Synthetic data is reshaping how organizations think about consumer insights data and market research. At Dynata, we view synthetic data not as a replacement for real-world insights, but as a powerful complement—when designed, validated, and deployed with precision.
What is synthetic data?
Synthetic data are products created using AI, each type design using the best combination of techniques working together. It draws on machine learning, generative modeling, and statistical methods to create new records that preserve the patterns, relationships, and constraints of real data.
Each synthetic dataset is an AI product, crafted with a specific business or research purpose in mind. Dynata approaches synthetic data as a tool for targeted innovation, not generalized simulation.
Synthetic data is only as good as the foundation it’s built on—and Dynata’s foundation is unmatched. Over our 40+ year history, we have collected a wealth of accurate, permissioned, signal-rich datasets that embed the structural patterns that AI models need to produce reliable results. Our proven pipelines curate, organize, and prepare this data before synthetic generation, reducing the risk of distortion or hallucination.
How do you evaluate the quality and value of synthetic data?
The true value of synthetic data lies not only in statistical fidelity and coherence, but in its ability to support economic decisions within operational tolerances. Because synthetic data is AI-generated, quality depends on rigorous acceptance standards.
Dynata applies systematic validation across three dimensions:
- Statistical and semantic match against real data
- Privacy safety with no leakage or re-identification
- Economic value assessment to confirm datasets support sound decisions
We are developing quantitative standards and systems that define when synthetic data is fit for purpose, creating a trust framework that clients can adopt with confidence.
What comprises Dynata’s AI data structure
Dynata’s synthesis technology stack integrates multiple analytical and generative methods. Classical statistical models provide high interpretability and reliable replication when assumptions are valid, making them valuable for imputation and hierarchical modeling. Machine learning methods expand scalability by managing heterogeneity and missing data while supporting segmentation and bias detections. LLMs, both inferential and generative, enable flexible, high-fidelity modeling of language, sentiment, and behavior.
Structural LLMs, a class of LLM enabled by our research grade structural data, advance this capability by capturing nonlinear relationships, and modeling complex dependencies across variables, creating a foundation for AI-native survey synthesis.
This focus ensures synthetic data is not “innovation theater,” but a practical tool for improving decision-making. Our philosophy: start broad, validate rigorously, and invest where impact is greatest.
When is synthetic data applicable?
Synthetic data is not one-size-fits-all. Dynata deploys it selectively, guided by purpose and validated impact. After evaluating almost 60 potential use cases, we are focusing on those where synthetic data delivers the most immediate, measurable, and reliable value:
- Survey feasibility forecasting: Simulating outcomes to estimate reach, incidence, and completion likelihood before launch.
- Pricing and concept optimization: Extending and testing market scenarios to accelerate time-to-market and reduce cost.
- Persona and segmentation modeling: Enriching audience definitions and filling gaps in underrepresented profiles.
- Brand tracking and trend analysis: Stress-testing brand performance measures over time, even when direct response data is sparse.
- Boosting underrepresented groups: Expanding participation of niche or hard-to-reach cohorts while preserving privacy.
- Filling gaps in responses: Using synthetic completion to address skipped survey questions and maintain dataset integrity.
Dynata combines structural insights from historical data with the strength of signal-rich datasets to maximize downstream impact.
Our position emphasizes both technical rigor and commercial usability—ensuring clients receive synthetic data that is trustworthy, actionable, and valuable. At Dynata, we bring discipline, transparency, and purpose to synthetic data—so our clients can innovate with confidence, and act with clarity.
If you’d like to learn more, click on this link to view a recent presentation.