Data mining techniques for knowledge discovery in Information Management and service delivery in National Open University Libraries

Abstract

This study examines the data mining techniques for knowledge delivery in information management and service delivery in National  Open University, libraries. The research questions were: what are the data mining techniques used in academic libraries for Information Management, knowledge discovery, and Service Delivery in Academic Libraries? What are the skills and competencies required by  librarians to effectively use data mining techniques for knowledge discovery? & what are the various ways data mining techniques can be  used in academic libraries? The study adopted quantitative research methodology and area consisted of one hundred and sixty-eight  (168) academic librarians from all the study centers in South East of National Open University of Nigeria. Data was collected using a  Goggle Form questionnaire, and one hundred and six (106) responses were collected and analyzed using SPSS. The analysis included  descriptive statistics such as mean scores, standard deviation as well as regression analysis to test hypothesis. The study’s findings  revealed that librarians need to possess technical competency in algorithms and have a wide range of abilities such as domain-specific knowledge in library science, strong analytical skills, and good communication abilities in order to use data mining techniques for  knowledge discovery in academic libraries. The study concluded that the fact that these abilities are so widely valued highlights how  crucial it is to use data mining to enhance decision-making and library services. The study recommended that academic Libraries should  invest in professional development programs to enhance their librarians' technical proficiency in algorithms, analytical skills, and  communication. Prioritize Python training for handling large datasets. Integrate data mining techniques into daily operations to improve  service delivery, resource management, and user satisfaction; develop customized learning resources for different professional ranks;  encourage collaboration and networking among librarians to share best practices; address data security concerns through robust  monitoring systems and continuously evaluate and improve the impact of data mining on services to ensure effective utilization.