What Is Synthetic Data?

Synthetic data is artificially created by computers or algorithms based on real-world data sets. This data type is widely recognized for its ability to train machine learning models, reduce biases in data sets, and navigate ethical and privacy concerns surrounding real data. Learn more about synthetic data, use cases already in action, and how you might generate your own synthetic data.

What is synthetic data?

Synthetic data is data that has been artificially generated and does not contain real data values. For example, if you asked five people for their height and recorded their answers, this would be real data based on real measurements. However, if you came up with height measurements for five imaginary people, this would be synthetic data. 

In many cases, artificial intelligence (AI) algorithms generate synthetic data as a way to mimic real-world data in terms of its structure. This approach uses the same type of algorithm that imputes missing values in more extensive data sets by deriving the structure and distribution of the data and then creating false data points that are the “likely” values for that point. You can use synthetic data sets to develop software, refine machine learning models, and protect sensitive information. 

While synthetic and real data have different advantages, recent research has found that synthetically trained models tend to perform at a higher level than models trained on real data. In a recent study from MIT-IBM Watson Lab, researchers found that video processing algorithms were more effectively trained on object recognition using synthetic data based on three publicly available data sets than with actual footage [1].

Benefits of synthetic data

Synthetic data offers various benefits, including the ability for researchers to analyze data and develop algorithms without compromising the privacy and security of participants. For educational purposes, synthetic data helps learners and professionals understand different data structures and types. This can assist with developing codes and methods to use in practical applications. Before deploying code in real-world scenarios, developers can test and refine their work using synthetic data sets.

Another benefit of using synthetic data is that you can produce it on demand without concern for regulatory, security, or ethical concerns. Researchers can also decide exactly which features they want in their data to design it in a way that best trains their models. For example, researchers can ensure data sets have “edge cases,” which are extreme or rare events that occur. Since these cases are rare, real data sets might not include them. However, it’s important that models can handle these situations if they arise.

Because researchers design synthetic data, it’s also a powerful tool for bias reduction. Real data often has biases present, and researchers can alter the data structure to account for potential biases. Researchers also do not need actually to collect the data, which can reduce costs. This can be particularly valuable for clinical trials or market research data. 

Disadvantages of synthetic data

While you will find many benefits of synthetic data, it also has potential limitations. If you don’t generate synthetic data correctly, it can include biases, lack variety, and not have the quality necessary to train models and represent real-world data effectively.

When you use generative adversarial networks (GAN) to generate data, it can sometimes be difficult to ensure the distribution of the data is accurate. Researchers may choose to use probabilistic statistical models to create evenly distributed synthetic data sets, but in this case, the models might not accurately represent the data. 

When creating a synthetic data set, it’s important to take steps to make sure the data is high quality and accurate. If it is inaccurate, models trained on the data will not work reliably. A good general rule is that if the real-world data is viable and complete, the synthetic data based on this data will be of better quality. Because of this, it’s best to use diverse data to create synthetic data sets and generative AI models that are well-suited for your type of data.

Real-world uses for synthetic data.

Synthetic data has already shown efficacy in many professional fields. Depending on your area of expertise, you might find existing examples of synthetic data use or related applications that can carry over into your subject field. Some recent ways synthetic data has been applied across different industries include:

• X-rays: If you work in the health care space, you can use synthetic X-rays to train diagnostic algorithms without handling complex real-world data.

• Area estimates of indigenous populations: As an anthropologist, you can apply synthetic data to estimate population sizes and distributions without intruding on these communities' privacy and being limited by missing information.

• Chatbot practice: If you have expertise in natural language processing algorithms, you can use synthetic data to improve chatbot function. Synthetic conversations can help in training chatbots, making them more adept at handling a variety of user interactions.

• Detecting humans who fall at a construction site: Professionals who work in safety can use synthetic data to train surveillance algorithms. Safety systems can use synthetic data to practice detecting and preventing accidents in high-risk environments like construction sites.

• Training smart home robots to understand user movements: In the field of home automation, you might use synthetic data in programming robots to predict and respond to human behavior.

• Training autonomous vehicles for safety: If you work in robotics, you may use synthetic data to improve the safety of new robotic types such as autonomous vehicles. With synthetic data, you can include unusual behavior of pedestrians and other drivers to train autonomous vehicles to handle unexpected situations.

• Testing biases: As a researcher, you might utilize synthetically generated data sets to test biases in existing algorithms and improve the methodology to reduce intrinsic biases.

How to generate synthetic data

When creating synthetic data, you can choose between two primary methods: data-driven generation and process-driven generation. Use your overall purpose and needs of the data set to guide you in choosing the right method.

Process-driven methods

Process-driven methods create synthetic data using mathematical models representing an underlying physical process. You might use this model type when the process is well understood, such as in physics and engineering. It’s also well suited for probabilistic modeling, such as for risk assessment or simulating events. 

Data-driven methods

Data-driven methods use generative models based on observed data. This type of model is often created through imputations using the statistical model of the data or probability distribution methods that look at the entire data distribution and create a mock-up version of it. 

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