Optimizing Data Capture and Storage for Research Data Management: A Study on Cloud Computing Solutions in Academic Institutions

Abstract

The increasing volume of research data generated by academic institutions necessitates effective data management strategies to ensure data accessibility, security, and long-term preservation. This study explores the optimization of data capture and storage for research data management, focusing on cloud computing solutions within academic settings. A systematic literature review was conducted, examining studies from the past five years sourced from reputable databases such as IEEE, Springer, and Elsevier. Thematic analysis was employed to identify key trends, challenges, and best practices related to data management, cloud technologies, and storage optimization. The results of a bibliometric analysis indicated a significant upward trend in publications addressing Data Management, Cloud Computing, and Research Data Storage between 2009 and 2023, with a notable peak in 2023. Findings revealed that while cloud computing platforms offer significant advantages—such as scalability, cost-efficiency, and enhanced collaboration—challenges related to data standardization, security, and interoperability persist. Furthermore, the study highlights the growing   importance of automated data capture techniques and metadata tagging in managing large datasets. Despite the transformative potential of cloud-based solutions, optimization efforts remain necessary to fully realize their benefits for research purposes. This research underscores the need for future empirical studies to test cloud solutions in real-world academic contexts and develop standardized, secure, and efficient practices for research data management. Optimizing cloud computing solutions is crucial for enabling academic institutions to meet the demands of the evolving digital research environment