Weighing the Data Types
While there are advantages and disadvantages to both data tactics, each company must weigh what’s needed for their particular research situation. Key differences are evident in terms of sourcing, privacy and compliance, accuracy and representation, scalability and availability, and the various types of use cases.
In essence, the choice between synthetic and natural data depends on the specific goals and constraints of your project. While natural data offers authenticity, synthetic data provides valuable advantages in terms of privacy, scalability, and the ability to explore scenarios that are difficult or costly to replicate in the real world.
According to AIMultiple Research’s article, “Synthetic Data vs Real Data: Benefits, Challenges in 2025,” here is a brief comparison between synthetic data vs. real data:
| Aspect | Synthetic Data | Real Data |
| Definition | Data generated to simulate real-world data patterns. | Actual data collected from real-world sources. |
| Source | Created through algorithms, simulations, or pattern generation. | Collected directly from user interactions, transactions, or events. |
| Data Privacy | High, as it contains no actual sensitive information. | Can contain sensitive, identifiable information. |
| Accuracy | May approximate real data patterns but lacks true authenticity. | High, as it represents real events and interactions. |
| Risk of Re-identification | Low, as it doesn’t include real user information. | High, depending on the presence of personally identifiable information (PII). |
| Data Utility | Effective for testing, model training, and simulations. | Essential for production and insights requiring actual trends. |
| Cost of Acquisition | Generally lower, as it can be generated programmatically. | Higher, often requiring data collection efforts and compliance. |
| Bias and Representativeness | Potentially less biased if designed well, but depends on input patterns. | May contain biases reflective of actual populations and collection methods. |
| Scalability | Easily scalable, as it can be generated in large quantities. | Limited by the availability and accessibility of real data. |
| Use Cases | Ideal for prototyping, algorithm testing, and privacy-preserving analytics. | Essential for production, regulatory reporting, and detailed insights. |
Natural Versus Synthetic Data
Still weighing the benefits and drawbacks of natural versus synthetic data? TMRE 2025 will hold the session, “Don’t Bet on the Wrong Data: Coca-Cola & Delineate on Natural vs Synthetic in Measurement.” It will be presented by James (JT) Turner, Founder & CEO at Delineate, and Gabe Gales, Director, Global End-to-End Communications Effectiveness at The Coca-Cola Company.
As AI and synthetic data take center stage in marketing analytics, it’s getting harder to separate innovation from illusion. In this session, Coca-Cola and Delineate dig into the real differences between natural and synthetic data—what each is good for, where they fall short, and how to use both wisely. Through real-world examples and honest insights, we’ll show how combining speed with substance leads to smarter, more confident measurement.
Key Takeaways: Explore the pros and cons of natural vs synthetic data in modern measurement; learn how to balance the use of natural and synthetic data to drive speed, accuracy, and confidence; and discover practical guardrails to ensure synthetic data strengthens, rather than skews, decision-making.
The Synthetic Data Landscape
During this year’s TMRE @ Home virtual event, Joshua Hawthorne, Research Operations Leader, LinkedIn, presented the session, “Beyond Real: Harnessing Synthetic Market Research Data to Accelerate Business Insights.” Hawthorne navigated the landscape of synthetic market research data, elucidating its creation, emphasizing rigorous validation, and advocating for its strategic role in augmenting traditional research to unlock faster and broader business insights. Takeaways included:
- Demand Transparency in Creation: When exploring synthetic data solutions, delve into the methodologies employed – from basic prompting to sophisticated fine-tuning and post-processing – to ensure alignment with your research objectives and desired accuracy.
- Scrutinize Validation Rigor: Prioritize vendors who can clearly articulate and demonstrate robust validation processes (A/B testing, statistical comparisons, backtesting), providing the necessary confidence in the reliability and applicability of the synthetic data.
- Strategically Extend Your Reach: Recognize synthetic data as a powerful tool to complement traditional research, enabling insights into areas previously inaccessible due to time, budget, or logistical constraints, rather than a complete replacement for human-derived understanding.
Integrating Synthetic & Real Data
Clearly, there’s a lot of debate revolving around synthetic data and natural data in the market research community. There are pros and cons to each method. Many questions remain centered around ethics, expense, and other factors. Just what guardrails and safeguards are in place in terms of data governance, compliance, privacy and security? The question of bias, too, remains a concern in the usage of real-world data, as well as the data being supplied to machine learning systems. There are tradeoffs, limitations and complexities to both. Perhaps, it is the integration of the two that can balance the equation.
So where to use what? In TechTarget’s “Synthetic data vs. real data for predictive analytics,” data strategist Donald Farmer of TreeHive Strategy offers a starting point with a list of potential scenarios:
| Scenario | Synthetic data | Real data | Notes |
| Rare events/edge cases | Preferred: Generate thousands of edge cases quickly | Limited: Might take years to collect sufficient samples | Use synthetic to augment. Validate it on real samples when available. |
| Privacy-sensitive applications | Preferred: Regulatory compliance, data minimization | High risk: Personal information exposure, regulatory constraints | Document the synthetic generation process for audit trails. |
| System/pipeline testing | Preferred: Controlled, repeatable test scenarios | Risky: Might expose production data in test environments | Synthetic provides safe testing without production data access. |
| Model training (Initial) | Good: Rapid iteration, perfect labeling | Essential: Ground truth, real distributions | Start with real data understanding, augment with synthetic. |
| Model validation (Final) | Insufficient: Might miss real-world complexity | Required: The only way to verify actual performance | Never deploy without real data validation. |
| Dashboard prototyping | Preferred: No production access needed | Access constraints: Might delay development | Use synthetic for design, switch to real for go-live. |
| Regulatory submissions | Context-dependent: Thoroughly document your methodology | Preferred: Higher regulatory confidence | Hybrid approaches are often the strongest for compliance. |
In terms of integration, Farmer notes, “An effective method for using synthetic and real data together is through an iterative process. Begin with a reduced set of real data to generate synthetic records and train initial models. Then, validate those models on real data and refine the synthetic generation using improved results. This takes advantage of the strengths of both data types while mitigating their weaknesses.”
As the use of AI continues to expand, synthetic data is becoming more of a strategic asset that can be used thoughtfully and appropriately, depending on such factors as time, budget and privacy.
Farmer notes, “Trade-offs remain. Synthetic data is not a perfect approach, but it has often been dismissed due to concerns around authenticity. Synthetic data is ultimately a tool. And like any tool, it’s about using it for the right job at the right time. The future of data in predictive analytics isn’t synthetic or real; it’s synthetic and real, working together intelligently.”
Video: “Beyond Real: Harnessing Synthetic Market Research Data to Accelerate Business Insights,” courtesy of TMRE @ Home 2025.
