Modelo de business intelligence y analytics soportado por la tecnologia cloud computing para pymes del sector retail
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AsesoresChumpitaz Avendaño, Max Raúl
Fecha de publicación2017-12-01
Palabras claveBusiness Intelligence
Tablero de mando
Tecnología de información
Ingeniería de Sistemas de Información
MetadatosMostrar el registro completo del ítem
CitationLopez Inga, Milton Elvis; Guerrero Huaranga, R. M. (2017). Modelo de business intelligence y analytics soportado por la tecnologia cloud computing para pymes del sector retail. Universidad Peruana de Ciencias Aplicadas (UPC). Retrieved from http://hdl.handle.net/10757/622650
ResumenThe main challenge for Peruvian SMEs is obtaining information for decision making. In this context, the use of data analysis technologies such as Business Intelligence and Analytics are not very accessible to companies, due to economic and human capital limitations. The main objective of the project is the implementation of a technological model that combines Business Intelligence and Analytics with Cloud Computing, to allow retail SMEs to integrate and process their data to make informed and timely decisions regarding inventory planning and management, with a low cost of implementation and deployment. For the development of the project, a preliminary investigation is carried out about Business Intelligence, Analytics and Cloud Computing technologies, where the applications of each one is studied and success cases of retail SMEs worldwide. Then, the technological model oriented to the necesities of Peruvian retail SMEs is designed, accompanied by a plan to implement the model based on Business Intelligence and Cloud Computing methodologies and an analysis of cloud service providers that best suit to this type of Business. The model is validated through its implementation in a Peruvian retail SME. To accomplish it, a business and technological infrastructure analysis is carried out and the key information requirements are identified. All this data allows to integrate the isolated information of multiple stores, reducing the data consolidation delay time by 94% and reducing costs by 20%. Finally, we propose a continuity plan that allows scaling the functionalities of the model, oriented to the technological trends of the retail sector.
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