The Challenge

Across both phases, the engagement required navigating significant technical, analytical, and organizational complexity within compressed timelines.

• An active data crisis with real stakes. The engagement was initiated because the organization had identified a live risk to its scientific mission and legacy data holdings. Recommendations needed to be grounded in the organization’s own evidence base and credible to a technically diverse research community, not generic best practices.
• Complex internal survey data requiring structured analysis. The SDSNA survey captured data on storage types, volumes, access patterns, processing requirements, and gaps across a diverse set of research labs. Extracting defensible insights from this material to inform options selection required systematic analysis rather than surface-level synthesis.
• Gap analysis and workflow mapping. The client had produced a current-state workflow diagram and gap analysis documenting how research labs actually store, access, and process scientific data alongside the optimal workflow the organization sought to achieve. Bronson needed to use this gap as the primary frame for evaluating storage system options.
• Three options, not one. The Phase 1 deliverable required Bronson to identify and compare three distinct storage and network system options, each with its own advantages, disadvantages, cost estimate, cost-benefit analysis, and implementation timeframe — demanding both breadth of systems knowledge and the discipline to meaningfully differentiate options.
• Cost-benefit analysis under uncertainty. Many science data storage costs are difficult to estimate precisely, particularly for cloud-based systems with variable usage-based pricing. Producing credible cost-benefit comparisons for three options required transparent assumptions and sensitivity to implementation context.
• Multi-stakeholder advisory structure. The Phase 1 working group spanned scientific, security, policy, and information management branches, each with distinct priorities. Recommendations needed to hold up to scrutiny across this range of perspectives.
• Scaling to 22 systems in Phase 2. The Phase 2 classification framework required a consistent and defensible assessment of 22 storage systems across five storage categories, covering security classification, cost profile, regional accessibility, future outlook, and suitability ratings. Maintaining analytical consistency across this scope while producing output usable by non-specialist research staff was a significant design challenge.
• Diverse and overlapping storage landscape. The 22 systems assessed range from locally managed network-attached storage and portable field devices through to shared supercomputing clusters, approved cloud platforms, external collaboration networks, open data portals, and specialized genomics research infrastructure. No single prior framework described how these systems related to each other or to the stages of scientific data work.
• Bandwidth and regional access constraints. Research teams at regional science centers face persistent bandwidth limitations affecting the usability of centrally managed systems. The framework needed to explicitly capture these constraints rather than assuming uniform connectivity across the organization.
• FAIR alignment as an organizational priority. Both phases needed to connect the storage infrastructure analysis to the organization’s broader objective of applying FAIR principles to its scientific datasets, particularly for archival storage where metadata, inventorying, and cataloguing features are essential.

The organization needed an engagement partner capable of absorbing a complex internal evidence base, making defensible recommendations within tight timelines, and building durable operational tools for a diverse scientific community.

Our Solution

Bronson structured the engagement across two phases, each with its own analytical workstreams and deliverables, while ensuring the Phase 2 framework was explicitly grounded in the findings and recommendations of Phase 1.

Phase 1: Needs Assessment and Options Report (2022)

1. Project Kickoff and Scope Confirmation

Bronson convened a kickoff meeting with the client project team and working group to align on deliverables, schedule, data transfer arrangements, and the format and acceptance criteria for the draft and final reports. A weekly progress meeting cadence was established to keep the six-week engagement on track.

2. SDSNA Survey Data Processing and Analysis

Bronson processed and analyzed the SDSNA survey data collected from the organization’s research labs, extracting findings on current storage types, data volumes, access patterns, processing requirements, and identified gaps. This analysis established the evidence base for options development, ensuring recommendations reflected the organization’s expressed needs rather than assumed requirements.

3. Gap Analysis and Workflow Review

Bronson reviewed the current-state workflow diagram and gap analysis, mapping the distance between how research labs were actually managing scientific data and the optimal workflow the organization sought to achieve. This review informed the selection and ranking of storage system options by clarifying which gaps each option was best positioned to close.

4. Options Identification and Comparative Analysis

Drawing on the survey analysis, gap review, and a broad assessment of available science data storage and network systems, Bronson identified the top three options most suited to the organization’s research community. For each option, Bronson produced a structured comparative analysis covering fit with identified needs, advantages, disadvantages, applicable use cases, and relevant constraints including security classification, regional accessibility, and management requirements.

5. Cost-Benefit Analysis and Implementation Planning

For each of the three recommended options, Bronson developed a cost-benefit analysis incorporating estimated direct and indirect costs, implementation complexity, ongoing operational requirements, and expected benefits. Implementation timeframe estimates were provided for each option, structured to reflect the organization’s capacity and resource environment.

6. Draft Report, Working Group Review, and Final Delivery

Bronson produced a draft options report and circulated it to the client project team and multi-branch working group for structured feedback. Bronson incorporated this feedback into the final report, which was accepted by the March 31, 2022 deadline.

Phase 2: Storage Classification Framework and Decision Tools (2023)

7. Storage Type Framework Development

Bronson defined five scientific data storage types corresponding to the stages of the scientific data lifecycle: Portable and Collection Storage (temporary field storage and raw data lakes); Robust and Scratch Storage (processing and analysis space, including temporary scratch space near HPC resources); Sharing and Collaboration Storage (controlled external sharing with permission management and retention limits); Open Data and Publication Storage (public-facing storage for final scientific products); and Archival Storage (long-term storage of legacy datasets with metadata, inventorying, cataloguing, and FAIR-aligned management). Each type was defined with plain-language descriptions, storage volume requirements, and operational characteristics.

8. 22-System Comparative Assessment

Bronson assessed all 22 storage systems against the five storage types, producing a structured matrix rating each system’s suitability for each category. For each system, the assessment documented the system’s purpose and technical characteristics, managing entity, future outlook, regional accessibility constraints, cost profile, and data security classification level. The systems assessed span network drives, supercomputing clusters and their collaboration variants, approved cloud platforms, network-attached storage, research collaboration networks, genomics infrastructure, external storage devices, local workstations, open data portals, geospatial platforms, file transfer services, collaborative cloud tools, data hubs, code repositories, object storage variants, and the Digital Research Alliance.

9. Decision Flowchart Development

Bronson developed a visual decision flowchart to guide research teams and data managers through storage selection decisions based on data type and lifecycle stage. The flowchart maps from data collection through processing, sharing, publication, and archival, providing clear branching logic usable without technical expertise.

10. Storage Landscape Visualization

Bronson produced a diagrammatic overview of the full storage landscape, mapping the five storage types and their associated systems across the data lifecycle from field collection to archival and open data publication, designed as a quick-reference orientation tool for research staff encountering the framework for the first time.

Key Deliverables

• Science Data Storage and Network Systems Options Report (Draft and Final) – A structured report presenting the top three science data storage and network system options, with comparative analysis of advantages, disadvantages, cost estimates, cost-benefit analysis, and implementation timeframes for each option. Final version accepted March 31, 2022.
• SDSNA Survey Data Analysis – A structured analysis of the organization’s internally administered Science Data Storage Needs Assessment survey, identifying storage patterns, gaps, and requirements across the research lab community.
• Gap Analysis Review and Workflow Assessment – A review of the current-state data storage workflow diagram and gap analysis, mapping the distance between current research lab practices and the optimal storage workflow and framing the selection criteria for recommended options.

The Impact

Together the two phases gave the organization both the immediate strategic direction to resolve its data crisis and the durable operational infrastructure to manage science data storage consistently over the long term.

• The SDSNA-grounded options report gave decision-makers recommendations directly traceable to the organization’s own survey data and gap analysis, producing credibility with the research community and working group advisors that generic benchmarking could not have achieved.
• The top-three options structure with individual cost-benefit analyses gave the organization a practical, differentiated basis for investment prioritization, addressing the resource constraints that had contributed to fragmentation in the first place.
• The implementation timeframe estimates translated recommendations into actionable planning inputs, allowing the organization to begin sequencing storage improvements against its operational and budget cycles immediately following report acceptance.
• The engagement directly addressed the risk to legacy scientific datasets, with both the options recommendations and the archival storage category of the classification framework explicitly oriented toward long-term preservation and FAIR-aligned data management.
• The 22-system comparative matrix consolidated dispersed and often informal knowledge about the organization’s storage landscape into a single authoritative reference, surfacing gaps, risks, and future outlook concerns that had not been systematically documented.
• The decision flowchart made the Phase 2 framework immediately actionable for scientists and program leads without technical backgrounds, providing a practical tool for storage selection at the point of need.
• The multi-branch working group review ensured Phase 1 recommendations were tested against scientific, security, policy, and information management perspectives before acceptance, giving the final report credibility across the full organizational stakeholder community.

The engagement reflects Bronson’s ability to rapidly absorb complex internal evidence bases, make defensible costed recommendations under tight timelines, and build durable operational tools for diverse scientific communities. By grounding both phases in the organization’s own data and aligning all outputs to FAIR principles and the full scientific data lifecycle, Bronson delivered not just a point-in-time report but a lasting framework for science data management decision-making.