What Is Synthetic Data? How AI Generated Data Is Used

What is synthetic data?

Synthetic data is artificial data that an algorithm, a simulation, or a generative AI model creates to mimic the statistical patterns of real records without copying them. It can take the form of tables, text, images, or time series. The goal is to train models, test software, and run analysis without exposing real people.

Think of a flight simulator. Pilots rehearse emergencies there without a real crash. In the data world, synthetic data works the same way. You run your systems on realistic records, so you never touch real customer files.

Do not confuse it with fake data. In short, fake data is random and meaningless, while synthetic data deliberately keeps the structure of the real thing. Still, a simulator never fully replaces reality. In this guide we explain the term at a conceptual level: how teams produce it, where it helps, and where it becomes risky.

How do you generate synthetic data?

Generation methods range from simple to complex. They share one idea: rules that experts define, or patterns learned from real data, drive the creation of new and independent records.

• Rule-based generation: A domain expert writes the rules. For example, an order date cannot come after the delivery date. As a result, the output is predictable but narrow.
• Statistical modeling: You extract distributions and column relationships from real data, then sample new records from them.
• Generative models: GANs (generative adversarial networks), VAEs (variational autoencoders), diffusion models, and large language models produce realistic samples.
• Simulation: You build scenes in a physics or game engine, and the labels come for free.

The right method depends on the data type and on how realistic the output must be. However, the stronger the generator, the heavier the validation work. Also, a model can invent records that look plausible but never existed.

In practice, teams often mix methods. For instance, rules keep the data consistent, while a generative model adds variety.

What is synthetic data and which types exist?

Three main types stand out by format. Time series and audio also exist. Still, tables, text, and images are what businesses meet most often.

Choose the type after you set the goal. A stock management app needs tables. A customer support bot needs text.

Some projects combine types. A multimodal system that matches product photos with descriptions needs both images and text.

How does tabular synthetic data work?

In tabular generation, the model studies how each column is distributed and how columns relate to each other. For example, it captures the link between basket value and item count. Then it produces new rows that match no real row.

Two measures matter at the same time. The first is statistical similarity: averages, ratios, and relationships should stay close to reality. The second is privacy: a generated row should not resemble any individual in the source data too closely.

These goals sometimes clash. Very similar data is useful but risks leakage. On the other hand, very different data is safe but may become useless. Therefore, you tune the balance by measuring both sides.

Consistency rules also matter. A delivery date cannot precede the order date, and a discount cannot exceed one hundred percent. We recommend a validation step after generation that checks such rules.

What are text and image synthetic data good for?

For text, a large language model (LLM) writes sample conversations, question and answer pairs, or labeled sentences for a given scenario. For example, you can ask for varied samples of refund requests, shipping questions, and complaints. Our guide to large language models explains the concept.

For images, two paths exist. Generative models draw new pictures. Simulation environments change light, angle, and background to produce many variations of the same object. In addition, simulation has one big advantage: the labels are automatic and accurate.

One warning applies to both. Generated samples may not reflect the language and image conditions of real users. Therefore, we suggest holding out a small validation set made of real examples.

Text data also suffers from low variety. A model may repeat the same patterns, so you should filter near duplicates after generation.

Where do teams use synthetic data in real life?

Synthetic data does not belong to one industry. It shows up wherever access to data is hard or rare events matter. The list below contains example scenarios, not client results.

• E-commerce: Test databases of orders and returns, plus sample support messages for classification.
• Logistics: Damaged parcel images and simulated route or delivery delays.
• Manufacturing lines: Defective part photos and sensor failure series.
• Autonomous driving research: Rare traffic conditions recreated in simulation.
• Software teams: Records without real identities for development and staging environments.
• Education and research: Sample datasets that students can share, with no real people inside.

These cases share one thing: real data is scarce, risky, or expensive. So synthetic data eases at least one of those three problems.

Small businesses can start too. First, pick one narrow use, such as test data or support message classification. Then validate the result with real samples before you expand.

Why does synthetic data matter?

Data is the shared bottleneck of AI and software projects. It is scarce, expensive, or locked away because it contains personal information. Synthetic data can ease all three problems at once.

• Access: Team members and outside developers work without touching real customer records.
• Speed: You build prototypes without waiting for data collection and labeling.
• Coverage: You can deliberately multiply situations that rarely happen in real life.
• Privacy: The area that touches personal data shrinks, so the risk surface narrows.

Cost matters too. For example, collecting, cleaning, and labeling real data takes weeks of team effort. Synthetic generation automates part of that work, so your experiment cycle gets shorter. Then you can test an idea early and drop weak approaches cheaply.

Still, these benefits do not arrive automatically. You must prove quality and privacy separately.

How do you use synthetic data in model training?

Machine learning models improve by learning from examples. When real examples fall short, synthetic ones complete the training set. For instance, a small model that classifies return reasons in a store can get extra samples of rare reasons.

A practical approach looks like this. First, keep all of your real data, and add synthetic samples only to balance missing classes. Then evaluate the model on a separate test made only of real records. That way you see whether the synthetic data truly helped.

Language models follow a similar idea. For instance, a strong model can produce examples that train a smaller one. We cover that topic in our post on knowledge distillation, so we will not repeat it here.

How do synthetic data and large language models work together?

Large language models are the most common tool for synthetic text. You give the model a role, a scenario, and an output format in a prompt. The model then writes dozens of samples in that frame. Afterward, you filter and label them.

Evaluation sets are another use. To test an assistant that answers from company documents, you can generate question and answer pairs from those documents. Our post on RAG shows how such an assistant works.

However, a generator also repeats its own mistakes. Therefore, review part of the generated pairs by hand. Also note that one model writing the questions and judging the answers can reinforce its own bias.

Why is synthetic data useful for software testing?

Copying real customer data into a test environment is risky for security and for compliance. In fact, synthetic data removes that problem at the root. There are no real identities, yet field formats, lengths, and relationships look realistic.

Example scenario: you are testing an order management screen. The synthetic set deliberately includes long addresses, names with special characters, canceled orders, and empty fields. As a result, the screen gets tested on edge cases you rarely meet in real data.

For custom projects, this approach keeps development and staging safe. In our custom software development work, we recommend planning test data from day one.

How do you use synthetic data for rare scenarios?

Some events are very rare, yet your system must handle them correctly. A warehouse camera has to recognize a damaged parcel. An anomaly alert has to catch unusual behavior. Both are good examples.

When real samples are few, the model cannot grasp these cases well. Instead, simulation or a generative model lets you multiply them. Producing images of a damaged parcel from different angles and lighting is a good case.

There is a trap, though. If you describe the rare case wrongly, the model learns the wrong lesson. Therefore, define scenarios together with a domain expert and compare them with at least one real example. A warehouse team knows how damage really looks, so carry that knowledge into your generation settings.

Does synthetic data really protect privacy?

The short answer is no, not always. Done right, synthetic data strengthens privacy. Done wrong, it can leak details about people in the training set. If a model memorizes a rare and distinctive record, the output can resemble that record closely.

However, mathematical methods such as differential privacy reduce this risk. You add controlled noise to the process, so a single person cannot change the result in a visible way. NIST, the US standards body, published guidelines for evaluating differential privacy guarantees.

Think like an attacker, too. In a membership inference attack, someone tries to tell whether a specific person was in the training data. Moreover, the closer the output sits to the source, the easier that guess becomes. Privacy tests try exactly these scenarios.

The practical rule is simple: never accept a privacy claim without measuring it. Test how close generated records come to source records, and ask for an independent review when stakes are high.

What is synthetic data under GDPR and similar laws?

This section is general information, not legal advice. Data protection law covers information about an identified or identifiable person. In principle, truly anonymous data can fall outside that scope, but proving it is your responsibility.

A synthetic label alone does not make data non-personal. You use real personal data during generation, so the processing itself may need legal review. Also, if the output can be linked back to a source person, it may still count as personal data and the duties continue.

The European Data Protection Supervisor (EDPS) treats synthetic data as a chance for privacy and fairness, yet it also warns that such data can carry the bias of the original. See the EDPS page on synthetic data for details.

Documentation helps in practice. Record which source data you used, for what purpose, and by which method. Also, keep test results that show the output cannot be tied to source people. For website compliance, our GDPR compliant website guide adds useful context. Above all, make the final call with your legal counsel.

How does synthetic data compare with real and anonymized data?

People often mix up the three approaches. The table below puts their strengths and weaknesses side by side. The values show general tendencies, and real results depend on the method and the data.

So what is synthetic data in this table? It is a middle path. It lowers the privacy problems of real data, yet it needs extra checks for realism and bias.

In short, synthetic data does not blindly replace anonymization. Both serve different goals, because each answers a different question. For example, you may choose anonymized data for analysis and synthetic data for a development environment.

How does synthetic data amplify bias?

A model trained on source data produces synthetic data. If the source under-represents a group, the output under-represents it as well. The model may even lean toward the majority pattern and widen the gap. This effect stays silent until the results expose it.

Example scenario: a courier company has support records dominated by addresses from big cities. The generated addresses then drift toward big cities. A system trained on that data may serve customers in small districts worse.

The good news is that you can balance synthetic data on purpose. You raise the share of under-represented groups and fix the distribution. However, you must measure the bias first, because you cannot fix what you do not measure.

• Extract the group distribution of the source data before generation.
• Measure the same distribution again after generation and compare.
• Generate extra samples for under-represented groups and label them separately.
• Test the final result on each group on its own.

What is model collapse and how does it relate to synthetic data?

Model collapse is the gradual loss of output variety and quality when models train again and again on data that models generated. Rare examples vanish first. Then the outputs turn uniform.

An academic paper in Nature, AI models collapse when trained on recursively generated data, studies this concept. Its main message is that real human data stays valuable during training.

Picture the mechanism like this. A model reproduces very rare samples a little poorly. The next model trains on that weaker output and sees those samples even less. After a few rounds the tails of the distribution disappear, and only average samples remain. Photocopying a photocopy causes a similar decay.

Consequently, the practical lesson is clear. Do not replace real data with synthetic data, and use it as a complement instead. Add fresh real samples in every round and measure quality regularly.

How does a lack of realism weaken synthetic data?

Generated data looks plausible at first glance, yet it misses the roughness of real life. For example, typos, odd formats, surprising combinations, and shifting user behavior are often absent.

As a result, a model trained and tested on synthetic data looks great in the lab. Then it fails in unexpected ways with real users. It resembles a driver who masters a simulator and struggles on a real road.

The remedy is simple: run the final exam on real data you held out. Also remember that generators can invent details, which is called hallucination. Our post on AI hallucination explains it.

• Compare real and synthetic samples side by side with your own eyes.
• Look for the real share of outliers and empty fields in the synthetic set too.
• Include behavior that changes over time, such as seasons and campaigns.
• Add typos and rough edges on purpose.

How do you measure synthetic data quality?

No single number expresses quality. In practice you look at three dimensions and decide which one matters most for you.

• Fidelity: Is the statistical structure close enough to reality? Compare averages, distributions, and column relationships.
• Utility: Does a model trained on synthetic data perform acceptably on a real test set?
• Privacy: Do generated records come dangerously close to source records? Check outliers and rare records first.

These three often pull against each other. In particular, higher fidelity can raise privacy risk. So write down your goal first: do you use the data only for testing, or for model training? Your answer sets the acceptance threshold. For example, fidelity leads for test data, while privacy leads for shared data.

What mistakes do teams make when they generate synthetic data?

Knowing what is synthetic data is not enough. You should also know the traps. In our experience, teams stumble in a few places.

• Skipping validation: The data looks fine, so you train on it, and the model fails with real users.
• Assuming privacy: You think it is safe because it is artificial, yet the model may have memorized a record.
• Relying on one source: Training only on one model output narrows variety.
• Ignoring source quality: Flawed source data becomes flawed synthetic data.
• Skipping documentation: Six months later nobody remembers how the data was made.

None of these mistakes is technically hard. All of them need discipline. A written checklist is the cheapest safeguard. Also keep your first project small, so you learn from errors at a low cost.

How do you balance synthetic and real data?

The healthiest approach treats them as partners, not rivals. First, real data anchors accuracy. Synthetic data widens coverage and safety. Instead of a fixed rule for the mix, you tune the ratio by measurement.

• Real data core: The backbone of training and especially testing should consist of real examples.
• Synthetic top-up: Support missing classes, rare cases, and edge scenarios with synthetic samples.
• Separate test set: Never let synthetic data leak into the test set.
• Gradual increase: Raise the synthetic share slowly and watch the real test result at each step.

If the score drops, pull the ratio back, because every dataset has its own sweet spot. This way you find the point where synthetic data helps and the point where it starts to hurt.

When should you avoid synthetic data?

However, synthetic data does not solve every problem. In the cases below, it is healthier to return to real data or pick another method.

• You want to measure real behavior itself, for example what users actually click.
• You lack even enough solid real examples for the generator to copy, because the model cannot know what to multiply.
• Your source data is poor or wrong, because errors multiply as they are.
• Decisions affect people directly and you cannot validate the results.

In these cases, consider better data collection, surveys, controlled experiments, or limiting real data to the fields you need.

How is synthetic data different from neighboring concepts?

Synthetic data often gets mixed up with similar terms. The table shows what each concept does and how it relates to synthetic data.

We covered guardrails in our post on AI guardrails. Synthetic data also helps produce test scenarios that push those guardrails.

What is the synthetic data checklist for businesses and developers?

After you learn what is synthetic data, answer the questions below one by one before you start a project. In practice, a missing answer usually points to an assumption that becomes expensive later.

• Did I write down the purpose and what I want to prove?
• Did I identify sensitive fields and sources of bias in the source data?
• Do I have a validation set that I kept apart from real data?
• Did I run a privacy test that measures closeness to source records?
• Am I using synthetic data as a complement, not as a replacement for real data?
• Did an expert review the legal side?
• Did I document the generation method and version?

If even one of the seven is unclear, settle it before you move on. Also, a small investment protects you from a large misunderstanding. Also share the list with your team, so everyone works with the same expectations.

Where should a small business start with synthetic data?

Example scenario: a small online store wants to try a tool that classifies customer support messages. However, it does not want to share real conversations with an outside developer.

1. Write the goal: only a topic classification trial.
2. Separate identity details from the real messages and keep only the topics.
3. Ask a language model for varied samples for each topic.
4. Filter out near duplicates and overly formulaic sentences.
5. Measure the result on real examples you held out, and document the version.

This approach is fast and keeps personal data inside, so risk stays low. Still, you measure final success on the real samples you kept apart. If you want to set up the process with a team, see our AI and automation services. In addition, our guide on using AI for data analysis offers a complementary view.

In short, the business answer to what is synthetic data is this: a helper that lets you experiment without harming real data, as long as you validate it.