MMGD - Memoria de Proyecto ...
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Fecha de publicación2017-04-01
Palabras claveAdministración de proyectos informáticos
Sistemas de información administrativa
Ingeniero de Sistemas de Información
MetadatosMostrar el registro completo del ítem
Citation N. S. Rivera Elescano, Stephanie Elsa; Loarte Laos, “Modelo de madurez de gobierno de datos para microfinancieras,” Universidad Peruana de Ciencias Aplicadas (UPC), 2002.
ResumenMicrofinance organizations play an important role as they allow the integration of all social sectors into the country's sustained economic growth. Given this panorama, it results in the exponential growth of data, the product of transactions and operations on a daily basis in companies and the necessary results a good management of this data, or otherwise, translate into economic losses in addition to be a Competitive disadvantage without valuable information and quality for decision making, process improvement, among others. For this reason, the concept of Data Governance gives a distinctive approach to data management, and is a vision of a management company. In this sense, it is necessary that the organization has the capacity to evaluate to what extent that management is the correct one or is generating expected results and thus to be able to take actions of improvement and definition Strategies that allow to reach the appropriate levels. Given the context, we propose a maturity model of governance for microfinance, which frames a series of criteria and requirements that require a medical diagnosis on the management of data in microfinance organizations. This model is implemented based on the information of a Peruvian microfinance with a range of four domains of the seven contemplated in the model to obtain reports that reflect the current state of the organization with respect to the level of governance of the data. It establishes a base that allows the decision making to improve the critical areas. Finally, after the validation of the proposed model, it was shown that it is a means of identifying the gap between the data management and the objectives that are to be achieved in the organization. It is based on a set of good practices in data management, It is an agile, flexible and easy-to-apply model, as it not only supports a holistic evaluation but also adjusts to the needs of the organization, allowing the evaluation of specific components that the organization considers critical.
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