What Is Synthetic Data? A Tech-Savvy Guide to Using Synthetic Data

Synthetic data is gaining attention as artificial intelligence (AI) continues to evolve. But what exactly is it, and why is it so important today?

At a high level, synthetic data refers to data that's generated by algorithms or mathematical models. It is not data collected from the real world. In other words, instead of gathering data from actual events or systems — like patient records or sensor readings — you simulate that data based on models that mimic the patterns and properties of real-world data.

This concept isn’t new. As far back as the 1940s, scientists like John von Neumann were using simulation-based models such as Monte Carlo methods to generate synthetic datasets.

So, what has changed in recent years? The scale, accuracy, and applicability of synthetic data, driven by advancements in machine learning and the growing need to overcome data limitations.

Introduction

The purpose of generating synthetic data is simple: to overcome situations that lack ‘ground-truth’ — that is, any data produced from a true real-world source.

Why use synthetic data?

There are several reasons why synthetic data has become such a big deal:

• Data privacy regulations: Laws like GDPR and CCPA restrict how companies can use and share real data, especially if it contains personal information.
• Data scarcity: In many domains — like healthcare, autonomous vehicles, or edge cases in finance — collecting sufficient real-world data is costly, impractical, or even impossible.
• Ethical and environmental concerns: Simulated data avoids potentially harmful testing (e.g., crash simulations) and reduces the environmental footprint of data collection.
• Model performance: Advances in generative modeling have made it possible to create synthetic datasets that closely mimic real-world distributions, improving the training of machine learning algorithms.

Types of synthetic data

Synthetic data can take many forms:

• Structured: Organized into rows and columns with consistent data types (e.g., transaction records, time series).
• Unstructured: Free-form data like images, text, or audio.
• Semi-structured: Somewhere in between, like log files or JSON-based datasets.

Some common synthetic data types include:

• Numerical and categorical data: Often used in financial systems or customer profiles.
• Geospatial data: Simulated movement patterns, traffic flows, etc.
• Text, audio, images, and video: Useful for training AI models in natural language processing or computer vision.

(Related reading: structured vs. unstructured data & common data types.)

How is synthetic data generated?

There are different methods for creating synthetic data, ranging from simple to highly complex.

For example, a simple statistical model could describe physical systems, like the behavior of ideal gas in a box, described by the Maxwell-Boltzmann Distribution model. Or complex probabilistic machine learning models can support drug discovery, as in the case of AlphaFold by Google DeepMind.

Statistical and traditional ML models

In privacy-sensitive and critical applications — such as biomedical analysis, financial modeling, defense and cybersecurity — traditional machine learning and statistical models are prioritized. That’s because these models are inherently simple, reliable, and knowable, as they are white box systems.

• Statistical models: Use basic probability distributions (e.g., Gaussian, Poisson) to simulate data. These are interpretable and often used in scientific or business modeling.
• Traditional machine learning: Includes models like decision trees or regression that can generate or augment datasets.

Generative models

Generative models have gained much attention and media hype, mostly due to the use of these ML models in consumer-oriented applications of generating synthetic images, audio, and videos, including deep-fakes.

• GANs (Generative Adversarial Networks): Pit two neural networks against each other to generate realistic data.
• VAEs (Variational Autoencoders): Encode and decode data in a compressed format to learn underlying patterns.
• Diffusion models: These newer models simulate data generation as a gradual process, and are known for producing high-quality outputs, particularly for images.

Each approach has trade-offs in terms of interpretability, scalability, and fidelity to real-world data.

Explicit vs. implicit generative models

Generative models can be explicit or implicit. Understanding the difference helps clarify where synthetic data works best.

Importantly, if these models are trained on biased or incomplete data, the synthetic data they generate may also be flawed, skewed, or factually incorrect.

Explicit models

Explicit models model the underlying data distribution directly and transparently. You can observe and interpret how they generate results, like seeing the exact function they use to generate outputs.

This makes them highly useful in critical domains like healthcare or finance.

Implicit models

Implicit models do not model the distribution directly— instead, they learn to approximate it through training data. GANs and diffusion models fall into this category.

Implicit models are powerful but harder to interpret, and their accuracy depends heavily on the quality and completeness of training data.

When can synthetic data replace real data?

This is one of the biggest open questions. In short: sometimes it can, sometimes it can’t. Meet these conditions so that your synthetic data is sufficiently reliable to train AI models or perform analytics tasks.

Yes, synthetic data can replace real data when…

• The synthetic data has high statistical fidelity, meaning it preserves key characteristics like mean, variance, and correlations.
• The synthetic data is relevant to the task, such as training AI models on the same types of data for which they'll be used.
• The domain knowledge has been correctly encoded, and data bias or skew has been minimized.
• There are privacy and safety concerns or legal constraints with using real data. synthetic data may be the only solution since acquiring real-world data may be impractical, impossible or unsafe.

One such example is the images of passengers in vehicles used to train AI models that would deploy safety features such as airbags depending on the physical form of passengers and the nature of the collision.

No, do not use synthetic data when…

• Real-world edge cases or rare events are critical and difficult to simulate. For example, if you’re trying to predict patient outcomes in clinical trials, synthetic data cannot replace real data.
• You're dealing with highly interconnected data sources that require inter-dataset correlation, e.g, combining two synthetic datasets that were generated independently may lose important relationships).
• You require 100% factual accuracy, such as for auditing or legal use.

Synthetic data is best seen as a complement rather than a total replacement. For example, it can be used to augment small datasets, fill in gaps, or test models before deployment.

Additional points to consider

When choosing whether to use synthetic data, start with these two questions:

1. Can you do things with synthetic data in the same way as real data? For example, analytics, hypothesis testing, training other models for downstream tasks.
2. Can you do things to synthetic data in the same way as real data? For example, combining database records.

These are important answers to have, because replacing real data with synthetic data is not that simple. Here’s why:

• Synthetic models are only approximations. That means errors and limitations are built in to the model.
• Evaluation metrics aren’t perfect. Tweaking your model to optimize one metric could underweight another metric, for example. No single metric can capture the entire picture.

Lastly, think about what happens when you combine synthetic data with other synthetic data and real data. The result is not necessarily consistent and accurate.

If the datasets are synthesized independently, they may not hold inter-dataset correlations, as is the case for real-world data sources. Therefore, the resulting combined dataset would be less reliable than the individual datasets.

Benefits of synthetic data

Synthetic data offers a range of benefits, especially for organizations pushing the boundaries of innovation:

Privacy protection: Since it doesn’t contain actual user information, synthetic data reduces the risk of data breaches.

Cost savings: Collecting real-world data — especially at scale — is expensive. Synthetic data eliminates the need for costly data collection, storage, and anonymization.

Faster experimentation: Data scientists and engineers can quickly generate datasets tailored to specific scenarios or edge cases.

Ethical testing: In domains like self-driving cars or healthcare, testing edge scenarios is safer and more ethical using simulated environments.

Best practices for using synthetic data

Here are a few tips for making the most out of synthetic data:

• Validate against real data: Always benchmark synthetic data against real datasets to confirm it retains key characteristics.
• Ensure diversity and coverage: Make sure synthetic data represents the full variety of demographics, behaviors, or events needed.
• Understand the model: Know whether you're using an explicit or implicit model and what its strengths and limitations are.
• Avoid overfitting: Especially when training models on synthetic data, be cautious not to learn patterns that don’t generalize to the real world.

Final thoughts

Synthetic data is a powerful tool in the modern data stack. While it won’t replace real-world data entirely, it opens up new possibilities in AI, analytics, and privacy-first development. From structured transaction logs to photorealistic video simulations, the ability to generate lifelike yet artificial data is reshaping how we build and train intelligent systems.

As generative models continue to evolve, expect synthetic data to play an even larger role in critical fields — from cybersecurity and defense to healthcare, transportation, and beyond.