Q01
Long Text
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Developer Synthetic Data Adoption & Ethics Survey
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.
Sample questions
A preview of what’s in the template. Every question is editable before you launch.
24 questions · ~4 min
Q02
Multiple Choice
In the last 12 months, have you worked with synthetic data?
Q03
Multiple Choice
Which tools or approaches have you used to generate synthetic data? (Select all that apply)
Q04
Long Text
How much demonstrated fidelity and utility do you require before using synthetic data in production?
Q05
Long Text
How likely are you to increase your use of synthetic data in the next 6 months?
Q06
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.
Q07
Multiple Choice
Which best describes your primary role?
Q08
Long Text
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].
Q09
Long Text
<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>
Q10
Multiple Choice
Which use cases for synthetic data are most relevant to you or your team? (Select all that apply)
Q11
Long Text
Approximately what percentage of data in your projects over the last 12 months was synthetic?
Q12
Long Text
<p>How appropriate is synthetic data for <strong>model training and development</strong> in your context?</p>
Q13
Multiple Choice
Which factors most limit your use of synthetic data today? (Select all that apply)
Q14
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.
Q15
Long Text
How many years of professional experience do you have in software or data roles?
Q16
Long Text
<p>How appropriate is synthetic data for <strong>production decision-making</strong> in your context?</p>
Q17
Long Text
Rank the following improvements by how much they would accelerate synthetic data adoption in your organization, from most to least impactful.
Q18
Long Text
What is your primary domain or industry?
Q19
Long Text
<p>How appropriate is synthetic data for <strong>testing and QA</strong> in your context?</p>
Q20
Long Text
What is your organization's approximate size (global headcount)?
Q21
Long Text
<p>How appropriate is synthetic data for <strong>external reporting or compliance submissions</strong> in your context?</p>
Q22
Long Text
In which region do you primarily work?
Q23
Long Text
Rank your top concerns about synthetic data from most to least concerning.
Q24
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.
What’s included
AI follow-ups
Adaptive probes on open-ended answers that pull out detail a static form would miss.
Attention checks
Built-in safeguards against rushed answers and low-quality respondents.
AI-drafted copy
Wording, ordering, and branching written by the AI — tuned to your research goal.
Auto report
Themes, quotes, and a plain-English summary write themselves once responses come in.
Ready to launch?
Open this template in the editor. Every part is yours to change before the first respondent sees it.