What is Synthetic Data and Why is it Gaining Popularity?

Synthetic data, artificially generated to mimic real-world data, is gaining traction across industries. Unlike data collected from real-world scenarios, synthetic data is produced using algorithms, simulations, or statistical models. As demand for large, high-quality datasets grows, especially in AI and machine learning, synthetic data presents a compelling alternative.

How is Synthetic Data Generated?

Synthetic data is generated using computational methods and simulations to create data that mimics the statistical properties of faux data. The data can take various forms, such as text, numbers, images, or videos. There are three main ways to create synthetic data:

1. Statistical Distribution: Scientists study real data to find patterns and underlying statistical distributions (e.g., normal, exponential) and generate non-real samples from these distributions, creating a dataset that statistically resembles the original.
2. Model-based: Computers are trained with real data to learn how it behaves. After training, they can create new data that behaves like real data, which is useful for hybrid datasets.
3. Deep Learning Methods: Advanced techniques like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) are used to make very realistic fake data, especially for things like pictures or time-based data.

Why is Synthetic Data Gaining Popularity?

The market for creating synthetic data is growing fast. In 2023, Gartner predicted that by 2024, 60% of the data used for AI would be synthetic. In 2023, it was worth about $0.29 billion, and it’s expected to grow by 33% (CAGR) each year, reaching around $3.79 billion by 2032 ( S&S INSIDER ).

Key reasons that synthetic data is gaining popularity include:

• Privacy and Compliance: Synthetic data helps protect individual privacy, reducing risks and enabling compliance with regulations like GDPR.
• Cost and Time Efficiency: It can be generated quickly and at a lower cost, especially for simulating rare events.
• Data Augmentation: It augments real-world data, helping create more balanced datasets that improve machine learning models.
• Innovation Enablement: Synthetic data allows for experimentation without real-world constraints, essential for industries like autonomous vehicles.
• Overcoming Data Scarcity: It provides an alternative in scenarios where real data is scarce or costly to obtain.

Use Cases

Synthetic data is increasingly utilized across various scenarios:

• Software Testing: Simulates various conditions to test software functionality without using sensitive real data.
• ML Model Training: Used to train models when real-world data is incomplete or unbalanced.
• Privacy-Compliant Data Sharing: Enables safe data sharing without exposing sensitive information.
• Product Design and Behavioral Simulations: Used for benchmarking and testing under controlled conditions.

Pros and Cons

Here’s a quick overview of the key advantages and disadvantages of using synthetic data:

Pros

• Privacy Protection: No real individuals involved, reducing the risk of breaches.
• Cost Efficiency: Cheaper and quicker than collecting real-world data.
• Data Augmentation: Balances datasets for better model accuracy.
• Customization: Tailored to specific needs, supporting innovation.

Cons

• Quality and Accuracy: Often lacks real-world complexity, leading to biases, overfitting, and unreliable models.
• Validation and Trust: Raises doubts about model reliability, risking performance degradation over time.
• Ethical, Regulatory, and Resource Issues: Raises ethical concerns, demands significant resources, and creates barriers for smaller players in the AI field.
• Impact on Research and AI: May omit key edge cases, spread misinformation, and reduce AI diversity, affecting adaptability and the authenticity of online content.

Human oversight can help maintain data quality and fairness. Long-term, we need sustainable ways to address these issues as well as ethical and legal issues like privacy.

Applications & Tools

Synthetic data has become a powerful tool across various industries, enabling businesses to innovate while protecting privacy and improving efficiency.

Applications of Synthetic Data

Synthetic data is widely used across industries:

• Financial Services: Used to test fraud detection, risk assessment, and trading strategies, synthetic data enables innovation while maintaining privacy and compliance. ( Gon?alo (G) Martins Ribeiro )
• Retail and eCommerce: Helps model consumer behavior, optimize pricing, and personalize customer experiences based on simulated data.
• Manufacturing: Essential for simulating driving scenarios and interactions, supporting the development of autonomous vehicles.
• Healthcare: It simulates patient records, allowing researchers to test algorithms without compromising privacy and to model rare diseases.

Top Generative AI Tools

Several tools are available for generating synthetic data, each tailored to different needs:

• MOSTLY AI : Generates privacy-compliant synthetic data with a focus on bias protection.
• Gretel : Facilitates the creation of diverse data types for analytics and machine learning.
• Tonic.ai : Secure synthetic data generation and de-identification for AI and software development.
• Additional Noteworthy Tools: Other specialized tools include GenRocket , Hazy , and The Synthetic Data Vault for complex and scalable synthetic data generation. MDClone and Synthea cater specifically to healthcare, while Faker and KopiKat provide open-source and no-code options, respectively.

Final Thoughts

Synthetic data is becoming an essential tool for organizations, offering privacy-preserving, cost-effective, and diverse datasets. While it may not fully replace real-world data, its advantages are significant, and its use will continue to expand. Balancing synthetic and real data is crucial to avoid pitfalls like model collapse, ensuring AI systems remain effective, reliable, and ethical.

If you're interested in exploring how synthetic data can benefit your business, we're here to help. We invite you to schedule a complimentary 30-minute consultation with our team at Blue Orange Digital . Our experts are ready to guide you through the possibilities and solutions tailored to your needs.