Бібліотечний вісник | bv.nbuv.gov.ua
Телла А. - Забезпечення надійних даних: інноваційні підходи до досліджень і практики метаданих, Манду М. (2025)

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Tella Adeyinka, Mandu Molaudi

Ensuring Reliable Data: Innovative Approaches to Metadata Research and Practices

Section: Library affair and science. Foreign experience

Abstract: The purpose of the article. The increasing volume and diversity of data sources, including structured and unstructured data, pose challenges in maintaining data reliability. Therefore, this study examined innovative approaches to metadata research and practices to ensure reliable data. Research methods. The literature search strategy was adopted. This involves defining the scope of the study, including the specific aspects of innovative approaches to metadata research and practices. The study used multiple databases (e.g., Scopus, Google scholar) to have access to related contents through academic journals, conference proceedings, books and reports to gather relevant literature. Scientific novelty. This study is unique in that it emphasises data management dependability, a factor that metadata studies frequently overlook, making it both important and original. It is notable for emphasising the vital necessity of trustworthy data, which is necessary for precise decision-making and maintaining the integrity of research. Innovative approaches are included because they indicate a forward-thinking viewpoint and promise to investigate novel techniques and technologies. This blend of originality and dependability fills a vacuum in the literature by providing new perspectives and useful fixes. In addition, the title’s focus on practices implies practical application, which makes it extremely pertinent for professionals looking to enhance data management and quality procedures. Conclusions: innovative metadata practices play a pivotal role across various industry sectors, enabling organizations to enhance data reliability, accuracy, consistency, and trustworthiness. Through case studies and real-world implementations, we see how metadata automation, semantic modeling, collaborative platforms, and governance frameworks contribute to improved data management, decision-making, and operational efficiencies. In healthcare, semantic metadata modeling aids in complex data relationships, improving patient care and medical research. Financial services benefit from metadata automation for compliance reporting and risk management. Retail and e-commerce sectors leverage collaborative metadata platforms for product data management and customer satisfaction.

Keywords: Meta-data, Reliable Data: Innovative Approaches, Metadata Research, Practices, Data Accuracy, Data Integrity.


Author(s) citation:

Cite:
Tella Adeyinka (2025). Ensuring Reliable Data: Innovative Approaches to Metadata Research and Practices. Bibliotechnyi visnyk, (4) 132-145. doi: https://doi.org/10.15407/bv2025.04.132

References:

  1. Aldoseri, A., and Al-Khalifa, N. K., Hamouda, A. M. (2022). Re-thinking data strategy and integration for artificial intelligence: concepts, opportunities, and challenges. Applied Sciences,13 (12), 7082. [In English]. doi: https://doi.org/10.3390/app13127082
  2. Badi, S. (2024, February). IoT and Big Data analytics: revolutionizing business and society with advanced insights. International Journal of Applied Mathematics and Computer Science, 34, 42-56. [In English].
  3. Bernasconi, E., and Di Pierro, D., Redavid, D., Ferilli, S. (2022). SKATEBOARD: semantic knowledge advanced tool for extraction, browsing, organisation, annotation, retrieval, and discovery. Applied Sciences, 13 (21), 11782. [In English]. doi: https://doi.org/10.3390/app132111782
  4. Berry, S. (2024). Metadata management: what it is, why it is important, when to use it, and best practices. [In English].
  5. Brown, A. W., and Kaiser, K. A., Allison, D. B. (2018). Issues with data and analyses: Errors, underlying themes, and potential solutions. Proceedings of the National Academy of Sciences, 115 (11), 2563-2570. [In English]. doi: https://doi.org/10.1073/pnas.1708279115
  6. Cai, L. and Zhu, Y. (2015). The challenges of data quality and data quality assessment in the Big Data era. Data Science Journal, 14. [In English]. doi: https://doi.org/10.5334/dsj-2015-002
  7. (2024). Case study on Internet of Things in manufacturing. OECRiLibrary: web site. [In English].
  8. (2023). Data lineage in data governance: unlocking the potential of modern data landscapes with comprehensive data tracking. [In English].
  9. Dunsin, D., and Ghanem, M. C., Ouazzane, K., Vassilev, V. (2024). A comprehensive analysis of the role of artificial intelligence and machine learning in modern digital forensics and incident response. Forensic Science International: Digital Investigation, 48, 301675. [In English]. doi: https://doi.org/10.1016/j.fsidi.2023.301675
  10. Elouataoui, W., and El Mendili, S., Gahi, Y. (2023). An automated Big Data quality anomaly correction framework using predictive analysis. Data, 8 (12), 182. [In English]. doi: https://doi.org/10.3390/data8120182
  11. Ghafoor L., and Tahir F. (2023). Data governance in the era of Big Data: best practices and strategies. [In English].
  12. Hassani, H., and MacFeely, S. (2023). Driving excellence in official statistics: unleashing the potential of comprehensive digital data governance. Big Data and Cognitive Computing, 7(3), 134. [In English]. doi: https://doi.org/10.3390/bdcc7030134
  13. Hillmann, D. I., and Marker, R. Brady, C. (2008). Metadata standards and applications.The Serials Librarian, 54 (1/2), 7-21. [In English]. doi: https://doi.org/10.1080/03615260801973364
  14. Hoseini, S., and Theissen-Lipp, J., Quix, C. (2024). A survey on semantic data management as intersection of ontology-based data access, semantic modeling and data lakes. Journal of Web Semantics, 81 (3), 100819. [In English]. doi: https://doi.org/10.1016/j.websem.2024.100819
  15. (2024). Intuitive performance management for today’s diverse networks. [In English].
  16. Khurana, D., and Koli, A., Khatter, K. (2023). Sukhdev Singh Natural language processing: state of the art, current trends and challenges. Multimedia Tools and Applications, 82, 3713-3744. [In English]. doi: https://doi.org/10.1007/s11042-022-13428-4
  17. Koltay, T. (2016). Data governance, data literacy and the management of data quality. IFLA Journal. [In English]. doi: https://doi.org/10.1177/0340035216672238
  18. Mosha, N. F., and Ngulube, P. (2023). Metadata standard for continuous preservation, discovery, and reuse of research data in repositories by higher education institutions: asystematic review. Information, 14 (8), 427. [In English]. doi: https://doi.org/10.3390/info14080427
  19. Muñoz, A., and Martí, L., Sánchez-Pi, N. (2021). Data governance, a knowledge model through ontologies. Technologies and Innovation. 18-32. [In English]. doi: https://doi.org/10.1007/978-3-030-88262-4_2
  20. Nangia, S., and Makkar S., Hassan R. (2020). IoT based predictive maintenance in manufacturing sector. In International Conference on Innovative Computing and Communication (ICICC 2020). New Delhi, India. [In English].
  21. National Information Technology Development Agency (NITDA). (2019). Nigeria e-government interoperability framework (Ne-GIF). [In English].
  22. Pajooh, H. H., and Rashid, M., Alam, F., Demidenko, S. (2020). Hyperledger Fabric Blockchain for Securing the Edge Internet of Things. Sensors, 21 (2). [In English]. doi: https://doi.org/10.3390/s21020359
  23. Pala, S. K. (2024). Implementing master data management on healthcare data tools like (Data Flux, MDM Informatica and Python). International Journal of Transcontinental Discoveries, 10 (1). [In English]. doi: https://doi.org/10.13140/RG.2.2.31823.46243
  24. Pan, B., and Stakhanova, N., Ray, S. (2023 December). Data provenance in security and privacy. ACM Computing Survey, 55 (14), Article 323. [In English]. doi: https://doi.org/10.1145/3593294
  25. Quarati, A., and Albertoni, R. (2024). Linked open government data: still a viable option for sharing and integrating public data? Future Internet, 16 (3), 99. [In English]. doi: https://doi.org/10.3390/fi16030099
  26. Rathore, B. (2016). Leveraging IoT and AI for smart manufacturing through smart industrial automation. International Journal of New Media Studies, 3, 2394-4331. [In English]. doi: https://doi.org/10.58972/eiprmj.v3i2y16.96
  27. Sadeghi, M., and Carenini, A., Corcho, O., Rossi, R. S., Andreas, V. (2024). Interoperability of heterogeneous Systems of Systems: from requirements to a reference architecture. The Journal Supercomputing, 80, 8954-8987. [In English]. doi: https://doi.org/10.1007/s11227-023-05774-3
  28. Tengilimoğlu, D., and Orhan, F., Şenel Tekin, P., Younis, M. (2023). Analysis of publications on health information management using the science mapping method: a holistic perspective. Healthcare, 12 (3), 287. [In English]. doi: https://doi.org/10.3390/healthcare12030287
  29. Thalhath, N., Nagamori, M., and Sakaguchi, T. (2024). Metadata application profile as a mechanism for semantic interoperability in FAIR and open data publishing. Data and Information Management, 10 (1), 100068. [In English]. doi: https://doi.org/10.1016/j.dim.2024.100068
  30. Ulrich, H., Kock-Schoppenhauer, K., Deppenwiese, N., Gött, R., Kern, J., Lablans, M.,… and Ingenerf, J. (2021). Understanding the nature of metadata: systematic review. Journal of Medical Internet Research, 24 (1). [In English]. doi: https://doi.org/10.2196/25440
  31. Villar, A., and Abowitz, S., Read, R., Butler, J. (2024). Maximizing supply chain resilience: viability of a distributed manufacturing network platform using the open knowledge resilience framework. Operation Resources Forum, 5 (26). [In English]. doi: https://doi.org/10.1007/s43069-024-00303-1
  32. Vnuk, L., and Koronios, A., Gao, J. (2012). Managing metadata towards enhanced data quality in asset management. In: Mathew, J., Ma, L., Tan, A., Weijnen, M., Lee, J. (eds.). Engineering Asset Management and Infrastructure Sustainability. Springer, London. [In English]. doi: https://doi.org/10.1007/978-0-85729-493-7_76
  33. Wang, J., and Liu, Y., Li, P., Lin, Z., Sindakis, S., Aggarwal, S. (2023). Overview of data quality: examining the dimensions, antecedents, and impacts of data quality. Journal of the Knowledge Economy, 15, 1159-1178. [In English]. doi: https://doi.org/10.1007/s13132-022-01096-6