-- The advent of Internet, the phone mobile and the globalization have modified the forms of communication in the world and the enterprises in general. All this has brought the search of facilities of communications permanents, mobilities and secures with quality of service to improve the Internet and the new aplications on devices mobile. The studies of the new protocol of internet as Ipv6 are in a proces that consist in finding new focus to improve the quality of service. This thesis consists in analyzing and give recomendations of Ipv6 with Business Intelligence to demonstrate that the use improve the management in the enterprises. Ipv6 is the best solution to improve the throughput in the network. The other side, it proposes a new way of business on internet y the born the new aplications as for example the m-commerce and Business Intelligence since any devices mobile.

Banks use telemarketing to contact potential customers for their products directly. This sales channel is complex, requiring large databases of possible prospects, and is subject to time and personnel restrictions. This article has three objectives: to compare five prediction models based on machine learning algorithms to find the one that offers the best predictive accuracy, deploy a pilot of this model, and recommend a roadmap for the future architecture that supports it. The comparison results show that the selected algorithm considerably improves the identification of customers who accept the product, which went from 11 % to 94 %, so its implementation can contribute to the competitiveness of these organizations.

Banks use telemarketing to contact potential customers for their products directly. This sales channel is complex, requiring large databases of possible prospects, and is subject to time and personnel restrictions. This article has three objectives: to compare five prediction models based on machine learning algorithms to find the one that offers the best predictive accuracy, deploy a pilot of this model, and recommend a roadmap for the future architecture that supports it. The comparison results show that the selected algorithm considerably improves the identification of customers who accept the product, which went from 11 % to 94 %, so its implementation can contribute to the competitiveness of these organizations.