Synthetic Data, Real Quality: Maintaining Research Standards in a New Era

by Alex Mangoff

Synthetic data has quickly become a focal point in research conversations. However, the term is often used broadly, covering methods that operate quite differently in practice—digital twins, persona bots, and probabilistic models among them. While all could be called “synthetic,” there are key differences in how each is developed:

• Digital twins are AI-generated replicas of real people and powered by large language models (LLMs) to simulate the individuals’ thoughts or reactions.
• Persona bots are LLM-based AI agents designed around a specific segment that act as stand-ins for a “type” of user to simulate likely reactions, needs, or behaviors.
• Generative data models are structured analytical models built from respondent-level data that use defined assumptions, rules, and data points to predict what responses would be to a given set of questions or inputs.

Generative data models are the most effective and efficient option of the three, for data simulation purposes, as they can be customized to specific needs and leverage highly relevant, real data. They have also become much more powerful and accessible with the acceleration of GPUs; what used to take six months now takes five minutes.

Still, all three methods share a common characteristic: they simulate information. Simulations aren’t new and shouldn’t scare us. Researchers have simulated data for decades (think weighting); we can just do the math a lot better now.

Let’s dig into that last part by exploring how key principles of research quality can help us not only use synthetic data responsibly, but also make the most of it.

Synthetic Data as an Extension of Reality

At their best, synthetic methods don’t invent reality – they build on it. They don’t turn an out-of-focus picture of a dog into a cat; they turn it into a clearer picture of the same dog.

A synthetic data approach must begin with real data: primary research, transactional data, verified third-party information. Then, powerful models allow us to leverage those inputs to create more complete information. Done well, synthetic data doesn’t replace people, it simply amplifies what we learn from them.

Like weighting, imputation, and calibration, high-quality synthetic data uses disciplined math to reduce noise and fill gaps responsibly. It’s an evolution, one that requires us to ensure quality doesn’t get lost in the pursuit of progress.

The quality angle is one reason probabilistic models are the best-in-class option for synthetic data. They are built from real data, but are also more transparent, defensible, and flexible than digital twins or persona bots due to the fact they do not leverage LLMs.

However, even when using a best-in-class approach, high quality outcomes don’t occur organically. Let’s explore how we make it happen.

Expanding the Definition of Quality

Quality in research tends to focus on well-known risks such as respondent authenticity and engagement, measurement error, and non-response bias. Synthetic data models require us to account for an additional dimension of risk: model risk.

Model risk shows up when:

• the underlying real data has blind spots (e.g., underrepresented segments or context)
• the model over-smooths true differences, causing everything to look “average”
• the model learns shortcuts (leakage) that don’t hold in the real world,
• the world changes (drift), while the model keeps confidently describing yesterday

Synthetic outputs can look impressively clean, even when they’re wrong. That’s why quality must be baked in from beginning to end – shaping how we build, validate, and govern the synthetic data workflow.

Quality Fundamentals Still Apply

Synthetic data hasn’t changed the research rulebook. It’s evaluated against the same standards that have always separated “interesting output” from “reliable decision support.”
In practical terms, this means synthetic data must still answer:

• Fit-for-purpose: Is the use of synthetic data appropriate for this decision, this timeline, and this risk tolerance?
• Transparency: Can we explain what was done, why it was done, and what tradeoffs we accepted?
• Fundamentals: Is the underlying research design sound, and is the source data credible?

This is where human expertise becomes essential. It’s our job to determine whether synthetic data is the right tool for the job, make sure we’re using it appropriately, and assess the quality of resulting output.

Applying Research Fundamentals to Synthetic Data

Moving from “model output” to “decision-ready evidence” requires attention to several foundational elements:

Data Quality: What’s our foundation?

It’s worth repeating: synthetic data should begin with real people and real data, not an LLM. If the inputs are incomplete or unrepresentative, the outputs will inherit those limitations.
We need to clearly understand our data: source(s) used, volume and representativeness, transformations and why they happened, and limitations.

Measurement Validity: Are we modeling what we think we are?

Poorly defined inputs lead to poorly defined synthetic data; it’s the classic “garbage in, garbage out” problem.

We need clarity regarding what our foundational data should measure, confidence that it measures what we expect, and a clear view of the relationships we expect to hold.

Model Confidence: Did we check our work?

In research, we must always understand and account for uncertainty: sampling error, confidence intervals, respondent bias. Quality synthetic data requires similar accounting through checks such as holdouts, back-testing, sensitivity analysis, and comparisons to known benchmarks.

The goal isn’t to make synthetic data look perfect. The goal is to understand when it’s reliable enough to use to make decisions, and when it isn’t.

Process and Governance: How do we maintain discipline?

Finally, synthetic data needs guardrails: documentation, repeatable pipelines, review checkpoints, and decision rules for when it is appropriate and how to use it.

A structured, organized workflow ensures that results are not only plausible, but also explainable. These guardrails can help prevent results that are polished and persuasive at look, but ultimately unusable because no one can explain or defend how they were made.

Synthetic data? Yes, but with a Measured Approach

Synthetic data has a clear role in modern research. However, its value depends on how it is applied. It’s most effective when it is deemed appropriate for the research need, anchored by high-quality (real) data, developed with discipline, and governed with transparency.

You can’t model your way out of a measurement problem. If there are upstream issues with the quality of the starting data or flaws in the research design, synthetic data will paper over the cracks rather than correct them.

Ultimately, synthetic data represents an exciting evolution in market research. By applying established research standards, we can ensure it informs business decisions—rather than adding complexity without improving clarity.

To learn more about incorportating synthetic data into your insights practice, contact us at info@burke.com.

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Source: Feature Image – ©Anela Ramba/peopleimages.com – stock.adobe.com