Application of data mining as a strategic tool in knowledge management

Abstract

Introduction: The study analyzed the importance of data mining as a fundamental tool for transforming large volumes of information into knowledge applicable to business and academic decision making. This discipline allowed the discovery of hidden patterns and significant relationships between variables, facilitating the prediction of behaviors and the optimization of strategic processes.
Development: During the research, several studies were reviewed that evidenced the effectiveness of data mining techniques in different contexts. Sposito (2008) and Pautsch (2008) applied decision trees and classification methods in Argentinean institutions, managing to identify determining factors in student dropout. Domínguez González (2008) integrated fuzzy logic and data mining to predict school dropout in Mexico, demonstrating the usefulness of the hybrid approach. Likewise, Altamiranda et al. (2013) highlighted its application in the business environment, where data mining made it possible to segment customers, personalize strategies and improve organizational competitiveness. In the particular case of Telefónica del Perú, tools such as Weka, SQL Server and SPSS were used, confirming that technological integration strengthens management and analysis processes.
Conclusions: The research concluded that data mining significantly improved decision making in the customer management area of Telefónica del Perú, Tarapoto zone. It was proven that its implementation allowed automating the analysis, reducing errors and generating accurate information for strategic decisions. Consequently, the hypothesis that data mining tools have a positive impact on organizational efficiency and foster a culture based on knowledge and innovation was validated.