Benjamin Wu1 , Patrick Ngo1
1Netapp, North Sydney, Australia
Biography:
With 40 years’ consulting experience in the data industry and multiple engagments with research institutions, Ben is an collaborator who drives the awareness of shared experiences, insights and learnings to surface the best practices and approaches that help make research data management successful.
Ben researches the trends and patterns in his clients’ use of data infrastructures, employing statistical methods to identify optimal outcomes.
Ben is the Research Data transformation champion for Asia Pacific within NetApp and advocates for the unique data management needs of researchers and institutions. Ben has contributed to many research organisations’ data strategies and transformation programmes.
Abstract:
Significant challenges in research data management (RDM), as detailed in the November 2023 report (DOI: 10.5281/zenodo.10076883), result in research institutions struggle to identify, catalogue, value, and manage their data. Core RDM capabilities are often described as inaccessible or requiring considerable effort.
The rise of Artificial Intelligence (AI) and machine learning as key data analysis technologies underscores the urgency of making access to research data machine-readable, driving a need to adopt a national research data taxonomy. However, this step, while necessary, needs to be extended to be truly usable and sustainable; it must be complemented by seamless integration with RDM infrastructure to enable automated data discovery by providing scalable, safe and performant data access.
Drawing on a decade of research data management engagements, NetApp’s experts have gathered a wealth of insights, comparing Australian and international experiences. These unique insights will be shared, highlighting the strategies and best practices that have enabled our partner institutions to overcome RDM challenges and build robust capabilities.
The session will provide attendees with actionable knowledge, informed by a rich comparative analysis, to navigate their RDM complexities. By leveraging these insights, participants can strengthen their RDM systems, ensuring effective data management in an increasingly AI-integrated research environment.