Developer Synthetic Data Survey Template: Adoption & Ethics
Assess developer comfort, adoption, ethics, and use cases for synthetic data. Ready-to-use survey template covering risks, policy, and boundaries.
What's Included
AI-Powered Questions
Intelligent follow-up questions based on responses
Automated Analysis
Real-time sentiment and insight detection
Smart Distribution
Target the right audience automatically
Detailed Reports
Comprehensive insights and recommendations
Template Overview
23
Questions
AI-Powered
Smart Analysis
Ready-to-Use
Launch in Minutes
This professionally designed survey template helps you gather valuable insights with intelligent question flow and automated analysis.
Sample Survey Items
Q1
Chat Message
Welcome! This brief survey takes about 6 minutes. Your responses are confidential and reported in aggregate.
Q2
Chat Message
Synthetic data refers to artificially generated data (e.g., via simulations or generative models) intended to mimic real data’s statistical properties while protecting sensitive information or filling gaps.
Q3
Multiple Choice
In the last 12 months, have you worked with synthetic data?
Yes, regularly (monthly or more)
Yes, occasionally
No, familiar with the concept
No, not familiar
Q4
Multiple Choice
Which use cases are most relevant for you or your team? (Select all that apply)
Prototyping or training ML models
Class imbalance augmentation
Privacy-preserving sharing/compliance
Testing and QA (e.g., edge cases, rare events)
Synthetic logs or telemetry for load testing
Analytics demos or sandboxing
Education or training
Q5
Dropdown
Which tools or approaches have you mainly used to generate synthetic data?
Haven’t used tools directly (consumed output from others)
Q6
Numeric
Approximately what percentage of data in your projects over the last 12 months was synthetic? Enter a number from 0 to 100.
Accepts a numeric value
Whole numbers only
Q7
Long Text
If compliance or privacy is a priority, briefly describe the data types or regulations you must satisfy (e.g., HIPAA, GDPR).
Max 600 chars
Q8
Rating
How much demonstrated fidelity/utility do you require before using synthetic data in production?
Scale: 11 (star)
Min: Low confidence neededMax: High confidence needed
Q9
Matrix
For each activity, how appropriate is synthetic data in your context?
Rows
Never appropriate
Rarely
Sometimes
Often
Always appropriate
Early prototyping
•
•
•
•
•
Model training at scale
•
•
•
•
•
Model testing/validation
•
•
•
•
•
Performance benchmarking
•
•
•
•
•
Data sharing with partners
•
•
•
•
•
Analytics and dashboards
•
•
•
•
•
Q10
Ranking
Rank your top concerns about synthetic data (drag to order).
Drag to order (top = most important)
Privacy leakage or re-identification
Bias amplification or fairness issues
Poor realism or utility
Regulatory or compliance risk
Lack of transparency/traceability
Leakage of secrets/IP
Q11
Opinion Scale
How likely are you to try synthetic data in the next 6 months?
Range: 1 – 10
Min: Very unlikelyMid: NeutralMax: Very likely
Q12
Multiple Choice
Which factors most limit your use of synthetic data today? (Select all that apply)
Hard to evaluate quality/metrics
Limited domain coverage
Tooling or integration gaps
Compute or cost constraints
Stakeholder skepticism/buy-in
Policy/legal uncertainty
No clear need
Q13
Constant Sum
Allocate 100 points to the improvements that would most accelerate adoption in your org.
Total must equal 100
Better quality and validation metrics
Broader domain and data type coverage
Easier integration with existing pipelines
Lower cost or compute requirements
Clear policy/legal guidance and templates
Independent benchmarks and case studies
Training and best-practice playbooks
Min per option: 0Whole numbers only
Q14
Long Text
Anything else we should know about your limits or ideal use cases for synthetic data?
Max 600 chars
Q15
Multiple Choice
Which best describes your primary role?
Software engineer
ML/AI engineer
Data scientist
Data/ML platform engineer
Security/privacy engineer
Product/engineering manager
Researcher/academic
Other
Q16
Dropdown
Years of professional experience in software/data roles
0–1 years
2–4 years
5–9 years
10–14 years
15+ years
Q17
Multiple Choice
What is your primary domain or industry?
Technology
Finance/FinTech
Healthcare/Life sciences
Retail/Consumer
Telecom/Media
Manufacturing/Industrial
Government/Public sector
Education
Other
Q18
Dropdown
Organization size (global headcount)
1–9
10–49
50–249
250–999
1,000–4,999
5,000–19,999
20,000+
Q19
Dropdown
Region you primarily work in
North America
Latin America
Europe
Middle East
Africa
South Asia
East Asia
Southeast Asia
Oceania
Q20
Multiple Choice
Quality check: To confirm attention, please select “Yes.”
Yes
No
Q21
Long Text
Any final comments or context you want to share?
Max 600 chars
Q22
AI Interview
AI Interview: 2 Follow-up Questions on your responses about synthetic data
AI InterviewLength: 2Personality: [Object Object]Mode: Fast
Q23
Chat Message
Thank you for participating! Your input helps us understand practical needs and considerations around synthetic data.
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