AI requires a lot of data — data that organizations often lack. To be sure, organizations are generating and storing vast amounts of data, but only a small portion of it may be relevant to their AI initiatives. In a recent IBM Institute survey, 42 percent of business leaders said they worry about not having enough proprietary data to train AI models or fine-tune foundation models for their specific needs.
One of the most effective ways to fill this gap is to utilize synthetic data. As the name suggests, synthetic data is artificially generated data that mimics real-world data but doesn’t originate from real-world sources. It is generated using algorithms and computer simulations to represent the statistical characteristics, patterns and relationships found in real datasets, making it useful for training AI models.
Why Use Synthetic Data
In the past, data scientists used statistical techniques and agent-based modeling to create artificial datasets. Now, however, AI-powered tools enable users to easily generate synthetic data and customize it for their specific use cases. Generative AI models analyze patterns in real-world data and “learn” how to generate similar data and tailor it to meet specific conditions.
Synthetic data can be generated quickly, enabling organizations to accelerate the development and testing of AI models. It also reduces the costs of acquiring, processing and storing real-world data.
The exposure of sensitive data is a serious threat with any AI initiative, and synthetic data reduces the risk. In fact, it provides stronger privacy guarantees than anonymized data, helping organizations comply with regulations such as GDPR and CCPA. Synthetic data can also be generated to represent diverse demographics and scenarios, counteracting the biases that are often present in real-world data.
Types of Synthetic Data
Like real-world data, synthetic data can take a variety of forms depending on the application. Industries such as finance, education and healthcare typically use structured synthetic data to simulate the complete database records of customers, students or patients. In addition to closing gaps in real-world datasets, synthetic data helps these industries comply with legal and regulatory mandates to maintain privacy and confidentiality.
Training models for speech recognition, object detection and similar tasks requires unstructured synthetic data, which can include text, sounds, images and other multimedia data. Synthetic unstructured data is also used to train models for medical diagnostics, robotics and other applications that require the analysis of images and 3-D spaces.
Models that simulate future trends and behaviors require synthetic time series data. This data mimics the patterns and characteristics of data such as sensor readings, traffic volumes and stock prices.
Other Benefits of Synthetic Data
In addition to providing the volume of data organizations need to train AI models, synthetic data can help eliminate the gaps and inconsistencies that often plague real-world data. Organizations can model hard-to-reach populations and analyze scenarios that rarely occur.
Synthetic data also allows for controlled test scenarios and repeatable experiments. Users maintain control over every aspect of the generation of synthetic datasets, enabling them to tailor the data to meet specific conditions. The generative models that create synthetic data can apply labels and annotations automatically, further increasing the value of the data.
Data collected from actual events and observations remains a valuable asset. It captures the full complexity and nuances of real-world scenarios in ways that synthetic data cannot match. However, as organizations seek to capitalize on AI, synthetic data can provide large volumes of data cost-effectively while minimizing the data exposure and bias concerns associated with real-world data. Contact Cerium to discuss how you can take advantage of synthetic data to accelerate your AI initiatives.
