The Skills of Information Professionals for the Analysis and Organization of Data. Conceptual Proposal and Application of OpenAI in Job Advertisements on the Web

Resumen

This study explores the evolving roles of library and information professionals (IPs) in data analysis and organization, particularly within interdisciplinary teams. It builds on the premise that data is pivotal for strategic business and institutional management, with IPs contributing through their expertise in information and communication technologies (ICTs), mediation with users, and mastery of taxonomies and semantic organizational structures (SOCs). The study examines job advertisements to determine the skills, experience, and training employers seek in IPs for data-centric roles. A total of 84 international job postings from platforms such as ALAJobLIST, LinkedIn, and Glassdoor were analyzed. The postings were XML-tagged to identify descriptions, competencies, and requirements. Using OpenAI tools, keywords were extracted, standardized, and grouped into thematic clusters and macro-clusters for analysis. The findings reveal that required skills span eight macro-clusters: business management; data management; information organization; data analysis and statistics; programming; web technologies; analytical creativity; and domain-specific expertise. IPs are valued for their technical and technological skills, particularly in navigating and applying standards for data description, visualization, and communication. Conclusions highlight that while IPs possess strong foundational skills, additional training in statistics and analysis platforms is critical to fully meet employer demands. Employers trust IPs for their ability to handle data-centric roles in competitive environments, emphasizing the importance of communication, leadership, and customer-oriented skills. The study underscores the need to enhance LIS curricula to better integrate advanced statistical training and entrepreneurial competencies. This research emphasizes the growing recognition of IPs’ contributions to data analysis and organization, underscoring their essential role in addressing the complexities of interdisciplinary data-driven projects