Synthetic Data

Also known as: Synthetic Dataset, Augmented Data, Privacy-Safe Data

Algorithmically generated data that statistically mimics distributions and patterns of real data without exposing personal information.

Synthetic Data is artificially generated information that replicates the statistical properties of real data: distributions, correlations, trends, and variability. It is created using techniques such as GANs (Generative Adversarial Networks), VAEs, diffusion models, or LLMs.

In market research, synthetic data is used to: (1) augment small samples, (2) protect the privacy of real respondents (especially under GDPR, CCPA, or Mexico's LFPDPPP), (3) train and test analytical models without exposing sensitive data, and (4) accelerate insight generation when fieldwork is costly or slow.

A critical limitation is that synthetic data inherits biases from the training data and may not capture rare events or emerging market behaviors. Validation against real data is therefore essential before making business decisions based on synthetic datasets.

Atlantia monitors and applies these techniques as part of its AI research acceleration stack.