What Is Synthetic Data? (A Definitive Guide for 2025)

Over 80% of developers say they'd choose synthetic data over real data, mainly because it's safer and easier to access. (Source: IBM research)

Synthetic data is artificially generated data that is similar to real-world data and has zero privacy risk. In 2025, it's the best solution for AI teams, developers, and data scientists who need high-quality, bias-free data. This is needed when real data is limited, sensitive, or too expensive to use.

In this blog, we will explore

• What is synthetic data
• It's history and how it's evolving in 2025
• Is synthetic data legal
• Benefits of Generating Synthetic Data
• Different types of synthetic data
• Synthetic Data Generation Tools and Technologies
• Synthetic Data Generation Use cases

We will also check a revolutionary synthetic data generation tool that makes generating synthetic data reliable and rewarding.

What is Synthetic Data?

In fields like AI and machine learning, a huge volume of high-quality data is needed to train the models, but there's one big problem: real-world data can be hard to find, expensive, and heavily regulated. This makes accessing the data difficult; and this is where synthetic data can tackle this challenge.

Synthetic data is artificially generated datasets that mimic the statistical properties of real data. It is based on real data but is created by algorithms that simulate real-world events. Synthetic data can be created whenever you need it and in large amounts.

It can be used as a safe replacement for real data in testing and training AI models. With synthetic data, teams can build faster, keep privacy intact, and follow data rules without using real sensitive info. This is especially useful in industries like healthcare, finance, the public sector, and defence.

History of Synthetic Data and How it is Evolving

Stats: As per a study, the global synthetic data market is expected to grow from $215 million in 2023 to over $1 billion by 2030, with a rapid 25.1% annual growth rate.

Synthetic data may look like a new term — but it is not entirely new.

It started in the 1970s

During the early days of computing (1970s and 1980s), researchers and engineers used computer simulations to generate data for physics, engineering, and other scientific domains where real measurements were difficult or costly.

One notable example: flight simulators and audio synthesizers produced realistic outputs from algorithms.

The 1990s paved the way ahead

The modern concept of synthetic data (generating data for privacy and machine learning) started around the 1990s. In 1993, Harvard statistician Donald Rubin suggested a new idea: create fake data that looks real to protect people's privacy.

He proposed that the U.S. Census could use a model trained on real data to generate new, similar data (with no personal details of the public included).

In 2010, it grew roots around AI

As AI started to grow fast, synthetic data became more important in the 2010s. To train deep learning models, huge amounts of data were needed — but collecting and labeling real images was expensive. So, teams began creating fake images using tools like 3D models to help train their AI.

2015 and the Present

Synthetic data generation is evolving because of modern generative AI.

• Transformer-based models and GANs can produce convincing synthetic text, images, and even video.
• Hybrid approaches are used to generate synthetic data to boost the diversity of datasets.
• Many synthetic data generation tools are being developed that address different challenges of developing synthetic data.

Is Synthetic Data Generation Legal?

The legal rules around synthetic data are still evolving and they vary a lot from country to country. There's no single global law focused only on synthetic data yet. Instead, companies must follow existing data protection laws (like GDPR in Europe or PDPA in Singapore), based on where the data comes from. These laws cover how data is collected, used, and stored. If synthetic data is created from personal information, privacy safeguards like anonymization or differential privacy must be used.

Since rules differ across regions, it's important to:

• Understand which country's laws apply
• Use privacy-safe techniques
• Stay up-to-date with new AI and data regulations

Benefits of Generating Synthetic Data

If you're wondering, "what is the main benefit of generating synthetic data?" then understand that it has many. Generating synthetic data offers many practical advantages over real data. Here are a few notable ones:

1. Get Unlimited & Customizable Data

You can generate synthetic data at any scale that fits your needs. Instead of waiting to collect new real-world examples, you can instantly generate as much data as needed. This speeds up AI model development and lets organizations experiment with new scenarios without delay.

2. More Privacy and Compliance

Since synthetic data contains no real personal information, it can be used without exposing privacy. Industries with strict data laws (healthcare, finance, public sector, and others) can use synthetic data as it provides the same statistical insights as real data while checking all regulatory requirements. In sensitive fields like genomics or healthcare, synthetic data copies the patterns of real data but uses fake identities. This lets teams safely share and test data without risking anyone's privacy.

3. Save Costs and Time

Collecting and producing real data is expensive and takes a lot of time. With synthetic data generation, the costs and timeline can be cut down by eliminating the need for data collection and manual labeling. For example, manually labeling an image can cost a few dollars and take some time; while generating a similar synthetic image costs just a few cents and can be generated in seconds.

4. More Data Diversity and Bias Reduction

One of the major benefits of synthetic data is that it can include rare cases or underrepresented groups that may be missing from real datasets. This helps reduce bias and allows AI models to handle unusual or unexpected inputs better—something that's often not possible with real data alone. As a result, the AI performs more accurately in real-world situations. Since diversity is a built-in feature of synthetic data generation, you can balance classes or create rare scenarios. Example: In Banking, synthetic data can identify unusual fraud patterns to reduce bias in your AI models.

5. Better Control Over Quality and Safer

Since synthetic data is created in a controlled way, it can be made cleaner and more accurate than real data. You can add rare cases or special situations on purpose — like extreme weather for sensors or unusual medical conditions. This helps companies test systems safely, without real-world risks. In security areas, they can even simulate cyberattacks or fraud without exposing real networks. Overall, synthetic data makes testing safer and more reliable.

Types of Synthetic Data

Don't confuse — synthetic data is not mock data.

Before AI became popular, synthetic data mostly meant random or rule-based mock data. Even today, many people confuse AI-generated synthetic data with basic mock data, but they're very different. Synthetic data made by AI is more realistic and far more useful.

Synthetic data comes in different forms depending on what kind of AI or system you're training. Usually, there are two main types:

a) Partial Synthetic Data

Only sensitive parts of a real dataset (like names or contact info) are replaced with fake values. The rest of the data stays real. This helps protect privacy while keeping the dataset useful.

b) Full Synthetic Data

The entire dataset is generated from scratch, using patterns and stats learned from real data. It looks and behaves like the original but contains no real-world records. This makes it safe to use without privacy risks.

Other types of synthetic data include

• Tabular Data: These are similar to spreadsheet elements (rows and columns). It helps train models for predictions, fraud detection, and analysis — without using real customer records.
• Text Data: Used to train chatbots, translation tools, and language models. AI generates realistic messages, reviews, or support queries to improve systems like ChatGPT or virtual assistants.
• Audio Data: Synthetic voices, sounds, or speech are created to train voice assistants and speech recognition tools. For example, Alexa uses synthetic speech data to improve understanding in different accents and tones.
• Image & Video Data (Media): AI-generated visuals train systems in face recognition, self-driving cars, or product detection. For example, Waymo uses synthetic road scenarios to test vehicle safety.
• Unstructured Data: This includes complex combinations like video + audio + text (e.g., a news clip with captions). It's useful in advanced fields like surveillance, autonomous systems, and mixed-media AI tasks.

What Are Synthetic Data Generation Tools and Technologies?

There are many tools and techniques for generating synthetic data. The right choice depends on your use case, the type of data you need (text, images, tables, etc.), and how sensitive your real data is. Here are a few tools & technologies used for generating synthetic data:

• Large Language Models (LLMs): Used to create synthetic text, conversations, or structured data based on training inputs.
• Generative Adversarial Networks (GANs): Two neural networks work together to generate data that looks real. Commonly used for images, videos, and tabular data.
• Variational Autoencoders (VAEs): This model compresses real data and recreates new versions that keep the same patterns and structure.
• Statistical Sampling: You can create data manually using known patterns or distributions from real-world datasets.
• Rule-based Simulations: Generate data by defining business logic or event-based rules.
• Syncora.ai's Agentic AI: This platform uses intelligent agents to generate, structure, and validate synthetic data across multiple formats. It is faster, safer, and privacy-friendly.

Some tools are better for privacy, while others are designed for high realism or specific formats. Whether you're building AI for healthcare, finance, or retail, picking the right generation method is important to create safe, high-quality, and useful synthetic datasets.

Who can Use Synthetic Data? — Use Cases

Practically any organization that relies on data can benefit from synthetic data. Check the table below for the application for each industry.

Industry	Use Cases (Applications)
Autonomous Vehicles & Robotics	Car makers generate massive synthetic driving scenes to train self-driving AI. They can test systems safely in simulation before real-world trials.
Finance & Insurance	Banks and insurance agencies can use synthetic data to model risk, detect fraud, and meet rules. They can create fake transactions and customer behaviors to mimic real data without using confidential information.
Healthcare	Using synthetic patient data can speed up drug discovery by simulating clinical trials. AI for medical imaging is trained on artificial X-rays and MRIs to improve disease detection while protecting patient privacy.
Manufacturing & Industrial	Factories can use synthetic sensor and visual data to improve quality control. This helps AI spot product defects and predict equipment failures.
Retail	Retailers can use synthetic data to simulate customer behavior, test pricing strategies, and improve recommendation engines.
Government	Governments can use synthetic population data to model public services, forecast policy outcomes, and run simulations without risking citizen privacy.
Others	Synthetic data also helps in marketing (simulating customer behavior), cybersecurity (simulating attacks), and other areas.

Who can use it in a Company?

Synthetic data can be used by

• Data scientists & AI ML engineers to train AI models & prototype quickly when real data is scarce
• QA & development teams can test apps and systems under various scenarios. They can also use synthetic data to detect bugs early.
• HR & business teams can simulate employee data for planning and run what-if scenarios without exposing real people.
• Marketing & product teams to model customer segments or run A/B test campaigns without using real user data

How to Generate Synthetic Data?

Synthetic data can be generated by using statistical models or simulations that mimic real-world data. This involves training algorithms like GANs or rule-based engines on real datasets. This way, they can learn patterns, then produce new, similar data that doesn't expose any actual records.

You can use tools like

• Scikit-learn
• SDV (Synthetic Data Vault)
• Faker (Python package)
• PySynthGen

Although this way of generating synthetic data is effective, this process often requires heavy manual setup, deep domain knowledge, and can be time-consuming.

There is a new approach to this.

What is Syncora.ai? How Does it Help with Synthetic Data Generation?

Syncora.ai is an advanced AI platform that automatically creates realistic synthetic data. It uses AI agents to understand what you need, then generates various types of data like tables, text, or images. You just tell it what data you want, and Syncora.ai creates it for you.

Core capabilities:

• Self-generating & highly realistic: AI agents create and improve data without manual coding. You just give raw data, and it will restructure and create synthetic data that has 97% fidelity.
• Fast & saves money: No ETL backlogs, and the data is generated within minutes (saves weeks of manual work) with the help of agentic AI. This helps you to launch AI faster and cuts labeling and prep costs by 60%
• Trackable and compliant: Every piece of data is logged on a secure blockchain for transparency, and the process complies with HIPAA, GDPR, and other norms.
• Fixes data gaps: Uses hidden or hard-to-access data without revealing personal info, giving edge to the AI model for training edge cases.
• Better accuracy: The built-in feedback loop helps reduce bias and improves model performance, up to 20% better in early tests.

Syncora.ai lets you generate synthetic data without risk of privacy concerns and scaling issues. It provides secure, on-demand synthetic data and lets you accelerate your AI projects and innovate faster.

To Sum It Up

Synthetic data is changing how AI teams, data scientists, and companies access and use data. It solves problems like privacy, bias, and high data costs and makes it easier to train, test, and deploy smarter AI systems. From healthcare to finance, it's already helping teams move faster while staying compliant. And now, with agentic AI tools like Syncora.ai, generating high-quality, privacy-safe synthetic data takes just minutes, not weeks. If you're building AI in 2025, synthetic data isn't just helpful, it's essential.

FAQs

1. What is synthetic data generation software?
   Synthetic data generation software creates artificial data that mimics real data. It is used to train and test AI models without using private real data. There are many software you can use, with Syncora.ai being one of the best. Syncora.ai uses agentic AI to generate high-fidelity, privacy-safe synthetic data quickly and at scale.
2. What is synthetic data in machine learning?
   In ML, synthetic data is artificially created data. It is used to train, test, and improve AI/ML models. It helps fill gaps, simulate rare scenarios, and improve model performance, and is useful when real data is limited or sensitive.
3. What is synthetic test data generation?
   Synthetic test data is fake data created for testing software or systems. It simulates real-world inputs to check how applications would behave, without risking real customer or sensitive data.
4. Why is synthetic data important for AI and ML in 2025?
   In 2025, synthetic data plays a crucial role in AI and ML development by solving data scarcity, reducing bias, protecting privacy, and enabling scalable, low-cost model training.
5. What tools are used for synthetic data generation?
   Popular tools include:
   • Syncora.ai
   • Mostly AI
   • Synthetaic
   • Gretel.ai
   • DataGen
   Each offers unique features like agentic data generation, privacy guarantees, or domain-specific synthesis.
6. Is synthetic data accurate and reliable?
   Yes. With advancements in agentic AI and GANs, synthetic data can reach up to 99.6% fidelity, making it highly reliable for AI/ML model training and testing.
7. What is synthetic proxy data?
   Synthetic proxy data is fake data and is used when real data isn't available or can't be shared. It copies the patterns of real data, so teams can test and analyze systems safely.
8. What is synthetic panel data?
   Synthetic panel data mixes real and fake information to show how people or groups might change over time. It's helpful for studies in economics or policy when long-term real data isn't available.
9. What makes Syncora.ai different from other synthetic data platforms?
   Syncora.ai stands out for its agentic synthetic data generation- powered by autonomous AI agents that structure, synthesize, and clean data with high fidelity and privacy. It offers a single-API experience and supports blockchain-based licensing for enterprise-grade deployments.
10. Can I use Syncora.ai to generate synthetic data for machine learning?
    Yes, Syncora.ai is purpose-built for AI/ML workflows. It enables users to create high-quality, privacy-compliant synthetic datasets for training, testing, or augmenting models in domains like healthcare, finance, and software development.