Last reviewed: October 6, 2026. A team needs realistic customer records to test a new app. It cannot put actual customers’ purchases, names or addresses into a development database. Synthetic data offers one possible answer: create new records that reproduce useful patterns without simply copying the original rows. The catch is that “synthetic” does not automatically mean private, accurate or fit for every task.
This guide explains what synthetic data is, how teams produce it, where it helps, and the checks to run before relying on it. If you are new to the broader field, start with our generative AI guide.
What Is Synthetic Data?
Synthetic data is artificially created data designed to resemble the structure or behavior of another dataset or real-world process. It can be made with hand-written rules, simulation software, statistical models or generative AI. The output may be a table of transactions, a collection of images, sensor readings, conversations or a simulated environment.
The goal is not to recreate an exact person or event. It is to retain the properties needed for a specific use: valid field formats, plausible relationships, uncommon cases, or patterns a model must learn. A synthetic dataset can be useful for one purpose and misleading for another.
Newly generated records can still reveal information about people in the source data. Treat synthetic data as a candidate dataset that needs utility and privacy testing—not as an automatic replacement for consent, security controls or review.
How Is Synthetic Data Created?
The process begins with a target. A test database may only need realistic formats and boundary cases. A machine-learning project may need correlations and performance that carry over to real data. A privacy-sensitive public release has a much higher bar. Choosing the method before the goal often produces a dataset that looks convincing but fails its actual job.
Four common approaches
| Approach | How it works | Best fit | Main caution |
|---|---|---|---|
| Rule-based data | Generate records from explicit formats and constraints | Software tests, demos and known edge cases | May miss relationships and real-world variation |
| Simulation | Model a process such as traffic, machines or robots | Rare events and controlled experiments | A flawed simulation transfers its assumptions |
| Model-generated data | Train a generator on examples, then sample new records | Preserving complex patterns for analysis or training | May memorize examples or distort rare groups |
| Partially synthetic data | Replace selected fields in a real dataset | Limited sharing or testing workflows | Untouched fields can still identify people |
These methods can be combined. A retailer might use business rules to enforce valid product categories, a model to approximate basket sizes, and manual checks for refunds or unusually large orders. The best method depends on which relationships matter.
A Simple Synthetic Data Example
Imagine a small online shop with four fields: purchase date, sales channel, basket value and refund flag. The real table also contains customer identities, which are unnecessary for a checkout test. A rule-based generator could create thousands of fictional orders using a valid date range, known channels and sensible value limits. It could deliberately add refunds, empty baskets and unusually high totals to test how the app behaves.
For a sales forecast, that simple approach may be inadequate. The team might need realistic weekly seasonality, the relationship between channel and basket value, and the frequency of refunds. A model trained on real records could capture these patterns—but its output would require stronger privacy review. The same dataset cannot be judged “good” without specifying the task.
Could a developer, analyst or model complete the intended task using the synthetic records—and would that result still hold when checked against real data?
Where Synthetic Data Is Useful
Software development and testing
Teams can test forms, dashboards and database migrations with made-up records instead of copying production data. Synthetic records are particularly useful for unusual conditions such as missing fields, very long names, invalid timestamps and rare payment states. They also make repeatable test scenarios easier to create.
Machine-learning training
Generated examples can supplement limited training data or expose a model to controlled variations. In image recognition, for instance, synthetic scenes can vary lighting and camera angle. In tabular prediction, they can help test a pipeline before access to sensitive data is granted. This does not guarantee improved real-world accuracy: validation must use appropriate held-out real examples.
Simulation and physical AI
Robots and autonomous systems may rehearse events in simulation that are dangerous, expensive or uncommon to capture. The challenge is the “reality gap”: success in a simulated setting may not transfer to a physical one. Our physical AI guide explains why real-world feedback still matters.
Sharing and collaboration
An organization can use synthetic samples to explain a data schema, demonstrate an analysis or let partners develop code before receiving controlled access to real data. However, a public release can create different risks from an internal test. Access decisions should follow the results of privacy assessment, not the word “synthetic” in a file name.
How to Test Whether Synthetic Data Is Useful
Visual similarity is a weak test. A few convincing rows do not tell you whether the dataset works for the job. A useful evaluation compares synthetic records with a held-out real dataset that the generator did not see. The test should reflect the intended task.
- Validate the schema: Check types, missing values, allowed categories, uniqueness and business rules.
- Compare distributions: Review ranges, averages, tails and rare values, not just typical examples.
- Compare relationships: Test correlations and dependencies between fields, such as age and income or channel and basket value.
- Run the actual task: Train, test or prototype on synthetic data, then evaluate on appropriate real-world data.
- Check groups and edge cases: A high overall score can hide poor performance for small groups or unusual events.
For analysis, ask whether the conclusions change: does the synthetic sample preserve the direction and approximate size of important relationships? For an app test, ask whether the dataset triggers the states the software must handle. A single “fidelity score” cannot replace those questions.
NIST’s SDNist Synthetic Data Report Tool illustrates this multi-part approach by reporting utility and privacy measures rather than treating realism as one number. Its metrics are aids to evaluation; teams still need domain knowledge and a clearly defined use.
Is Synthetic Data Private?
Sometimes it can reduce exposure to real records, but privacy is a separate claim that must be tested. A generator trained too closely on a small or sensitive dataset can reproduce a rare combination of facts or even near-duplicate records. Removing direct identifiers is not enough if other fields can be linked to outside information.
NIST distinguishes ordinary synthetic-data generation from methods that apply formal privacy protections. Differentially private synthetic data can provide a defined privacy guarantee when implemented correctly, but this usually trades away some detail or utility. A file produced by an AI model is not differentially private just because it is synthetic.
Privacy checks worth running
- Exact and near-match checks: Look for copied or unusually similar source records, especially rare ones.
- Membership inference: Test whether an attacker could infer that a particular person’s record was in the training data.
- Linkage risk: Consider what outside data could connect a synthetic record to a real person.
- Small-group exposure: Inspect rare combinations and outliers that may be identifiable even without names.
- Release review: Document who can access the dataset, what they can do with it and how long it will be retained.
NIST’s guidance on de-identifying government datasets is also relevant: removing visible identifiers does not erase all re-identification risk. Privacy review should account for the data source, the generation method, the audience and the consequences of exposure.
Synthetic Data vs. Anonymized Data vs. Dummy Data
| Term | What it means | Typical strength | Typical limitation |
|---|---|---|---|
| Synthetic data | New records generated to imitate useful properties | Can support realistic tests or analysis | May leak information or distort patterns |
| Anonymized or de-identified data | Real records altered to reduce identifiability | Keeps some original relationships | Residual re-identification risk varies |
| Dummy data | Invented placeholders with little concern for realism | Fast and easy for simple demos | Often poor for realistic testing |
The categories overlap. A fictional customer list can be both dummy and synthetic. A model trained on de-identified source records may still produce privacy risks. The label matters less than how the dataset was created, evaluated and controlled.
A Practical Workflow for Teams
- Define the decision: Write down the exact product test, analysis or model task the data must support.
- Map sensitive fields: Identify personal, confidential and regulated information in the source.
- Choose a method: Use simple rules when they are enough; use a learned generator only when preserving richer patterns is necessary.
- Keep a holdout: Reserve real examples for evaluation rather than letting the generator see everything.
- Measure utility: Check schema, distributions, relationships, edge cases and task performance.
- Measure privacy: Run appropriate duplication, inference and linkage tests; seek specialist review for consequential releases.
- Document limitations: Record intended uses, excluded uses, known distortions and access controls.
This workflow echoes a wider principle in AI systems: the quality of the inputs and checks often matters more than the sophistication of the generator. Our context engineering guide covers a similar idea for AI applications that depend on selected information.
Common Mistakes to Avoid
Assuming “synthetic” means safe
The largest mistake is treating generated data as anonymous by default. A privacy claim requires evidence tied to the source dataset, method and release plan. If the generator memorizes examples, the output can carry sensitive details.
Optimizing for appearance alone
Real-looking samples may fail business rules, underrepresent minority groups or smooth away rare events. Evaluate on the task, not just whether a chart looks plausible. If an AI-generated dataset supports a public claim, verify the claim with independent evidence, just as you would check the outputs discussed in our AI hallucinations guide.
Training only on generated material
Repeatedly training on model-generated outputs can amplify blind spots and errors. Keep a clear connection to trusted real-world measurements. Synthetic examples can expand coverage, but they should not quietly replace the evidence needed to judge performance.
Ignoring rare cases
A generator trained on typical examples often misses the very events a tester needs: unusual refunds, uncommon diagnoses, infrequent failures or atypical environments. Deliberately design and verify those cases rather than assuming the model will invent them.
Frequently Asked Questions
Is synthetic data fake data?
It is artificially created, but “fake” can be misleading. Good synthetic data is designed to preserve particular useful patterns. It is not an exact record of a real event, and its value depends on how well it supports a specific task.
Can synthetic data replace real data for AI training?
Sometimes it can supplement or stand in for real examples during development, especially when data is scarce or risky to collect. For many real-world systems, however, final evaluation still needs representative real data. A synthetic-only benchmark may conceal the gap between generation and deployment.
Does synthetic data guarantee privacy?
No. Some generators may reproduce or expose information from their training data. Formal techniques such as differential privacy can offer measurable guarantees when properly implemented, but every release still needs a review of utility, threat model and access.
What is the best synthetic-data tool?
There is no universal answer. The right choice depends on data type, required relationships, privacy needs, scale and evaluation options. Start with the simplest method that meets the task, then compare its results with a stronger method if needed.
Bottom Line
Synthetic data is a practical way to create realistic examples for testing, training, simulation and collaboration. Its promise is controlled usefulness, not automatic safety. Define the job first, test whether results transfer to real data, measure privacy risk, and explain where the dataset should not be used.
