What is synthetic data?
Synthetic data is information that's artificially generated by computer algorithms instead of being collected from real-world events. Think of it like a flight simulator for artificial intelligence (AI). Just as a pilot learns to fly in a simulated cockpit without risking a real plane, AI models can learn to recognize patterns using simulated data without risking user privacy.
The key distinction is that synthetic data mimics the statistical properties of real data—like the averages, correlations, and distributions—but it doesn't contain any identifiable information about real people. It looks and acts like the real thing, but no actual humans were involved in creating a specific record.
How is synthetic data generated?
Generating synthetic data isn't just about copying and pasting. It involves using advanced machine learning models to understand the "shape" of real data and then creating new, original samples that fit that shape.
- Engineers often use Generative Models to do this. Technologies like generative adversarial networks (GANs), variational autoencoders (VAEs), and diffusion models analyze a real dataset to learn its hidden patterns. Once the model learns these patterns, it can output an infinite number of new, fake samples that are statistically identical to the original set.
- Another common method is Simulation. This is popular in industries like robotics and autonomous driving. Developers use physics engines—similar to those used in video games—to create virtual worlds. In these worlds, they can generate data by simulating scenarios, such as a car driving through a rainy city or a robot arm picking up a box, without ever needing a physical camera or sensor.
Types of synthetic data
Not all synthetic data is created equal. Depending on your needs for privacy versus accuracy, you might choose different types.
Fully synthetic data
This data is generated completely from scratch. It contains no original user data, meaning there is zero one-to-one mapping between a synthetic record and a real person. Because it doesn't relate to any specific individual, it offers the highest level of privacy protection. However, it requires rigorous validation to ensure it's still accurate enough to be useful for training AI.
Partially synthetic data
Sometimes, you need to keep some real data to make the dataset useful. Partially synthetic data involves taking a real dataset and replacing only the sensitive parts—like names, social security numbers, or addresses—with synthetic values. The rest of the data remains distinct. This approach balances privacy with high utility but carries a slightly higher risk of re-identification compared to fully synthetic data.
Hybrid synthetic data
This approach blends real and synthetic records to create a "super-set" of data. It is often used to enrich a smaller real dataset. For example, if you have a lot of data on regular banking transactions but very little data on fraud, you can generate synthetic fraud records to mix in with the real ones. This helps "upsample" rare events so the AI model has enough examples to learn from.
Synthetic data versus real data
Real data is the standard for accuracy, but it can be expensive to collect, often messy or incomplete, and tightly restricted by privacy laws like GDPR or HIPAA. Collecting real-world data can also take months or years.
Synthetic data, on the other hand, can be less costly to scale. You can generate millions of records in hours. It’s perfectly labeled (because the computer created it, it knows exactly what it created), and it’s privacy-compliant by design. Additionally, synthetic data can be balanced to remove the natural biases often found in real-world collections.
Feature | Real data | Synthetic data |
Cost | Higher (collection and labeling) | Lower (compute power only) |
Speed | Slower (months/years) | Faster (hours/days) |
Privacy | More restricted (PII risks) | Safer (No PII) |
Accuracy | Higher (reflects reality) | Variable (depends on model quality) |
Feature
Real data
Synthetic data
Cost
Higher (collection and labeling)
Lower (compute power only)
Speed
Slower (months/years)
Faster (hours/days)
Privacy
More restricted (PII risks)
Safer (No PII)
Accuracy
Higher (reflects reality)
Variable (depends on model quality)
Industry use cases for synthetic data
Training computer vision models
Autonomous vehicles and robotics rely heavily on synthetic data because collecting real-world information is slow. Even after logging millions of miles on the road, physical testing cannot possibly account for every unique accident scenario. By generating virtual driving environments, engineers can train cars on billions of miles of simulated roads, testing them against dangerous situations like a child running into the street without any physical risk.
Healthcare and medical research
Medical data is extremely private and hard to share. Synthetic data allows researchers to create fake patient records that mimic the statistical patterns of real diseases. This enables hospitals to share data for cancer research or rare disease studies without violating HIPAA. A survey by ManageEngine found that 81% of healthcare organizations are now using synthetic data to innovate while managing privacy concerns.
Financial services
Detecting fraud is difficult because actual fraud is rare. Which can make it hard for AI to learn what fraud looks like. Banks may use synthetic data to generate thousands of fraudulent transaction patterns. This "upsampling" helps train AI to spot suspicious activity without exposing real customer financial histories.
Software testing and devOps
Developers need massive amounts of data to test how an application performs under pressure. This is known as "test data management." Instead of using a dangerous copy of the real production database, DevOps teams can populate a staging environment with millions of synthetic users. This allows them to stress-test new app updates safely and ensure the system can handle high traffic.
Benefits of synthetic data
Privacy and compliance
Privacy and compliance
Synthetic data greatly reduces the risk of exposing personally identifiable information (PII). Because the data refers to no real person, it typically falls outside the scope of strict regulations like GDPR and CCPA. This allows global teams to more easily share datasets across borders freely, without navigating complex legal hurdles.
Cost and speed
Cost and speed
Using synthetic data can significantly accelerate the "Data-to-AI" lifecycle. You don't need to hire humans to manually label images or wait for data to be collected in the field. This efficiency can lower costs and speed up development.
Bias mitigation
Bias mitigation
Real-world data often reflects real-world prejudices. If you train an AI on historical hiring data, it might learn to prefer one demographic over another. Synthetic data allows developers to artificially correct these imbalances. You can generate more data for underrepresented groups—such as ensuring an AI recognizes all skin tones or genders equally—to create fairer, more robust models.
Edge case testing
Edge case testing
Some scenarios are too dangerous or rare to test in real life. You can't crash a thousand real cars just to see how the airbag sensor works. Synthetic data allows you to create these "edge cases" safely. You can simulate rare events, like a blizzard in Phoenix or a specific engine failure, to train systems on situations that represent less than 0.01% of real-world data but are critical for safety.
Generating synthetic tabular data with Vertex AI
For developers, the fastest way to generate a small-to-medium synthetic dataset (example for unit testing or simple demos) is often not to train a complex GAN, but to leverage the generative capabilities of large language models (LLMs) available in Vertex AI.
In this walkthrough, we will use the Vertex AI SDK for Python and Gemini to generate a synthetic "customer transaction" dataset from scratch. Imagine you are a developer building a fintech dashboard. You need 50 rows of "transaction data" to test your frontend. You need specific fields: transaction_id, timestamp, amount, merchant_category, and is_fraud.
Step 1: Set up your environment
First, ensure you have the Vertex AI SDK installed in your Python environment.
- Bash
Step 2: Initialize Vertex AI
Import the library and initialize it with your project details.
- Python
Step 3: Craft a structured prompt
The quality of synthetic data from an LLM depends heavily on your prompt. You must be specific about the schema, the constraints (for example, no negative numbers), and the output format (CSV or JSON).
- Python
Generate and parse the data
Send the prompt to the model and load the response directly into a Pandas DataFrame.
- Python
Using a general-purpose LLM like Gemini for synthetic tabular data is often faster than training a custom statistical model (like a VAE) when you only need "plausible" data for testing software functionality.
- Speed: You get data in seconds
- Flexibility: You can change the schema just by editing the text prompt
- Logic: You can ask the model to bake in complex logic (such as, "If merchant is 'Travel', amount must be over $100"), which is hard to do with simple randomizers
*Note for Enterprise Scale: If you need to generate millions of rows that statistically clone a massive existing private dataset, you would likely move from simple LLM prompting to using Vertex AI Pipelines integrated with specialized partners like Gretel.ai or MOSTLY AI, which are available directly in the Google Cloud Marketplace.
Generating synthetic data with Google Cloud
Google Cloud provides robust tools to help developers generate and manage synthetic data effectively.
Vertex AIOffers a comprehensive platform where you can build and train custom generative models, such as GANs or diffusion models, to create synthetic images or text. You can create a pipeline that automatically generates, validates, and stores this data for your machine learning projects.
BigQueryAllows you to manage and analyze massive synthetic datasets. You can use it to test your analytics pipelines on synthetic data before deploying them to production, ensuring your queries are efficient and cost-effective.Solution
Responsible AITools within Google Cloud help you detect bias in your datasets. You can use these tools to analyze your real data, identify underrepresented groups, and then guide your synthetic data generation to fill those gaps, ensuring your final models are fair and inclusive.
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