Synthetic Data

What is synthetic data?

Synthetic data is created by a program rather than collected from real events. It can be tabular (customer records, transactions), text (support conversations, emails), images or time series. The aim is for it to look and behave like the real thing closely enough to be useful for a given purpose, while containing no actual customer, patient or employee.

There are a few ways to make it. Rule-based generators fill fields with realistic values that respect formats and relationships, for example a postal code that matches its city. Statistical and machine learning methods learn the patterns in a real dataset and sample new records with similar distributions. Generative AI models can produce realistic text, images or conversations from instructions.

Businesses use synthetic data to populate test and demo environments, build training and evaluation sets for AI, simulate rare scenarios such as fraud, and share data with vendors or analysts without exposing personal information.

What is synthetic data used for?

The common uses fall into a few groups:

• Software testing: realistic test databases without copying production data into less secure environments.
• AI training and evaluation: more examples of rare classes, or test cases for AI evals before real usage exists.
• Privacy-preserving sharing: datasets for partners, analysts or proofs of concept that do not reveal individuals.
• Scenario simulation: stress-testing forecasts, pricing or fraud rules against situations that have not happened yet.
• Demos and training: product demos and onboarding environments that look real.

In each case the question is the same: does the data need to be real, or only realistic enough for this job? If realistic is enough, synthetic data is often the safer choice.

How is synthetic data different from anonymized data?

Data anonymization starts from real records and removes or alters identifying details. Synthetic data generates new records that never belonged to anyone. That difference matters for privacy: anonymized data can sometimes be re-identified by combining the fields that remain, while well-made synthetic data has no one-to-one link back to a person.

The words well-made carry weight here. A generator trained on real data can memorize and reproduce unusual records, especially outliers, so synthetic data is not automatically private. Teams still need to test for leakage and treat the generator and its training data under the same rules as the original data. Data masking is a third option, replacing sensitive values in place, and is often used for test environments where the real structure must be kept exactly.

What are the limits of synthetic data?

Synthetic data is only as good as the process that made it. If the generator misses real correlations, such as how order size varies by season or region, models and analyses built on it will learn the wrong patterns. It also tends to underrepresent the messy reality of production data: typos, missing fields, duplicates and edge cases that real systems must handle.

Bias carries over too. A generator trained on skewed data reproduces the skew, and can amplify it. For decisions about real customers or money, synthetic data should support real data rather than replace it. A sensible rule is to train or test with synthetic data where it helps, then validate final results on real, properly governed data before anything goes live.

How do you evaluate the quality of synthetic data?

Judge it against its purpose. Three checks cover most cases:

• Fidelity: do distributions, correlations and constraints match the real data closely enough? Compare summary statistics and relationships between fields.
• Utility: does a model or analysis built on synthetic data perform similarly to one built on real data when both are tested on real records?
• Privacy: can any synthetic record be traced back to a real one? Look for near-duplicates of real rows and test how easily rare records can be inferred.

Document how the data was generated, from what source and for which uses, so others do not apply it to purposes it was never checked for. A dataset can pass one check and fail another, so test all three.

Where does synthetic data fit in Vizio AI's work?

Our DX Studio works on data platforms and architecture, AI-powered internal workflows, and forecasting and scoring models, and synthetic data is a tool we use in that work when real data is scarce, sensitive or not yet available. Typical uses are populating test environments for integrations so production records stay where they belong, and building test cases for classification or document processing before enough real examples exist.

We validate anything built on synthetic data against real, governed data before it supports a decision. The practical starting point is reviewing what data you have, where it lives and which rules apply to it.