victor Mendoza
Mazzarri-Rodriguez, Carlos Javier
Remotti-Becerra, Erick Enrique
Plata, Edison Humberto Medina La
Luque-Vega, Luis F.
dc.subject.es_PE.fl_str_mv Business intelligence
dashboard
data mart
Extract
Load (ETL)
Point of Sale (POS)
Transform
topic Business intelligence
dashboard
data mart
Extract
Load (ETL)
Point of Sale (POS)
Transform
description The objective of this research is to design a Data Mart to identify tactical actions and increase the use of POS (points of sale) in the bodega business sector of Lima, Peru. A quantitative approach, using transaction history data, is applied using the Kimball methodology. This involves the ETL (Extract, Transform, Load) process to create a dimensional model and to develop a dashboard to visualize key indicators using Power BI. This solution is expected to improve the detection and analysis of transactional errors, categorized by geographic location and business sector while enhancing decision-making processes. This research improves the transactional flow and digital payment adoption in small businesses, fostering greater financial inclusion in the Peruvian market. Therefore, the methodology and tools to be applied in this research offer a framework as a model for similar contexts, especially in emerging markets, which will allow closing gaps in digital payment adoption and financial inclusion.
publishDate 2025
dc.date.accessioned.none.fl_str_mv 2025-04-30T03:36:04Z
dc.date.available.none.fl_str_mv 2025-04-30T03:36:04Z
dc.date.issued.fl_str_mv 2025-01-01
dc.type.es_PE.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.issn.none.fl_str_mv 2158107X
dc.identifier.doi.none.fl_str_mv 10.14569/IJACSA.2025.0160216
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/10757/684683
dc.identifier.eissn.none.fl_str_mv 21565570
dc.identifier.journal.es_PE.fl_str_mv International Journal of Advanced Computer Science and Applications
dc.identifier.eid.none.fl_str_mv 2-s2.0-86000306402
dc.identifier.scopusid.none.fl_str_mv SCOPUS_ID:86000306402
identifier_str_mv 2158107X
10.14569/IJACSA.2025.0160216
21565570
International Journal of Advanced Computer Science and Applications
2-s2.0-86000306402
SCOPUS_ID:86000306402
url http://hdl.handle.net/10757/684683
dc.language.iso.es_PE.fl_str_mv eng
language eng
dc.rights.es_PE.fl_str_mv info:eu-repo/semantics/openAccess
dc.rights.*.fl_str_mv Attribution 4.0 International
dc.rights.uri.*.fl_str_mv http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
rights_invalid_str_mv Attribution 4.0 International
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dc.format.es_PE.fl_str_mv application/pdf
dc.publisher.es_PE.fl_str_mv Science and Information Organization
dc.source.none.fl_str_mv reponame:UPC-Institucional
instname:Universidad Peruana de Ciencias Aplicadas
instacron:UPC
instname_str Universidad Peruana de Ciencias Aplicadas
instacron_str UPC
institution UPC
reponame_str UPC-Institucional
collection UPC-Institucional
dc.source.journaltitle.none.fl_str_mv International Journal of Advanced Computer Science and Applications
dc.source.volume.none.fl_str_mv 16
dc.source.issue.none.fl_str_mv 2
dc.source.beginpage.none.fl_str_mv 157
dc.source.endpage.none.fl_str_mv 166
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A quantitative approach, using transaction history data, is applied using the Kimball methodology. This involves the ETL (Extract, Transform, Load) process to create a dimensional model and to develop a dashboard to visualize key indicators using Power BI. This solution is expected to improve the detection and analysis of transactional errors, categorized by geographic location and business sector while enhancing decision-making processes. This research improves the transactional flow and digital payment adoption in small businesses, fostering greater financial inclusion in the Peruvian market. 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