In today's world, many organizations aim to become data-driven and leverage AI use cases. The right data is crucial to developing an AI strategy that delivers the desired ROI. Despite abundant data, 75% of AI solutions fail or remain undeployed. This paradox indicates that the problem may not be the quantity of data but the scarcity of "usable data." Small organizations and startups also face challenges due to a lack of data. However, synthetic data offers a solution, providing much-needed relief and helping to overcome these obstacles. 

Another compelling statistic is a study by Gartner, which suggests that by 2030, most organizations will rely on synthetic data for their AI use cases.

What is Synthetic Data?

“Synthetic Data” is generated using specialized techniques that enable data scientists/organizations to mimic actual data but customize it per the use case requirements and the volume needed. It is generated using different techniques, one of which will be discussed in this blog.

Apart from using synthetic data as a way to generate more data or usable data, synthetic data also has the following benefits: 

1. Many real-world datasets are imbalanced (when one class dominates the other, e.g., you are solving a classification problem where your target variable has two classes, “Yes” and “No,” but 70% of your data is “No” only.) This kind of data cannot be used to build an AI model. Synthetic Data can help generate non-dominating classes to make the data more balanced and usable for AI use cases.
2. Highly regulated industries can’t use PII to train their model and can now generate something similar to the original data rather than the actual data. Imagine building a prediction model on medical image data; rather than using the actual data, which might have confidential patient information, you decide to generate a dataset that represents that information, but at the same time, since it is not original data, it is successfully able to mask the information. This provides security and confidence to your legal and compliance partners. 
3. Acquiring data can be expensive, often costing organizations millions of dollars. Generating synthetic data might be a more economical option for those with in-house talent.

Now that we are convinced synthetic data is beneficial let's discuss the widely used technique for generating it. 

Generative Adversarial Network (GAN): GANs are a popular deep-learning model for generating synthetic data. There are two primary components of GANs: Discriminator and Generator. The generator is responsible for generating fake data, while the discriminator classifies whether the generated data is close to actual data and then provides feedback to the generator.

Use Cases for GANs

1. Predicting rare diseases: This technique is highly useful for detecting cancers or rare diseases where the data is scarce. Synthetic data can help “augment” the data, and then a model can be built on top of it. 
2. Predicting fraudulent transactions: The data used for fraud detection is highly imbalanced, as fraud transactions are usually lower than real transactions. Synthetic data can simulate more complex fraud scenarios and generate additional data to capture these patterns better.

It's essential to remember that real-world data can be biased, and this bias can be perpetuated in synthetic data. To combat this issue, it is crucial to ensure that the data used to generate synthetic data is as unbiased as possible.

Gartner