Data has become one of the most valuable resources in modern technology. Businesses use data to understand customers, improve products, detect fraud, train artificial intelligence systems, and make better decisions.

But collecting useful data isn't always easy.

Real-world data can be expensive to collect, difficult to obtain, protected by privacy laws, or simply unavailable in large enough quantities. This has led to growing interest in something called synthetic data.

Synthetic data is artificially generated information that is designed to resemble real-world data. Instead of collecting every piece of information from real people, machines, or environments, computers can generate realistic data for testing, analysis, and training.

Synthetic data is becoming particularly important as artificial intelligence continues to grow.

What Is Synthetic Data?

Synthetic data is data created by computer systems rather than directly collected from real-world events.

For example, imagine a company wants to develop an AI system that can recognize pedestrians in photographs.

Instead of collecting millions of photographs of real people, the company could create computer-generated images containing artificial pedestrians in different environments.

The images aren't photographs of actual people, but they can contain many of the characteristics the AI system needs to learn.

Synthetic data can take many forms, including numbers, text, images, video, audio, customer records, financial transactions, and other types of information.

The goal is usually to create data that behaves similarly to real data while avoiding some of the problems associated with collecting real-world information.

Why Do We Need Synthetic Data?

One of the biggest reasons is privacy.

Real-world datasets can contain extremely sensitive information. Medical records, financial information, customer details, and other personal data cannot simply be shared freely.

Synthetic data can provide an alternative.

A company could create artificial customer records that have similar statistical characteristics to real customers without containing the actual personal information of those customers.

This can make it easier for developers and researchers to work with realistic datasets while reducing the risk of exposing private information.

Synthetic Data and Artificial Intelligence

Artificial intelligence has become one of the biggest drivers of synthetic data.

AI systems need enormous amounts of training data. Collecting enough real-world data can be extremely difficult.

Synthetic data can help fill the gap.

For example, an autonomous vehicle system needs to understand roads, pedestrians, vehicles, traffic signs, weather conditions, and countless other situations.

Rather than relying entirely on real-world driving footage, developers can create simulated environments containing millions of different scenarios.

The AI can then learn from those artificial situations.

This is especially useful for rare events. An autonomous vehicle might encounter a particular dangerous situation only once in millions of kilometres of real-world driving.

A computer simulation can create that situation repeatedly and safely.

Synthetic Images and Video

Computer-generated images and video are another major source of synthetic data.

Developers can create artificial environments and objects that would be expensive or dangerous to capture in the real world.

For example, an AI system designed to identify damaged vehicles could be trained using artificially generated images showing different types of damage.

Synthetic images can also be created with precise labels.

A developer knows exactly where the object is located in an artificially generated image, making it easier to train certain types of AI systems.

Synthetic Data in Healthcare

Healthcare is another area where synthetic data could be extremely useful.

Medical datasets can contain highly sensitive information, making them difficult to share with researchers and software developers.

Synthetic medical records can be generated to resemble real patient populations without directly representing actual patients.

Researchers could potentially use this information to develop software, test systems, study trends, or train machine learning models.

However, synthetic healthcare data still needs to be carefully evaluated to make sure it accurately represents the characteristics of real patients.

Synthetic Financial Data

Banks and financial companies also have reasons to use synthetic data.

Financial systems need to be tested against many different situations, including unusual transactions and potential fraud.

Synthetic transactions can help developers test their systems without exposing actual customer banking information.

For example, a bank could generate millions of artificial transactions containing different spending patterns.

Some could represent normal customer activity, while others could simulate suspicious behaviour.

This allows fraud detection systems to be tested in a controlled environment.

It Can Help With Rare Events

One particularly useful feature of synthetic data is the ability to create situations that are difficult to find in real-world datasets.

Consider a cybersecurity system.

A company might want to train an AI system to identify unusual network activity. A real dataset may contain thousands of examples of normal activity but relatively few examples of serious attacks.

Synthetic data can be used to create additional examples of suspicious activity.

The system can then be exposed to many different variations of potential threats.

This doesn't replace real-world data, but it can help fill important gaps.

Synthetic Data Can Reduce Costs

Collecting real data can be expensive.

Companies may need to send employees into the field, purchase equipment, gather photographs, conduct surveys, or pay for access to specialized datasets.

Synthetic data can sometimes be generated much more quickly and cheaply.

Once the software used to create the data has been developed, large quantities can potentially be produced automatically.

This makes synthetic data particularly attractive for organizations that need huge datasets for AI development.

Synthetic Data Isn't Always Perfect

There is an important catch.

Synthetic data is only useful if it accurately represents the real world.

If the artificial data contains unrealistic patterns, an AI system trained on it may learn the wrong things.

For example, imagine a facial recognition system trained primarily on synthetic faces that don't accurately represent the diversity of real people.

The resulting system could perform poorly when exposed to real-world faces.

This is known as a problem with data quality or distribution.

Synthetic data therefore needs to be carefully designed, tested, and compared against real-world information.

Synthetic Data Can Contain Bias

Synthetic data can also reproduce the biases found in the systems used to generate it.

If artificial data is created based on an incomplete or biased dataset, those problems can potentially appear in the synthetic version.

This is particularly important when synthetic data is used to train AI systems that make decisions affecting people.

Simply replacing real data with artificial data does not automatically eliminate bias.

Developers still need to examine the resulting datasets and understand how they were generated.

Synthetic Data and the Future

As AI systems become more sophisticated, synthetic data is likely to become increasingly important.

Companies may combine real-world datasets with synthetic information to create larger and more diverse training datasets.

AI systems may also become better at generating realistic artificial data.

This could create a cycle where AI helps generate data that is then used to train and improve other AI systems.

However, real-world data will remain extremely valuable.

Synthetic data is best viewed as another tool rather than a complete replacement for reality.

Final Thoughts

Synthetic data is essentially artificially generated data designed to behave like real-world data.

It can help companies overcome privacy concerns, reduce data collection costs, create huge training datasets, and generate unusual situations that are difficult to find in the real world.

Artificial intelligence is likely to be one of the biggest beneficiaries. From autonomous vehicles and cybersecurity to healthcare and financial services, synthetic data can give AI systems additional examples from which to learn.

But synthetic data has limitations. Poorly generated data can produce poor results, and artificial datasets can still contain bias or fail to accurately represent reality.

The most effective approach will often be a combination of real and synthetic information.

As computers become better at simulating the real world, synthetic data may become an increasingly important part of modern computing.

In a world where data is becoming more valuable and more difficult to obtain, the ability to create useful data artificially could become almost as important as collecting it.