| Título: | Master data management maturity model for the microfinance sector in Peru |
| Autores: | Vásquez Zúñiga, Daniel ; Kukurelo Cruz, Romina ; Raymundo Ibañez, Carlos ; Dominguez, Francisco ; Moguerza, Javier |
| Tipo de documento: | documento electrónico |
| Editorial: | Association for Computing Machinery, 2018-11-29T16:20:21Z |
| Nota general: | info:eu-repo/semantics/embargoedAccess |
| Idiomas: | Inglés |
| Palabras clave: | Facultad de Ingeniería , Pregrado , Artículos científicos , Ingeniería de Sistemas y Computación |
| Resumen: |
El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. The microfinance sector has a strategic role since they facilitate integration and development of all social classes to sustained economic growth. In this way the actual point is the exponential growth of data, resulting from transactions and operations carried out with these companies on a daily basis, becomes imminent. Appropriate management of this data is therefore necessary because, otherwise, it will result in a competitive disadvantage due to the lack of valuable and quality information for decision-making and process improvement. The Master Data Management (MDM) give a new way in the Data management, reducing the gap between the business perspectives versus the technology perspective In this regard, it is important that the organization have the ability to implement a data management model for Master Data Management. This paper proposes a Master Data management maturity model for microfinance sector, which frames a series of formal requirements and criteria providing an objective diagnosis with the aim of improving processes until entities reach desired maturity levels. This model was implemented based on the information of Peruvian microfinance organizations. Finally, after validation of the proposed model, it was evidenced that it serves as a means for identifying the maturity level to help in the successful of initiative for Master Data management projects. Revisión por pares |
| En línea: | http://hdl.handle.net/10757/624687 |
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