Wu B1
1NetApp, North Sydney, Australia
Biography:
Ben Wu is a Principal Consultant in Business Consulting Services at NetApp and a long‑standing contributor to the Australasian eResearch community. With four decades of experience in the data industry, his work focuses on how research institutions govern, scale, and derive value from digital research content under increasing cost, compliance, and analytical pressure.
Ben’s primary area of practice lies in eResearch data strategy, research storage services, and lifecycle governance spanning institutional, national, and collaborative research environments. He has led or contributed to data strategy reviews, service design engagements, and forensic assessments for a wide range of universities and research institutes, including CSIRO, UNSW, University of Wollongong, University of Newcastle, Children’s Cancer Institute, and other public and classified research organisations. These engagements typically combine workload telemetry, stakeholder interviews, and policy analysis to surface systemic patterns affecting sustainability, cost recovery, researcher experience, reproducibility, and risk management.
A recurring theme across Ben’s published work and conference contributions is the structural gap between research data treated as an asset and storage infrastructure used as a proxy for value. He contributed to, and has since disseminated insights from, the ARDC‑funded Business Intelligence and Reporting of Research Data initiative, which documented the sector’s limited ability to characterise research data holdings in ways that meaningfully inform decisions on cost, risk, and benefit. This work has strongly influenced his subsequent outputs, particularly in relation to data taxonomy, reporting discipline, provenance, and service‑enabled operating models for research storage.
Ben is a regular contributor to eResearch Australasia, presenting lightning talks, posters, and workshops from 2021 to 2025 on topics including data fabric patterns for research, business intelligence for research data, FAIR‑aligned governance, AI‑ready research data capability, and architecting research data experiences at scale. His conference publications consistently adopt a practitioner‑researcher stance, aggregating de‑identified insights from multiple institutions to identify repeatable behaviours and transferable practices rather than institution‑specific solutions.
In parallel with his applied work, Ben actively participates in Australian Research Data Commons (ARDC) Special Interest Groups and Research Data Alliance (RDA) discussion groups, with particular emphasis on FAIR data, research data lifecycle management, provenance, and sustainable research infrastructure. Through these forums, he contributes operational perspective from live institutional environments, helping to translate community frameworks and principles into practical, adoptable patterns for research organisations operating under real‑world constraints.
More recently, Ben’s work with research institutes and shared service providers has expanded into AI‑ready research data environments, focusing on governance, lifecycle automation, and research storage services that support emerging analytics without amplifying cost, sustainability, or compliance risk. This includes engagements where research data management intersects with sovereign infrastructure, trusted research environments, and shared national capability.
Across his body of work, Ben’s contribution centres on translating observed behaviour in real research data environments into actionable service design and governance insights that can be applied across institutional, national, and international research storage services.
Abstract:
Over the past two decades, eResearch infrastructure has expanded rapidly in scale and technical capability, and the sector now operates complex data environments that support diverse disciplines, governance obligations, and analytical workloads. Sector evidence from the ARDC‑funded RDM Business Intelligence and Reporting work (released November 2023) demonstrates a persistent constraint: institutions are able to report effectively on the systems that store digital research content, while remaining limited in their ability to characterise the properties of the data itself in ways that inform cost, risk, and benefit. Reporting signals remain distributed across organisational units, collation requires sustained effort, and adoption is inhibited by unclear responsibilities, weak motivation structures, fragmented information, and the requirement for automation to achieve scalability.
This Lightning Talk provides a compelling forensic synthesis of what eResearch data environments actually reveal when examined under operational and governance pressure. Drawing on de‑identified comparative observations from multiple institutional assessments, alongside the adoption barriers documented in national initiatives, the session surfaces recurring behavioural patterns that inflate operational overhead, obscure accountability, and quietly lock in irreversible spend. These patterns persist even in environments with mature infrastructure and well‑articulated policy intent, and they become more pronounced as data volumes and analytical demands continue to grow.
The talk reframes service‑enabled eResearch data management as a capability challenge centred on measurability, integratability, and automatability. Together, these capabilities support evidence‑led custodianship, cross‑unit coherence, and policy execution at scale, while streamlining and accelerating research analytics across the data lifecycle, including emerging AI‑enabled methods. The session contributes technology‑agnostic insights that are directly applicable to the design, operation, and sustainability of institutional, national, and international research storage services.
