Interweaving academic insights: advancing university knowledge management through a strategic data fabric framework

Purpose Effective knowledge management in large academic institutions is crucial for fostering innovation and improving educational practices. However, these institutions often face challenges, such as data fragmentation, siloed information systems and the complexity of integrating different data sources from various departments with complex hierarchical structures. To address these problems, the authors proposed a data fabric strategic framework that improves and enhances knowledge management by leveraging ontologies and knowledge graphs. This study aims to investigate the potential of knowledge graphs, ontological knowledge modelling and knowledge representation to improve knowledge management in large academic institutions. It also describes how technology can enhance knowledge accessibility and exchanges and improve decision-making processes based on insights from complex educational systems. Design/methodology/approach This study uses coordination theory as a foundational framework to analyse intricate data systems in preparation for constructing, the Wizard of Oz method to facilitate the systematic organisation and management of information and the execution of an ontology-based data fabric framework and knowledge graphs. The authors propose a data fabric strategic framework aimed at improving knowledge management by leveraging ontologies and knowledge graphs. Findings The final evaluation demonstrates that this approach effectively breaks down data silos, promotes research collaboration and improves decision-making processes in large academic settings, offering solution-oriented data fabric technologies applicable to universities and university federations globally. Practical implications The proposed system provides a more efficient way of managing and connecting fragmented academic resources, improving accessibility for both learners and educators. By interconnecting and streaming knowledge management process, the system can reduce not only operational costs but also expenses on doing scientific research. Originality/value Academic institutions prioritise time efficiency when acquiring vital data for improved scientific results. This emphasis extends beyond data governance to focus on how collective intelligence might improve organisational performance. The academic community has enhanced data utilisation through the implementation of data fabric technologies to improve data accessibility and data line tracking.