Synthetic data is any data that is not acquired through directly observing or measuring real-world processes. It can be created randomly, derived from real datasets, or generated through mathematical simulations to mimic the actual data.
Ideally, synthetic data bears a strong resemblance to real-life data points in terms of their statistical and structural properties. It also has the same features and attributes as real data, along with an identical database schema.
Generating synthetic data can be fast and cost-effective, and in some cases can work better than using real-world data. If you are developing an AI or data science application, you’ll likely need synthetic data for training and testing the system. You can also use the synthetic data generation process to create datasets that preserve the privacy of individuals by creating non-identifiable information.
Here's how the approach works—and why it matters to your AI efforts.
There are several ways to generate synthetic data.
Synthetic data can be composed of completely random points in space, which ensures that all the features and attributes present in the real data are also generated in the artificial data points. A typical example would be data used to test software systems. Example data points are generated using the database schema or metadata and fed into the software system to validate and test that it’s working.
Synthetic data can also be derived from real datasets. The goal is to create a dataset that has the same distribution, properties, and mathematical structure as the real dataset. This way, the synthetic examples closely resemble the real-life data points. Options for deriving a new set of points from existing ones include:
You can use several deep learning architectures to generate synthetic data. Generative adversarial networks (GANs) are popular for creating new data. They use two neural networks: the generator and the discriminator.
The generator creates data points, and the discriminator evaluates whether the created point is valid or not. These two networks work against each other like adversaries, hence the name. GANs have evolved into a powerful technology that can even create realistic images of different objects, people, and animals.
Other options for synthetic data generation are autoencoders and variational autoencoders, where the encoder encodes or compresses a real example point. The decoder inflates or decompresses the encoded instance, creating new examples. The output data points, therefore, resemble the input examples but are different from them.
With the popularity of artificial intelligence applications and deep learning networks, the use of synthetic data is growing by the day. Typical domains include natural language processing (NLP) systems, computer vision applications, medicine, finance, and others. There are many scenarios and situations where synthetic data is required and generated. Here are just a few.
You can use synthetic data for quality assurance and testing software systems before deployment. For example, an online banking system undergoes thorough testing before it goes live. During the test phase, artificially generated transactions are carried out with fictitious customer data. This helps determine when and why the system fails and where its weaknesses are.
Synthetic data helps establish benchmarks about the accuracy of many machine learning algorithms. The process of artificially generating data points is directly in the control of machine learning engineers. Not only can they specify correlations between variables, but they can also define the noise levels in data.
In this way, they can evaluate the performance of the algorithms under different controlled conditions, thereby helping developers to identify the strengths and weaknesses of their methods.
Many machine learning systems that include deep learning networks require large amounts of data for training. When data is not available or too costly to acquire, synthetic examples are generated and augmented with real data points to train the machine learning system.
Synthetic data is also used for transfer learning, where the goal is to train a machine learning system using real datasets augmented with artificially generated data points from one domain and apply it to a specialized domain for solving real life problems.
Artificially generated data points can help protect the privacy of individuals represented by a dataset. Generating synthetic data is part of anonymization and can be considered as a guard against leaking sensitive information.
With synthetic data, it becomes difficult to link the data back to any specific person or individual. This is important for applications in healthcare, finance, banking, and more.
For many applications such as medical imaging, data is too expensive to acquire. In these scenarios, synthetic examples are derived from real examples and are added to the existing dataset to reduce costs associated with data acquisition.
Many fraud detection or intrusion detection systems are trained and tested using authentic data with artificially generated fraudulent examples. Many existing datasets don’t include all possible types of cases and scenarios depicting theft, intrusion, or fraud. A synthetic data generator can create any number of such cases, which can then be added to the data for further processing by the learning system.
Synthetic data is also used in simulators; for example, when creating an emulated environment for training self-driving cars or in-flight simulators. You can also use mock data to build artificial scenes, buildings, objects, and even people in computer games, including those that use virtual reality.
Numerous challenges have to be addressed when generating and using synthetic data in AI and machine learning applications.
The accuracy and validity of machine learning systems trained and tested with synthetic data are not proven until they are run with real data. It is quite possible that the mathematical model that generates the artificial examples is either oversimplified or has some missing aspects of real-world data. For example, a simulated vehicular environment may not capture all of the physical nuances of the real world (think of weather conditions, say). Hence, data scientists and machine learning engineers require real-world examples to validate their system.
Also, the outliers, or boundary cases, which are critical in fraud detection or anomaly detection, may not be present in synthetic data. When run live, systems for detecting fraud are at risk of failing under scenarios that were overlooked by the data generation process.
Synthetic data has been a game changer in many data science and machine learning applications. In the future, more systems will be trained and tested with artificially generated datasets. Here are a few expectations for future directions for synthetic data:
Generating synthetic data can be fast and cost-effective. If you are developing an AI or data science application, you’ll likely need synthetic data for training and testing the system. Moreover, you can use the synthetic data generation process to create datasets that preserve the privacy of individuals by creating non-identifiable information.