In machine learning, in business intelligence, and in strategy discussions, one refrain echoes: there is never enough quality data.
Models underperform because datasets are too small. Surveys stall because the very people you most want to hear from — perhaps the high-net-worth individuals or the C-suite executives, maybe moms with toddlers— rarely have the time or inclination to respond. In recent times even regulations have tightened and access narrowed.
This tension has grown as organizations chase precision while society raises the bar for privacy and compliance. Our systems demand more information than the real world can reasonably supply. And so a provocative alternative has emerged: synthetic data.
Far from being a gimmick, synthetic data is an engineered instrument designed to bridge scarcity and scale. Like a wind tunnel for airplanes, it creates a controlled environment where ideas can be tested before reality can deliver. This is the story of what it is, when it works, when it fails, and how it can reshape even the most data-scarce domains.
When to Use It
Like any sharp tool, synthetic data can either solve problems or cause self-inflicted wounds. The distinction lies in knowing when to use it.
When it works:
- Scaling beyond scarcity. Taking an example response, thirty High-Net Individual (HNIs) responses to a survey out of three hundred contacted isn’t enough to build brand strategy. With synthetic augmentation, those 30 can be expanded into a dataset robust enough to test hypotheses.
- Balancing imbalance. If your respondents are 90% men in the age group 25–45yo and only 10% women, synthetic techniques can restore balance without running another expensive campaign. This is particularly useful when training classifiers, so as to give the ML models enough sample of the low incidence classes for it to optimally learn the patterns.
- Privacy by design. In regulated spaces like healthcare or finance, synthetic data lets teams model behavior without revealing the individual.
- Stress-testing resilience. Want to know how your fraud detection model behaves during an unlikely but catastrophic surge? Synthetic data creates an artificial yet realistic “storm” before it hits.
- Model convergence. In machine learning, small datasets can leave models unstable or unable to converge; synthetic augmentation stabilizes training.
- Rare event simulation. From executive churn to surgical failures, synthetic data can represent scenarios too rare or impractical to capture in reality.
When Not to Use It
Equally important is knowing when synthetic data becomes a crutch.
When it fails:
- Nuance loss. Subtle domains where nuances are individual/instance specific — like how a neurosurgeon weighs risk — can vanish in generation.
- Bias amplification. If the seed data is skewed, synthetic data generation copies that bias tenfold.
- Overconfidence. Flooding a dataset with synthetic rows may give the illusion of statistical strength but collapse under real-world validation.
- Distrust. For executives, the word “synthetic” can still trigger skepticism: “fake,” “fabricated,” “unreliable.”
- Cost-cutting as an excuse. Synthetic data should never be a shortcut to avoid research budgets or cut corners. It is not a free substitute for honest fieldwork.
It should be deployed only when there is genuine paucity of data, structural restrictions, imbalances, or rare events that cannot be captured in a reasonable fashion.
The truth is: synthetic data complements but never replaces reality.
The C-Suite Example: When Thirty Voices Must Speak Like Three Hundred
Imagine a consulting team tasked with understanding how senior executives evaluate five competing technology brands. Out of 300 invitations, only 30 C-suite leaders respond. The result: a dataset too thin to slice by cohort, brand, and attribute.
Each respondent rates five brands across fifteen attributes, producing 150 total “response units.” But executives differ wildly; one may emphasize innovation, another reliability, another cost. If decisions must be made from 30 voices, risk is high: a handful of outliers could sway the outcomes and strategy.
Enter synthetic augmentation. By conditioning on demographics (role, age, industry) and brand identity, deep learning methods such as CTGAN or statistical methods Gaussian Copula Models can generate new response units for tabular data. These synthetic executives don’t exist, but they mimic the correlations and trade-offs observed in the seed data set.
The result is not a fantasy dataset of a thousand CEOs — it is a richer simulation, enough to explore trends, test scenarios, and guide questions back to the real market. The original 30 remain the foundation; the synthetic expansion is the scaffolding that lets insights take shape.
Scarcity Is No Longer an Excuse
Synthetic data won’t replace reality — but it removes scarcity as an excuse. It gives us wind tunnels for ideas, simulations for strategies, and a bridge from today’s thin datasets to tomorrow’s stronger decisions.
At That Fig Tree with Fig Labs, we continuously push the boundaries of traditional brand and consumer research by infusing the latest world class statistical methods and machine learning thinking. We leverage deep learning techniques such CTGANs and statistical tecniques like copulas to help clients generate synthetic data and to do more with less and not walk away due to paucity of data.
