How Synthetic Data Is Revolutionizing Businesses

As businesses integrate AI into their processes and interactions, a major pain point has been the lack of sufficient and high-quality data to effectively train models. This can delay development or result in a limited model, impacting the accuracy, reliability, scalability, and overall performance of AI systems.

One solution to overcome these challenges is using synthetic data to supplement available data. It's crucial to understand the benefits and risks of using synthetic data; we've spoken with experts at Invisible and have produced a series on synthetic data to share these insights.

What is Synthetic Data?

Synthetic data is a class of data that is artificially generated, contrasted with real data which is directly captured from the real world.

Real Data

• Pros: Real data is almost always the best source of insights from data.

• Cons: Real data is often expensive, imbalanced, incomplete, unavailable, or unusable due to privacy regulations. It can also be biased or contain errors.

Synthetic Data

• Pros: Synthetic data can be an effective supplement or alternative to real data, providing access to better-annotated data to build accurate, extensible AI models. 

• Cons: The quality of synthetic data often depends on the quality of the model that created it and the dataset developed, and requires robust QA. 

• User skepticism might also be another challenge, as users may perceive synthetic data to be “inferior” or “fake”.

When combined with real data, synthetic data creates an enhanced dataset that often can mitigate the weaknesses of the real data. Gartner estimates that by 2030, synthetic data will completely overshadow real data in AI models. (Source: Gartner, 2022) 

Importance of Synthetic Data

“While synthetic data is powerful, human data adds authenticity and real-world relevance. Combining human-generated and synthetic data is crucial, and together they create robust, reliable AI models,” says Aleksei Shkurin, Technical Lead of AI Enablement at Invisible.

Here’s why it’s important:

• Data Scarcity: Often, there isn't enough real data available for training. Synthetic data can fill in these gaps, providing the vast amounts of information needed.

• Cost-Effective: Generating synthetic data is cheaper and faster than collecting real-world data, especially for large datasets.

• Enhanced Training: Synthetic data can be tailored to specific needs, improving the training process and model performance.

• Privacy: In industries where data privacy is paramount, synthetic data built based on real world data simplifies the training process.

Benefits for Businesses Using Synthetic Data

Businesses across industries can use diverse, realistic data to overcome limitations of real-world datasets, some examples include:

• Demand Forecasting: Retailers can use synthetic data to simulate various market conditions, customer behaviors, and seasonal trends. This helps in training AI models to accurately predict demand, even for new products with little historical data. By generating synthetic customer transactions, retailers can better forecast sales, optimize inventory, and reduce stockouts or overstock situations.

• Underwriting: Synthetic data can simulate various customer profiles and risk scenarios, helping insurers train AI models for more accurate underwriting decisions. This is particularly useful when real-world data is limited or when exploring new markets or products where historical data is scarce.

• Medical Imaging: Synthetic data is invaluable in training AI models for medical imaging, where labeled data can be scarce and expensive to obtain. By generating synthetic images of medical conditions, AI models can be trained to detect diseases, such as cancer or fractures, with high accuracy, even when real data is limited.

As for Invisible Technologies? Shkurin sees three top benefits to mastering synthetic data generation:

• Innovate Faster: Synthetic data allows for rapid development and iteration of AI models, leading to quicker innovation.

• Expand Capabilities: We can support clients with limited data by providing them with synthetic datasets, and opening new revenue streams.

• Stay Competitive: By quickly generating high-quality datasets, we can keep our AI solutions at the cutting edge.

This is the first part in a series of articles on synthetic data. The second article looks at the rise of synthetic data used in AI training and benefits for businesses, and the third article reveals some of the challenges and solutions to generating usable synthetic data.

Ready to Talk About How to Use Synthetic Data to Improve Your Business?

At Invisible, we consult with businesses spanning industries like Finserv, Healthcare, Retail, Hospitality, and more on their AI strategies. Visit our Get Started page to set up a conversation.