Measures developer experience, tooling preferences, risk perceptions, and adoption intent for synthetic data. Designed for engineering and data science teams evaluating synthetic data readiness and ethical 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
24
Questions
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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! Thank you for participating in this survey about synthetic data practices. This survey takes approximately 6 minutes to complete.
Your participation is entirely voluntary, and you may stop at any time. There are no right or wrong answers — we are interested in your honest opinions and experiences. All responses are confidential, anonymized, and reported only in aggregate. Results will be used for internal research to better understand developer needs around synthetic data.
Q2
Chat Message
<p>For this survey, <strong>synthetic data</strong> 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.</p>
Q3
Multiple Choice
In the last 12 months, have you worked with synthetic data?
Yes, regularly (monthly or more)
Yes, occasionally
No, but I am familiar with the concept
No, and I am not familiar with it
Q4
Multiple Choice
Which use cases for synthetic data are most relevant to you or your team? (Select all that apply)
Prototyping or training ML models
Class imbalance augmentation
Privacy-preserving sharing or compliance
Testing and QA (e.g., edge cases, rare events)
Synthetic logs or telemetry for load testing
Analytics demos or sandboxing
Education or training
Other (please specify)
Q5
Multiple Choice
Which tools or approaches have you used to generate synthetic data? (Select all that apply)
<p>How appropriate is synthetic data for <strong>model training and development</strong> in your context?</p>
Range: 1 – 7
Min: Not at all appropriateMid: NeutralMax: Highly appropriate
Q9
Opinion Scale
<p>How appropriate is synthetic data for <strong>production decision-making</strong> in your context?</p>
Range: 1 – 7
Min: Not at all appropriateMid: NeutralMax: Highly appropriate
Q10
Opinion Scale
<p>How appropriate is synthetic data for <strong>testing and QA</strong> in your context?</p>
Range: 1 – 7
Min: Not at all appropriateMid: NeutralMax: Highly appropriate
Q11
Opinion Scale
<p>How appropriate is synthetic data for <strong>external reporting or compliance submissions</strong> in your context?</p>
Range: 1 – 7
Min: Not at all appropriateMid: NeutralMax: Highly appropriate
Q12
Ranking
Rank your top concerns about synthetic data from most to least concerning.
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 or traceability
Leakage of secrets or intellectual property
Q13
Long Text
If compliance or privacy is a priority in your work, briefly describe the data types or regulations you must satisfy (e.g., HIPAA, GDPR, PCI-DSS). If not applicable, you may skip this question.
Max chars
Q14
Opinion Scale
How likely are you to increase your use of synthetic data in the next 6 months?
Range: 1 – 7
Min: Very unlikelyMid: NeutralMax: Very likely
Q15
Multiple Choice
Which factors most limit your use of synthetic data today? (Select all that apply)
Hard to evaluate quality or metrics
Limited domain coverage
Tooling or integration gaps
Compute or cost constraints
Stakeholder skepticism or buy-in
Policy or legal uncertainty
No clear need
Other (please specify)
Q16
Ranking
Rank the following improvements by how much they would accelerate synthetic data adoption in your organization, from most to least impactful.
Drag to order (top = most important)
Better quality and validation metrics
Broader domain and data type coverage
Easier integration with existing pipelines
Lower cost or compute requirements
Clear policy and legal guidance or templates
Independent benchmarks and case studies
Training and best-practice playbooks
Q17
Long Text
Based on your responses in this survey, please share any additional thoughts about your limits, ideal use cases, or expectations for synthetic data.
Max chars
Q18
AI Interview
We'd like to explore a few of your responses in more depth. An AI moderator will ask you up to 2 brief follow-up questions based on what you've shared so far.
AI InterviewLength: 2Personality: [Object Object]Mode: Fast
Reference questions: 5
Q19
Multiple Choice
Which best describes your primary role?
Software engineer
ML/AI engineer
Data scientist
Data/ML platform engineer
Security/privacy engineer
Product or engineering manager
Researcher/academic
Other (please specify)
Q20
Dropdown
How many years of professional experience do you have in software or data roles?
0–1 years
2–4 years
5–9 years
10–14 years
15+ years
Q21
Dropdown
What is your primary domain or industry?
Technology
Finance/FinTech
Healthcare/Life sciences
Retail/Consumer
Telecom/Media
Manufacturing/Industrial
Government/Public sector
Education
Other
Q22
Dropdown
What is your organization's approximate size (global headcount)?
1–9
10–49
50–249
250–999
1,000–4,999
5,000–19,999
20,000+
Q23
Dropdown
In which region do you primarily work?
North America
Latin America
Europe
Middle East
Africa
South Asia
East Asia
Southeast Asia
Oceania
Q24
Chat Message
Thank you for participating! Your input helps us understand practical needs and considerations around synthetic data. If you have any questions about this research, please contact [research team email].
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