Synthetic Data

Synthetic data is artificial data engineered to reproduce the statistical patterns of real data: a model or a set of rules generates new records that behave like the originals without containing any real person's or customer's information.

Generative AIartificial datasynthetic dataset

§ 01DefinitionSynthetic Data

No jargon, no math

In plain words, what is it?

The mechanism starts from the real thing. Take your dataset, profile its patterns and correlations, and train a generator: a statistical model, a GAN or a large language model that learns how values move together, not the individual records. Then sample: the generator produces new records with the same shape, same distribution, same correlations, and zero real rows. Before use, two checks. Utility: a model trained on the synthetic data must perform like one trained on the real data. Privacy: no record must be traceable back to a real person. Fail the first and the data is useless; fail the second and it is unlawful. The boundary: a generator cannot invent what it has never seen. Rare events stay under-represented, and correlations can be copied faithfully while the underlying meaning drifts.

A real example

The scene. An energy retailer wants an external vendor to build a consumption-forecasting model, but meter data is locked by contract and GDPR.Behind the scenes. Statistical profiles from 5 years of meter readings feed a generator, which produces synthetic consumption histories for 40,000 fictional customers, with the same seasonality, weekends and anomalies as the originals, and not a single real reading.The result. The vendor develops and benchmarks the model on the synthetic set: utility tests put the forecasting error within 2% of the internal benchmark, and privacy tests cannot link any record to a real customer.The safety net. The rare event is not invented: if 2023 had 3 anomalous peak hours, the synthetics reproduce that frequency but not a grid blackout the data never saw. Extreme scenarios still require real data, with proper authorisations.

§ 02How it worksFrom real data to artificial data, with checks at the gate

01
Profile. The real dataset's patterns and correlations are mapped.
02
Train. A generator learns those statistical patterns, not the individual records.
03
Sample. The generator produces new artificial records with the same shape.
04
Validate. Utility tests check a model trained on synthetic performs; privacy tests check no record traces back.
05
Ship or retrain. Pass both and the data goes to training or testing; fail and the generator is retrained.

§ 03Glossary3

Terms that live nearby.

GENERATIVE AI

Generative AI

Generative AI is software that produces new content, text, images, audio, code, by sampling patterns learned from vast training data, rather than only classifying or retrieving what already exists.

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FUNDAMENTALS

Dataset

A dataset is the organised collection of examples an AI learns from or is judged on: thousands or millions of rows of text, images or sensor readings, cleaned, labelled and split into training, validation and test parts.

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GENERATIVE AI

GAN

A GAN (Generative Adversarial Network) is a pair of neural networks trained against each other: a generator creates synthetic samples while a discriminator judges them against real data, and each improves by trying to beat the other.

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§ 04

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