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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.

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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?
  • In-house generation scripts
  • Open-source libraries (e.g., SDV, SynthCity, ydata-synthetic)
  • Vendor platform
  • Data augmentation utilities not aimed at privacy
  • 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?
RowsNever appropriateRarelySometimesOftenAlways 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)
  1. Privacy leakage or re-identification
  2. Bias amplification or fairness issues
  3. Poor realism or utility
  4. Regulatory or compliance risk
  5. Lack of transparency/traceability
  6. 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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