Synthetic Data: what it is, how it is generated and its benefits

6 min

How is artificial intelligence trained? Among the datasets used during supervised learning by researchers are synthetic data, i.e. artificially generated information. When real-world data is scarce, sensitive, or difficult to find, the computer itself will produce it to recreate a certain customized context. How is this achieved? We will define the term accurately and then discuss the advantages of using synthetic data.

What is synthetic data?

To put the definition into context, let’s start with an example related to the use of synthetic data for an e-commerce site. Let’s assume that an online shopping company wants to test a new product recommendation algorithm. Instead of taking a risk by using real customer data, the company could opt for a safer and more innovative approach: the use of synthetic data. This will allow it to create completely fictitious customer profiles, each with its own detailed preferences and different purchase histories.

The second step will be to test its new recommendation algorithm on such artificial data. Since the profiles are designed to cover a broad spectrum of consumer behaviour, the algorithm will be able to be tested in an infinite number of purchase scenarios. All this will be done without any invasion of privacy.

The result? A well-run recommendation algorithm is ready to be launched. Its task will be to offer personalized suggestions in line with the preferences of real customers, improving their shopping experience. Through the use of artificial data, the company will have innovated and experimented safely.

In essence, we use synthetic data to augment or replace real data, with the aim of perfecting artificial intelligence models. This, as we shall see, not only protects sensitive information but also helps reduce bias in the data.

Synthetic data meaning

Types of synthetic data: partial or complete

Now that the general definition has been established, we need to understand the various nuances. Indeed, it is not necessarily the case that real data must be artificially recreated in its entirety. There may also be cases where only parts that contain sensitive data or that need to be omitted need to be replaced. Therefore, depending on the process and the objective, we will decide whether to rely on partial or complete synthetic data.

Partially synthetic data

Partial synthetic data is an excellent tool for protecting privacy without losing the analytical value of a dataset. In practice, they only change certain sensitive parts of an original dataset. In this way, the analysis of the data is intact while ensuring that personal information cannot be linked back to real people.

This process of creating partial synthetic data, also known as data anonymization, starts with identifying information within a dataset that is considered sensitive or confidential. This may include names, telephone numbers, email addresses, or any other data that could be used to directly identify an individual.

Once this information has been identified, it is possible to create new synthetic versions that retain the same statistical characteristics as the original data (such as distribution and correlation) but cannot be traced back to a specific individual.

Furthermore, the use of partially artificial data is often compliant with privacy and data protection regulations, such as GDPR, as it reduces the risk of revealing identifiable data.

Complete synthetic data

In contrast, complete synthetic data are generated from scratch without including any part of a real dataset. This means that, unlike partial data that only modify some information of an existing dataset, complete synthetic data are entirely constructed by means of algorithms that simulate the characteristics of real data.

The main advantage of this type of data is that it is able to replicate the relationships, distributions, and statistical properties of real data while being entirely computer-generated. This makes them particularly useful in various fields, especially machine learning and scientific research, where real data is often limited or difficult to obtain due to ethical or privacy issues.

For example, researchers can use complete synthetic data to test and develop new models of artificial intelligence. This data provides the ability to run experiments in controlled scenarios, allowing them to evaluate the performance of a model under different conditions without the risk of revealing sensitive information or compromising people’s privacy.

In addition, the use of fully synthetic data is essential to ensure that the model is robust and performs well in situations not predicted by historical data or when there is insufficient real-world data to train complex models. This can be particularly useful in fields such as medicine or finance, where real data is often incomplete or too sensitive to be freely used.

Using synthetic data allows for innovation without compromising privacy, ensuring both the protection of sensitive information and the precision of your analytical results. Share on X

How is synthetic data generated?

Without going into too much technical detail, let’s try to understand how synthetic data is generated. The process is complex because it uses advanced computational techniques, but what we need to understand are the basic methods that we can adapt to different use cases.

Basic synthetic data generation methods:

  • Statistical distribution: This method first analyses the actual data to identify the underlying statistical distributions, such as normal or exponential. Data scientists then generate samples from these distributions to create a dataset that is statistically similar to the original. This approach is useful for simple data, such as numbers or tables.
  • Model-based: In this case, a machine learning model is trained to understand the characteristics of real data. Once trained, the model is able to generate artificial data that follows the same statistical distribution as the real data. This method is ideal for creating hybrid datasets that retain real statistical properties but with synthetic elements added.
  • Deep learning methods: Techniques such as generative adversarial networks (GANs) and variational autoencoders (VAEs) are used especially for complex data such as images, videos, or time series. GANs, for example, use two neural networks: one generates data while the other tries to discriminate between real and synthetic data. This process continues until the discriminator succeeds in making the distinction, generating high-quality synthetic data that faithfully mimics the variations in the real data.

As mentioned above, these techniques protect privacy and enable compliance with data regulations. They also allow algorithms and models to be tested under controlled conditions, improving their reliability and accuracy. Let’s look at the other benefits of delegating data generation to an algorithm.

What are the advantages of synthetic data?

Surely the main reason for using a simulation instead of real data is privacy protection. Indeed, obtaining data that cannot be traced back to real people makes it easier to comply with privacy regulations such as the GDPR. Companies will thus be able to use data freely in sensitive areas, such as healthcare and finance, without legal worries.

This is an advantage that makes this type of data the ideal solution in healthcare. In hospitals, synthetic data could be used to analyze disease trends without exposing sensitive patient information, ensuring compliance with data protection regulations. But in reality, there are also other, no less important advantages that promote the choice of this technique.

Enhancing Machine Learning

Synthetic data are widely used to enhance artificial intelligence, especially when real information is scarce or overly sensitive.

Using synthetic data, we can train a facial recognition algorithm safely and effectively, without compromising people’s privacy, by generating realistic faces that do not match any real individuals.

By providing large and varied datasets, synthetic data becomes ideal for training machine learning models. Quantity affects accuracy and reduces training time.

Effective testing and simulation

Synthesized data creates a safe and scalable environment for testing software and systems, enabling the simulation of risk-free scenarios. By creating a controlled environment, developers can identify and solve problems without fear of real-world consequences.

An example? In a flight simulator, synthetic data allows pilots to practice complex maneuvers and handle aviation emergencies without real risk, improving their preparedness and safety.

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Limitless scalability

Imagine having access to an unlimited stream of data perfectly tailored to your analytical needs. Synthesized Data transforms research and development by providing unlimited streams of customized data, eliminating the cost and complexity of real data collection. This on-demand access allows companies to generate large volumes of data, accelerating innovation and reducing time-to-market.

Many technology companies choose synthetic data to test the effectiveness of new artificial intelligence algorithms, achieving rapid results without the need to acquire external data.

Reduce bias

Designed as a neutral model, synthetic data helps eliminate bias in real data sets, making artificial intelligence models fairer and more reliable.

For example, in a recruitment model, synthetic data can be structured to ensure fair representation of gender and ethnicity. This counteracts the biases that are often present in historical recruitment data and promotes fairer and more objective hiring decisions.

Accelerate development and innovation

With data already tagged and ready to use, development teams can focus on innovation and product improvement rather than tedious data preparation. This not only speeds time to market but also increases the overall efficiency of the development process.

For example, in a machine vision project, synthetic data can include images of vehicles with pre-assigned labels identifying types, colors, and sizes. This allows researchers to focus on refining the algorithms rather than the time-consuming labeling process.

In summary, synthetic data is not just a technological resource; it is a key part of a company’s innovation process. Generating extremely similar, but not real, data allows for technological advancement that respects privacy without compromising analytical effectiveness.