Short answer
Integrate a forward-looking stakeholder engagement process into the early stages of infrastructure or service design to proactively identify and plan for future data management and capacity requirements.
- Field
- Commercial Production
- Source
- Lawrence Berkeley National Laboratory (2022)
- Method
- Qualitative Research / Case Study
- Evidence
- Strong effect
Engaging with research stakeholders to forecast data output and infrastructure needs over a 5-10 year horizon allows for more informed and cost-effective strategic planning of network operations and technological service investments. This commercial production research insight is drawn from a 2022 study published in Lawrence Berkeley National Laboratory. Using Qualitative research / case study, researchers explored how this design variable affects real-world outcomes. The key design takeaway: Integrate a forward-looking stakeholder engagement process into the early stages of infrastructure or service design to proactively identify and plan for future data management and capacity requirements.
Proactive Data Management Planning Reduces Cyberinfrastructure Investment Risk
Engaging with research stakeholders to forecast data output and infrastructure needs over a 5-10 year horizon allows for more informed and cost-effective strategic planning of network operations and technological service investments.
Lawrence Berkeley National Laboratory · 2022
Key Findings
- 01A structured process can effectively elicit future data management requirements from diverse research stakeholders.
- 02Understanding anticipated data output is essential for strategic planning of network capacity and technological service investments.
- 03Long-term (5-10 year) forecasting of research needs enables proactive cyberinfrastructure development.
Application
Design takeaway
Integrate a forward-looking stakeholder engagement process into the early stages of infrastructure or service design to proactively identify and plan for future data management and capacity requirements.
How to apply
When designing or upgrading any system that supports research or data-intensive activities, conduct in-depth interviews and workshops with end-users to understand their projected data generation and processing needs over the next 5-10 years.
Project actions
- 01When defining the scope of your design project, consider the long-term implications of user data generation.
- 02Incorporate methods for forecasting future user needs into your research plan.
Method & Evidence
Variables
Strengths & Limitations
Strengths
- +Focuses on a long-term planning horizon (5-10 years).
- +Emphasizes collaboration between infrastructure providers and end-users (researchers).
Limitations
The 'Deep Dive' process might be time-consuming and require significant resources to implement effectively.
Reliability & validity
The reliability of the findings depends on the consistency of the 'Deep Dive' process across different teams and institutions. Validity is supported by the direct engagement with stakeholders to understand their needs.
Think critically
To what extent can 'anticipating' future needs truly be accurate, and what are the risks associated with over- or under-investing based on these forecasts?
Design Principles
"Anticipate future needs through continuous stakeholder engagement and data forecasting."
Understanding the future data demands of research activities is crucial for designing scalable and efficient cyberinfrastructure. This proactive approach helps avoid costly reactive upgrades and ensures that technological services align with evolving research requirements, ultimately supporting the long-term viability of research endeavors.
What This Means for Your Design
Talking to the people who will use a system (like researchers) about how much data they think they'll create in the future helps plan the technology needed, saving money and making sure it works when they need it.
How to use in your project
- 1.Reference this study when discussing the importance of user needs analysis and long-term planning in your design project's introduction or methodology section.
- 2.Use the 'Deep Dive' process as a model for your own user research, adapting it to your specific project context.
Add to My Project
Quick Cite
Paragraph starter
This research highlights the critical role of proactive requirements analysis in ensuring the long-term viability of technological infrastructure. By engaging with stakeholders to forecast data output and needs over a 5-10 year horizon, as demonstrated by the EPOC 'Deep Dive' process at Arizona State University, design teams can make more informed strategic decisions regarding network operations and service investments, thereby mitigating future risks and optimizing resource allocation.
Source
Lawrence Berkeley National Laboratory
Arizona State University Requirements Analysis Report
journal · 2022
View sourceQuestions About This Research
- What does the research say about proactive data management planning reduces cyberinfrastructure investment risk?
- Integrate a forward-looking stakeholder engagement process into the early stages of infrastructure or service design to proactively identify and plan for future data management and capacity requirements. Evidence: Lawrence Berkeley National Laboratory (2022).
- Why does "Proactive Data Management Planning Reduces Cyberinfrastructure Investment Risk" matter for design?
- Understanding the future data demands of research activities is crucial for designing scalable and efficient cyberinfrastructure. This proactive approach helps avoid costly reactive upgrades and ensures that technological services align with evolving research requirements, ultimately supporting the long-term viability of research endeavors.
- How can designers apply this research?
- Integrate a forward-looking stakeholder engagement process into the early stages of infrastructure or service design to proactively identify and plan for future data management and capacity requirements.
- What were the main findings?
- A structured process can effectively elicit future data management requirements from diverse research stakeholders.. Understanding anticipated data output is essential for strategic planning of network capacity and technological service investments.. Long-term (5-10 year) forecasting of research needs enables proactive cyberinfrastructure development.
- What research method was used?
- Qualitative Research / Case Study.
- How strong is the evidence?
- Evidence strength is rated Strong effect, based on a 2022 journal from Lawrence Berkeley National Laboratory.
- What should I do differently in my next project?
- When designing or upgrading any system that supports research or data-intensive activities, conduct in-depth interviews and workshops with end-users to understand their projected data generation and processing needs over the next 5-10 years.
- What are the limitations?
- The findings are specific to the context of Arizona State University and may not be directly generalizable to all research institutions without adaptation.