Synthetic Methods: Better Data, Smarter Spend.

by Eli Moore

Can synthetic data be the key to better data and a smarter allocation of your budget? Without a doubt.

There has been a lot of talk of synthetic data lately. But say “synthetic,” and people hear very different things:

“Fake data.” A literal reading of “synthetic” from the Thesaurus without context.
“Magic robots that can stand in for my consumer.” A promise that sounds charming… and suspicious.
“A way to reach hard-to-reach audiences.” A practical, but uninspiring hope born of shrinking response rates.

Why the confusion? Some folks translate “synthetic” into “not authentic” and stop there. Some are selling products that are inexpensive to produce, but lack the understanding that quality is the difference between a right and wrong answer. And still others are genuinely trying to solve your sample problem, but without the scale or discipline to make it reliable or truly groundbreaking.

So, let’s clear the fog.

A Working Definition (Without the Hype)

Synthetic data doesn’t invent reality; it clarifies what we see in the data.
It starts with what we already observe (survey responses, behavioral signals, verified third-party data) and uses statistical modeling to create a clearer, more continuous picture of what we care about.

It’s not a replacement for people; it’s an amplifier of what we learn from them. The concept isn’t new. When you weight sample or impute missing values, you’re already using basic forms of synthetic data. What’s new is the level of sophistication. Modern methods and modern computing power let us build more complete, more representative views of a population than ever before – capturing the full story, not just scattered snapshots.

An Analogy and Product: From Blurry Photo to Sharper Image

Imagine a slightly blurry photo of a Labrador. That’s your survey data. It’s a real, authentic photograph made with a disposable camera – sorry Gen Z, ask ChatGPT. A faithful picture, just not perfectly crisp. Synthetic methods are like sharpening that photo. It doesn’t turn into a poodle or a horse. It’s the same Labrador—just clearer edges. We’re not making up a different dog; we’re reducing noise and refining our view of the dog we already see.

^{Source: Image Generated by Eli Moore using Argil AI, 10/10/2025.}

Now add time. You don’t have just one snapshot; you have many. The dog stands up, jogs across the frame, a ball comes into view. Synthetic methods let us interpolate between those frames—to connect the dots in a statistically disciplined way—so you can watch a smooth video rather than flip through choppy stills.

^{Source: Image Generated by Eli Moore using Argil AI, 10/10/2025.}

And then there’s redundancy. Out in the field (literally and figuratively), most frames look the same. The grass doesn’t change. Do we need to keep taking pictures of the grass? Synthetic approaches focus you on what’s moving, and compress what isn’t—retaining fidelity where it matters and saving effort where it doesn’t.

^{Source: Image Generated by Eli Moore using Argil AI, 10/10/2025.}

How This Translates to Survey Work

Why run a survey at all? Because we want to learn something we can’t learn any other way. We can’t know it’s a picture of a dog until we take our first picture. We can’t know a red ball will come into view unless we collect regularly. We can’t be confident the grass doesn’t change colors unless we sometimes observe it changing colors. But once we start collecting, synthetic methods help us ask, and answer, questions better:

Do we need to talk to everyone as often?
If some groups barely change wave to wave, we can sample them less frequently without losing accuracy.
Do we need to ask every question to every person?
If people who answer “A” on Q1 almost always answer “A” on Q2, we can infer Q2 for some respondents and shorten the survey tool.
Can we reduce “blurriness” with outside signal?
Beyond demographics, validated external data can anchor and calibrate results, improving precision and understanding of what we seek to understand.
What about “camera angle changes” (trend breaks)?
When the instrument, mode, or panel shifts, synthetic techniques can reconcile those changes so your trend stays a trend—not a step function.

Put simply, we can collect more efficiently, infer more intelligently, and report more consistently—without pretending we know what we never measured.

I Want to Be Very Clear On What Synthetic Data Is Not

Not fake: It should be grounded in measured data and disciplined modeling – not gross generalizations from the internet.
Not clairvoyant: It can’t read consumers’ minds; it should reflect the quality and coverage of inputs.
Not a substitute for human voice: It should amplify what people tell us by filling in gaps responsibly, using the latest in data science and AI.

The Payoff (Yes, You Can Take This to the Bank)

It enables us to do what was once impossible – optimize data collection while raising data quality. The result: stronger insights, faster decisions, and greater value creation.

Eli is Vice President, Data Strategy at Burke, where he leads the development of scalable, data-driven solutions that help clients strengthen their brands, enhance customer experiences, and uncover opportunities for innovation and growth. With nearly 20 years of experience in analytics and insights, Eli brings deep technical expertise in AI and advanced analytics, paired with a sharp focus on driving measurable business outcomes.

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