A Framework for AI-Driven Data Governance in Academic Libraries
Abstract
Academic libraries are central to knowledge creation and dissemination in higher education. However, persistent challenges in service delivery continue to affect user satisfaction. This study examines how data governance and artificial intelligence (AI) can enhance the quality of library services in higher education institutions. Using a narrative literature review approach grounded in data management and service quality theories, the study conducted a bibliometric analysis of peer-reviewed publications from 2015 to 2024 retrieved from the Dimensions database. Findings reveal that implementing an integrated data governance framework supported by AI improves service efficiency, decision-making, and user experience. Nonetheless, varying scholarly views persist regarding the operational role of data governance in AI-driven systems within academic libraries. The study concludes that a cohesive framework integrating data governance and AI is vital for optimizing service quality. It recommends further investigation into data preprocessing and validation to advance library performance and contribute to the achievement of Sustainable Development Goal 4.
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Ajani, Y. A., Adefila, E. K., Olarongbe, S. A., Enakrire, R. T., & Rabiu, N. (2024). Big data and the management of libraries in the era of the Fourth Industrial Revolution: Implications for policymakers. Digital Library Perspectives, 40(2), 311-329. https://doi.org/10.1108/DLP-10-2023-0083
Ajibade, P., & Muchaonyerwa, N. (2022). The importance of data mining, user information behaviour and interaction audit for information literacy. Library Hi Tech News, 40(4), 12-14. https://doi.org/10.1108/lhtn-09-2022-0109
Akanbiemu, A. (2024). The integration of big data analytics in knowledge management: Implications for libraries. Niger Delta Journal of Library and Information Science, 5(1), 86-108. https://doi.org/10.5281/zenodo.13293311
Akter, M. (2024). AI for sustainability: Leveraging technology to address global environmental. Journal of Artificial Intelligence General Science, 3, 40-48. https://doi.org/10.60087/jaigs.v3i1.64
Aldoseri, A., Al-Khalifa, K., & Hamouda, A. (2023). A roadmap for integrating automation with process optimization for AI-powered digital transformation. Preprints. https://doi.org/10.20944/preprints202310.1055.v1
Alem, D. D. (2020). An overview of data analysis and interpretations in research. International Journal of Academic Research in Education and Review, 8(1), 1-27. https://doi.org/10.14662/ijarer2020015
Arend, D., Psaroudakis, D., Memon, J.A., Rey-Mazon, E., Schu€ler, D., Szymanski, J.J., Scholz, U., Junker, A. and Lange, M. (2022). From data to knowledge – big data needs stewardship, a plant phenomics perspective. The Plant Journal, (111)2, 335-347. https://doi.org/10.1111/tpj.15804
Arowoogun, J. O., Babawarun, O., Chidi, R., Adeniyi, A. O., & Okolo, C. A. (2024). A comprehensive review of data analytics in healthcare management: Leveraging big data for decision-making. World Journal of Advanced Research and Reviews, 21(2), 1810- 1821. https://doi.org/10.30574/wjarr.2024.21.2.0590
Arumugam, P., Sharmila, M., Jeyanthi, R., Virumandi, A., Sivankalai, S. and Rega, R. (2021). A scientometric review of global research on the sustainability of paraffin oil. Natural Volatiles & Essential Oils, 8(4). 9243-9259. https://www.nveo.org/index.php/journal/article/view/1960
Avuglah, B. K. (2020). Research Data Management (RDM) at the University of Ghana (UG): Myth or reality? International Journal of Digital Curation, 15(1), 25-25. https://doi.org/10.2218/ijdc.v15i1.670
Awada, L., Phillips, P.W. and Bogdan, A.M. (2022). Governance and stewardship for research data and information sharing: issues and prospective solutions in the transdisciplinary plant phenotyping and imaging research center network. Plants, People, Planet, (4)1, 84-95. https://doi.org/10.1002/ppp3.10238
Borgman, C.L., Scharnhorst, A. and Golshan, M.S. (2019). Digital data archives as knowledge infrastructures: Mediating data sharing and reuse. Journal of the Association for Information Science and Technology, (70)8, 888-904. https://doi.org/10.1002/asi.24172
Briney, K. A. (2019). Data management practices in academic library learning analytics: A critical review. Journal of Librarianship and Scholarly Communication, 7(General Issue), eP2268. https://doi.org/10.7710/2162-3309.2268
Coombs, C., Hislop, D., Taneva, S. K. and Barnard, S. (2020). The strategic impacts of Intelligent automation for knowledge and service work: An interdisciplinary review. The Journal of Strategic Information Systems, 29(4): 101600. https://doi.org/10.1016/j.jsis.2020.101600
Cox, A. M., Pinfield, S., & Rutter, S. (2019). The intelligent library: Thought leaders’ views on the likely impact of artificial intelligence on academic libraries. Library Hi Tech, 37(3), 418-435. https://doi.org/10.1108/LHT-08-2018-0105
DAMA International. (2017). DAMA-DMBOK: Data management body of knowledge (2nd Ed.). Technics Publications. https://ia601404.us.archive.org/33/items/dama-dmbok-2nd-edition/DAMA-DMBOK-_2nd-Edition.pdf
Dorsey, D., Cyr, C. (2025). Data modeling. In: Unlocking dbt design and deploy transformations in your cloud data Warehouse. Apress, Berkeley, CA. https://doi.org/10.1007/979-8-8688-1844-8_2
El Khatib, M., Ankit, A., Al Ameeri, I., Al Zaabi, H., Al Marqab, R., Alzoubi, H. M., & Alshurideh, M. (2023). The Role and Impact of Big Data in Organizational Risk Management. In The Effect of Information Technology on Business and Marketing Intelligence Systems (pp. 2139-2153). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-12382-5
Forbes PR. (2017). Poor-quality data imposes costs and risks on businesses, says new Forbes Insights report [Press release]. Forbes. https://www.forbes.com/sites/forbespr/2017/05/31/poor-quality-data-imposes-costs-and-risks-on-businesses-says-new-forbes-insights-report
Gupta, N., Arora, S., & Chakravarty, R. (2021). Science mapping and visualization of research data management (RDM): Bibliometric and scientometric study. Library Philosophy and Practice, 0, 1-23. https://www.proquest.com/openview/2fe895b0927e1c5b9e2a873a3655db78/1?pq-origsite=gscholar&cbl=54903
Hassani, H., & MacFeely, S. (2023). Driving excellence in official statistics: unleashing the potential of comprehensive digital data governance. Big Data and Cognitive Computing, 7(3), 134. https://doi.org/10.3390/bdcc7030134
Hodonu-Wusu, J. O. (2025). The rise of artificial intelligence in libraries: the ethical and equitable methodologies, and prospects for empowering library users. AI and Ethics, 5(2), 755-765. https://doi.org/10.1007/s43681-024-00432-7
IBM Corporation. (n.d.). Extracting business value from the 4 V’s of big data. IBM Big Data & Analytics Hub. https://www.ibmbigdatahub.com/infographic/extracting-business-value-4-vs-big-data
Janssen, M., Brous, P., Estevez, E., Barbosa, L. S., & Janowski, T. (2020). Data governance: Organizing data for trustworthy Artificial Intelligence. Government information quarterly, 37(3), 101493. https://doi.org/10.1016/j.giq.2020.101493
Jetten, M., Grootveld, M., Mordant, A., Jansen, M., Bloemers, M., Miedema, M., & Gelder, C. W. G. V. (2021). Professionalising data stewardship in the Netherlands. Competences, training and education. Dutch roadmap towards national implementation of FAIR data stewardship. Zenodo. https://doi.org/10.5281/zenodo.448642
Khalifa, M. and Albadawy, M. 2023. Using artificial intelligence in academic writing and research: An essential productivity tool. Computer Methods and Programs in Biomedicine Update, 5, 100145. https://doi.org/10.1016/j.cmpbup.2024.100145
Kalisdha, A. (2024). The impact of artificial intelligence and machine learning in library and information science. International Journal of Research in Library Science, 10: 39-58. https://doi.org/10.26761/IJRLS.10.1.2024.1733
Koltay, T. (2016). Data governance, data literacy and the management of data quality. IFLA journal, 42(4), 303-312. https://doi.org/10.1177/034003521667223
Kwanya, T. (2021). Publishing trends on research data management in Sub-Saharan Africa: A bibliometrics analysis. IASSIST Quarterly, 45(3-4). https://doi.org/10.29173/iq996
Ladley, J. (2019). Data governance: How to design, deploy, and sustain an effective data governance program. Academic Press.
Lillard, L., & Al-Suqri, M. (2019). Librarians learning from the retail sector: reaching out to online learners using customer relationship management. Journal of Arts and Social Sciences [Jass], 9(3), 15. https://doi.org/10.24200/jass.vol9iss3pp15-26
Madanayake, U. H., & Egbu, C. (2019). Critical analysis for big data studies in construction: significant gaps in knowledge. Built Environment Project and Asset Management, 9(4), 530-547. https://doi.org/10.1108/BEPAM-04-2018-0074
Marzuki., Widiati, U., Rusdin, D., Darwin, & Indrawati, I. (2023). The impact of AI writing tools on the content and organization of students’ writing: EFL teachers’ perspective. Cogent Education, 10(2), 2236469. https://doi.org/10.1080/2331186X.2023.2236469
Matsieli, M., & Mutula, S. (2025). Generative AI and the Information Society: Ethical Reflections from Libraries. Information, 16(9), 771. https://doi.org/10.3390/info16090771
Mensah, J. (2019). Sustainable development: Meaning, history, principles, pillars, and implications for human action: Literature review. Cogent Social Sciences, 5(1), 1653531, 1–22. https://doi.org/10.1080/23311886.2019.1653531
O'hara, K. (2019). Data trusts: Ethics, architecture and governance for trustworthy data stewardship. University of Southampton. https://doi.org/10.5258/SOTON/WSI-WP001
Ogunmodede, T., Adio, G., Aboyade, M., Ebijuwa, A. and Oyetola, S. (2023). The role of library and information services in achieving sustainable development: issues and challenges. International Journal of Library and Information Science studies, 9: 30-40. https://doi.org/10.37745/ijliss.15/vol9n23040
Oh, D.M., & Pyrczak, F. (2023). Making Sense of Statistics: A Conceptual Overview (8th ed.). Routledge. https://doi.org/10.4324/9781003299356
Organisation for Economic Co-operation and Development. (2016). The role of national statistical systems in the data revolution, In Development Co-operation Report 2017: Data for Development. OECD Publishing. https://doi.org/10.1787/dcr-2017-8-en
Organisation for Economic Co-operation and Development. (2017). The role of national statistical systems in the data revolution, In Development Co-operation Report 2017: Data for Development. OECD Publishing. https://doi.org/10.1787/dcr-2017-8-en
Organisation for Economic Co-operation and Development. (2018). Governing open data for sustainable results, in Open Government Data Report: Enhancing Policy Maturity for Sustainable Impact. OECD Publishing, Paris. https://read.oecdilibrary.org/governance/open-government-data-report/governing-open-data-forsustainable-results_9789264305847-4-en#page1
Organisation for Economic Co-operation and Development. (2019). Data Governance in the Public Sector in The Path to Becoming a Data-Driven Public Sector, OECD Digital Government Studies. OECD Publishing. https://doi.org/10.1787/059814a7-en
Organisation for Economic Co-operation and Development. (2021). Recommendation of the Council on Enhancing Access to and Sharing of Data. OECD Legal Instruments. https://legalinstruments.oecd.org/en/instruments/OECD-LEGAL-0463
Osasona, F., Amoo, O. O., Atadoga, A., Abrahams, T. O., Farayola, O. A., & Ayinla, B. S. (2024). Reviewing the ethical implications of AI in decision making processes. International Journal of Management & Entrepreneurship Research, 6(2), 322-335. https://doi.org/10.51594/ijmer.v6i
Owan, V. J., Abang, K. B., Idika, D. O., Etta, E. O., & Bassey, B. A. (2023). Exploring the potential of artificial intelligence tools in educational measurement and assessment. Eurasia journal of mathematics, science and technology education, 19(8), em2307. https://doi.org/10.29333/ejmste/13428
Padeli, W., Pangil, F., & Kadir, K. A. (2025). Navigating Knowledge in the Hybrid Era: Critical Success Factors for Managing Knowledge in Commercial and Industrial Hybrid Workplaces. Electronic Journal of Knowledge Management, 23(2), 01-13. https://doi.org/10.34190/ejkm.23.2.3800
Plotkin, D. (2021). Data stewardship: an actionable guide to effective data management and data governance. Academic press.
Prentice, C., Weaven, S., & Wong, I. A. (2020). Linking AI quality performance and customer engagement: The moderating effect of AI preference. International Journal of Hospitality Management, 90, 102629. https://doi.org/10.1016/j.ijhm.2020.102629
Qhal, E. M. A. (2023). The role of smart systems in enhancing the performance of knowledge management in libraries based on the adoption of using expert system and robots. International Journal of Professional Business Review: Int. J. Prof. Bus. Rev., 8(2), 5. https://doi.org/10.26668/businessreview/2023.v8i2.1353
Salman, M., San, M., & Sahid, N. (2022). Assessing the big data analytics readiness based on technology-organization-environment (toe) framework of Malaysian libraries: descriptive analysis. International Journal of Academic Research in Progressive Education and Development, 11(2). https://doi.org/10.6007/ijarped/v11-i2/13903
Scharf, D., & Dera, J. (2021). Question formulation for information literacy: Theory and practice. The Journal of Academic Librarianship, 47(4), 102365. https://doi.org/10.1016/j.acalib.2021.102365
Srisusilawati, P., Rusydiana, A. S., Sanrego, Y. D., & Tubastuvi, N. (2021). Biblioshiny R application on islamic microfinance research. Library Philosophy and Practice, 2021(5096), 1-24. https://digitalcommons.unl.edu/libphilprac/5096/
Supply Chain Resource Cooperative (2018). Data governance, data quality and artificial intelligence in the supply chain, 2nd annual. North Carolina State University.
Timotijevic, L., Carr, I., De La Cueva, J., Eftimov, T., Hodgkins, C. E., Koroušić Seljak, B., ... & Zimmermann, K. (2022). Responsible governance for a food and nutrition e-infrastructure: Case study of the determinants and intake data platform. Frontiers in Nutrition, 8, 795802. https://doi.org/10.3389/fnut.2021.795802
Tran, N. (2023). Vietnamese enterprises’ considerations of big data and analytics implementation post-covid-19 pandemic. International Journal of Organizational Analysis, 32(1), 95-107. https://doi.org/10.1108/ijoa-12-2022-3545
Yallop, A., Gică, O., Moisescu, O., Coroș, M., & Séraphin, H. (2021). The digital traveller: implications for data ethics and data governance in tourism and hospitality. Journal of Consumer Marketing, 40(2), 155-170. https://doi.org/10.1108/jcm-12-2020-4278
Yang, L., Li, J., Elisa, N., Prickett, T., & Chao, F. (2019). Towards big data governance in cybersecurity. Data-Enabled Discovery and Applications, 3(1). https://doi.org/10.1007/s41688-019-0034-9
York, J., Gutmann, M., & Berman, F. (2018). What do we know about the stewardship gap. Data Science Journal, 17, 19. https://doi.org/10.5334/dsj-2018-019
Wang, S., & Wang, H. (2020). Big data for small and medium-sized enterprises (SME): a knowledge management model. Journal of Knowledge Management, 24(4), 881-897. https://doi.org/10.1108/JKM-02-2020-0081
Whyte, A., Green, D., Avanço, K., Di Giorgio, S., Gingold, A., Horton, L., Koteska, B., Kyprianou, K., Prnjat, O., Rauste, P., Schirru, L., Sowinski, C., Torres Ramos, G., van Leersum, N., Sharma, C., Mendez, E. and Lazzeri, E. (2023). D2.1 catalogue of open science career profiles - minimum viable skillsets, (v1.2). https://doi.org/10.5281/zenodo.8101903
Wendelborn, C., Anger, M., & Schickhardt, C. (2023). What is data stewardship? Towards a comprehensive understanding. Journal of biomedical informatics, 140, 104337. https://doi.org/10.1016/j.jbi.2023.104337
Wildgaard, L., & Rantasaari, J. (2022). RDA professionalising data stewardship-data stewardship landscape initial report. https://doi.org/10.15497/RDA00076
Venkatraman, S., Uchendu, A., & Lee, D. (2023). Gpt-who: An information density-based machine-generated text detector. arXiv preprint arXiv:2310.06202.
Verhulst, S. G. (2021). Reimagining data responsibility: 10 new approaches toward a culture of trust in re-using data to address critical public needs. Data & Policy, 3, e6. https://doi.org/10.1017/dap.2021.4
DOI: https://doi.org/10.17509/edulib.v15i2.87417
DOI (PDF): https://doi.org/10.17509/edulib.v15i2.87417.g34547
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