Synthetic data is data created by an algorithm or model to imitate the shape and behavior of real data — generated to match how real records look and relate, rather than collected from actual events. That makes it different from real (production) data, which is recorded straight from events, and from de-identified data, which masks real records in place; synthetic data is net-new, with no real individual behind it. Teams use it to get production-realistic data for software testing, AI training, and development — especially when real data is missing, too sensitive to use, or too limited to do the job.
What synthetic data is
Synthetic data is generated rather than recorded, and that one fact is what the whole concept turns on. Real data is a record of things that happened: a customer placed an order, a sensor logged a reading, a patient was admitted. Synthetic data is the opposite kind of object — a machine produces it to a specification, so every value is net-new and none of it is copied from a real person or event. The point of generating it is not to fake a record but to manufacture data that behaves like the real thing for a purpose the real thing can't safely or practically serve.
The word that does the heavy lifting is "realistic," and it means something more precise than "plausible-looking." Good synthetic data is statistically faithful: it reproduces the distributions, correlations, and referential integrity of the source it imitates, not just the surface format of individual values. Picture a year of e-commerce orders. A naive fake would generate rows with valid-looking order IDs and prices and stop there. A statistically faithful synthetic version keeps the same spread of order values, the same December spike, the same tendency for high-value carts to ship express — and it keeps every order tied to a customer who exists in the customer table. The realism lives in those relationships, which is exactly what a test suite or a model needs to behave the way it will in production.
There are two broad ways synthetic data is generated. The first is generation from scratch: you describe the data you want — a schema, a set of rules, a prompt — and a generator produces it with no underlying dataset. The second is generation by modeling an existing database, where the generator learns the patterns of data you already hold and produces new records that share its statistical shape. Both are forms of synthetic data generation, and which one fits depends entirely on whether you have usable data to start from.
How synthetic data differs from real and de-identified data
The cleanest way to separate the three kinds of data a team works with is by how each one comes to exist: real data is recorded, de-identified data is transformed, and synthetic data is generated. Real data is captured directly from events, so it carries genuine signal alongside genuine personal information and whatever gaps and biases the world handed you. The other two are both attempts to get usable data without exposing the real thing — but they take opposite routes, and the route decides how freely the data can move into the development, test, and training environments where the actual work happens.
De-identification starts from real records and alters them in place: it masks, scrambles, or replaces the identifying fields — names, account numbers, dates of birth — so the values that point to a specific person are removed while the rest of the record stays put. A de-identified patient row might swap a real name and medical-record number for fabricated ones while the admission date, diagnosis codes, and lab values carry over unchanged — it is the same real visit, relabeled. Synthesis takes the opposite approach: it starts from nothing and builds net-new records to a specification, inventing a patient who was never admitted and a visit that never happened, shaped to look and relate like the real ones in aggregate.
The practical consequence is traceability. A de-identified row began life as a real person's data, so if the transformation is weak — a reversible mask, a rare value left untouched — it can in principle be traced back toward the individual it came from, and it often stays subject to privacy review for exactly that reason. A from-scratch synthetic row has no individual behind it to trace, because no real record was ever involved. That distinction sets up the practical question of when to choose synthetic data over real or de-identified data, which usually comes down to your starting situation rather than a blanket preference.
| If your situation is… | The better fit is… |
|---|---|
| You already have production data and need its exact real-world shape and edge cases | De-identified data — a safer copy of the real records |
| You need more volume, coverage, or scenarios than production holds | Synthetic data — generated to your spec |
| You have no usable production data (it's gated, too sensitive, or doesn't exist yet) | Synthetic data — generated from scratch |
Why teams use synthetic data
Teams reach for synthetic data when real data is the bottleneck — when getting it, cleaning it, or clearing it for use costs more time than the work it's meant to support. In many organizations production data sits behind access tickets and compliance review: an engineer who needs a realistic dataset files a request, waits on a sign-off, and watches the feature work stall in the meantime — or never clears review at all. Generating data sidesteps that queue, and along the way it solves several distinct problems that collected data handles poorly. The through-line is control: you specify generated data, whereas collected data gives you only what the world already logged.
- Privacy by construction. Because from-scratch synthetic records don't map to real people, far less sensitive data has to travel into development, test, and training environments — the places where data is most exposed and least controlled. The privacy protection is a property of how the data was made, not a cleanup step applied afterward.
- Volume on demand. You can generate far more data than production holds, which is what load and stress testing actually require. A new feature might need ten million orders to find the query that falls over at scale, and waiting for production to accumulate them is not an option.
- Control over shape and coverage. Generation lets you engineer the specific cases you care about — a fraud pattern that appears once in ten thousand transactions, a malformed input that crashed the parser last quarter — and balance classes that are underrepresented in real life. You decide the coverage instead of accepting whatever the world happened to log.
- Availability when production is off-limits. When the real data is missing, gated, or too sensitive to touch, generated data is often the only way to get something realistic to build against at all.
The Tonic Advantage. Tonic Fabricate puts that on-demand generation behind a single conversation: describe what you need with a prompt, an uploaded schema, or a connection to a live database, and Fabricate generates relational data with referential integrity preserved across tables. For a complex schema it drafts a reviewable generation plan before producing anything, so you can see and adjust how it intends to build the data, and an optional Validation Agent reviews and refines the output even when the initial prompt was imprecise. The result is production-realistic data without the production-data dependency or the ticket queue.
Where synthetic data fits: common use cases
Synthetic data shows up wherever real data is missing, restricted, or insufficient, and a handful of patterns recur across teams. Each one is a case where generating data is faster, safer, or simply more complete than collecting it.
Software testing and QA
Testing needs realistic data that looks and relates like production without depending on it. Synthetic test data gives a QA suite the variety and referential structure of the real database — orders tied to customers, payments tied to invoices — with none of the sensitive values and none of the wait for a production extract. That makes test environments reproducible and shareable, and it lets teams build synthetic data for software testing that covers edge cases the current production snapshot happens not to contain.
AI model and agent training
Models learn from examples, and synthetic data fills the gaps real training data leaves. Because you defined the scenario that produced each record, you can augment a dataset that's too small, rebalance one that's skewed toward the common case, and attach the ground-truth labels that real datasets so rarely arrive with. This is the core of using synthetic data for AI model training, and the same approach extends across synthetic data for machine learning and AI, from supervised fine-tuning to building evaluation sets.
Reinforcement learning
Training an agent to act over time needs more than a pile of examples — it needs a complete, internally consistent world the agent can operate in, with events that connect sensibly across days and tasks that can be verified as done correctly. Synthetic generation can build that world end to end, with the temporal integrity and structured metadata that make graded tasks possible. In a Tonic.ai benchmark, an open-source model (Qwen3.5-35B-A3B) fine-tuned only on Fabricate-generated synthetic email data improved on the real-world Enron email benchmark from 80.5% to 86% — outperforming o3 and gpt-4.1-mini, both around 85%, without training on a single real email (reproducible dataset on Hugging Face).
Greenfield and cold-start
When a product is new, there is no usage data yet — and you still need data to build and test against. Generating against a schema produces a working dataset before the first real user arrives, so development isn't blocked waiting for the system to accumulate history.
Sales demos
A demo needs data that looks like the prospect's world without touching real customer records. Synthetic data shaped to the prospect's domain lets a team present on relevant, realistic data while keeping actual customer information out of it.
What synthetic data does and doesn't guarantee
Synthetic data earns trust by being honest about its limits, and two of them are worth stating plainly. The first is privacy. Data generated from scratch has no real individual to expose, which is the strongest privacy position available. But data modeled on real records is not automatically private: a generator built without the right controls can memorize and reproduce a rare real value — an unusual salary, a one-of-a-kind address — that points back to the person it came from. "Synthetic" describes how the data was made, not a guarantee about what it contains. Generation on real data pairs with deliberate safeguards for that reason — limiting how tightly a generator fits any one record, and screening output for rare real values that slipped through — which is the heart of managing re-identification risk.
The second limit is fidelity, and it's bounded by method and source. From-scratch data is only as good as the rules you give it — specify too little and you get data that's valid but unrealistic. Modeled data is only as good as the dataset it learned from — a biased or thin source produces biased or thin synthetic output. How close the output gets to that ceiling depends on how faithfully the generator captures the source: Fabricate models the real schemas, patterns, and distributions of a connected database, so the synthetic version inherits the source's statistical shape rather than a rough approximation of it. The practical test cuts through the theory: train on synthetic, test on real. Build the model or the test suite on generated data, then evaluate it against held-out real data and see whether performance holds.
Train on synthetic, test on real. The reliable way to judge synthetic data is by what it produces downstream: if a model trained on it performs on genuine real-world data, the data was faithful enough for the job — and if it doesn't, no amount of surface realism makes up for it.
That test is the same yardstick any data is held to — does a system built on it work in the real world — applied honestly to data you generated yourself. Synthetic data is not a universal replacement for real data, and the cases where it falls short are knowable in advance, which is what makes it dependable for the many cases where it fits. Measuring that fit is its own discipline, covered in how to measure synthetic data quality and fidelity.