Synthetic data is data created by a computer program rather than collected from real events or people, designed to reproduce the statistical patterns and structure of a real dataset. It is used to train and test AI models, test software, share data with partners and run analytics where using real records would be risky, slow to approve or impossible. Its value depends on how faithfully it reflects reality and how well it avoids reproducing real individuals’ data.
At a glance
- Synthetic data is generated, not copied, but it is usually modeled on a real dataset.
- Common uses are machine learning (ML) training, software and system testing, demos and data sharing.
- It can reduce, but does not by itself eliminate, privacy risk; poor generation can leak real records.
- It inherits bias and gaps from the source data unless deliberately corrected.
- Quality checks compare it with real data for accuracy and test it for privacy leakage.
What problem it solves
Organizations often need data they cannot easily use. Customer records may contain personally identifiable information (PII) that privacy rules and policy keep out of test systems, developer laptops and vendor hands. Real examples of rare events, such as fraud, equipment failures or unusual support cases, may be too scarce to train a model. And data needed for a new product may not exist yet.
Synthetic data offers a substitute. Teams can build and test systems on realistic data without copying production records into less-protected environments, share datasets with partners with less exposure, and add rare cases to training sets.
How it works
Generation methods fall roughly into three groups:
- Rule-based. Values are created from rules and templates, such as fake names, addresses and account numbers in the right formats. Useful for software testing, but the data rarely reflects real relationships.
- Statistical. A model learns the distributions and correlations in a real dataset and samples new records that follow them.
- Generative AI. Generative AI models trained on real data produce new records, text, images or other content that resembles the original.
Good practice then checks two things. Fidelity: does the synthetic data behave like the real data for the intended use, for example do models trained on it perform similarly on real data? Privacy: does any synthetic record match or reveal a real person, especially outliers that are easy to recognize? Some tools add techniques such as differential privacy to limit leakage, at some cost to accuracy.
Because synthetic data is often derived from sensitive data, the source data still needs data classification and access controls, and the generation process belongs under data governance.
When it matters for buyers
- When building or testing AI. Synthetic data can fill gaps and speed approval of training data. See our artificial intelligence overview for help evaluating AI projects and providers.
- When cleaning up test environments. Replacing copies of production data in development and test systems can reduce exposure.
- When sharing data externally. Vendors, researchers or developers may be able to work with synthetic data instead of real records.
- When privacy law applies. Under rules such as the GDPR, whether data counts as anonymous depends on the re-identification risk, so the status of synthetic data varies. Check with counsel.
Questions to ask vendors
- What generation method do you use, and what types of data (tables, text, images, time series) do you support?
- How do you measure fidelity, and can we test results against our real data?
- How do you test for privacy leakage, including rare or outlier records?
- Do you offer privacy techniques such as differential privacy, and what accuracy trade-off do they bring?
- Where is our source data processed, and is it retained after generation?
- What documentation do you provide to support our privacy and compliance reviews?
How it differs from anonymized and masked data
Anonymized and masked data start from real records and alter them: removing names, replacing identifiers, shuffling values or blurring details. Each row still corresponds to a real person or event, so the risk is that someone can link it back, especially by combining it with other data. Synthetic data is newly generated, so rows are not meant to correspond to real people; instead, the risk is that a generator copies or closely reproduces real records. Masked data usually keeps real-world relationships intact, which helps testing; synthetic data can be more flexible and easier to share but may lose some real-world detail. Many organizations use both, depending on the use case.