Exploring Synthetic Data Definition: A Guide for Innovators

Ever feel like you’re drowning in data, but somehow still thirsty for insights? Or maybe you’re sitting on a goldmine of sensitive information that you can’t fully use because of data privacy regulations. This is where the idea of a synthetic data definition comes into play for startup founders, investors and even marketing leaders.

The basic synthetic data definition is artificially generated information that mirrors real-world data. Synthetic data is created using algorithms and models. But it’s so much more.

Table Of Contents:

• What Exactly is a Synthetic Data Definition?
  • The Four Main Types of Synthetic Data
  • How Synthetic Data is Made
• Why Synthetic Data Matters
  • The Benefits of Synthetic Data
• The Limitations and Challenges of Synthetic Data
• Real-World Applications of Synthetic Data
  • Synthetic Data in Finance
  • Healthcare Uses
  • Self-Driving Cars
• Synthetic Data Definition in the Future
• Conclusion

What Exactly is a Synthetic Data Definition?

Think of synthetic data as a digital twin of your actual data. Instead of collecting information from real-world events, it is created using learning algorithms.

The goal is to create data which represents original data sources. This artificial data maintains the key statistical properties and patterns of the original dataset, but with this method, you will find that there are differences, as it contains none of the sensitive, identifiable information.

The Four Main Types of Synthetic Data

One perspective is that the discussion around the synthetic data definition isn’t cut and dried. In the AI community, agreement on a standard explanation remains debatable.

One expert created this breakdown of types of synthetic data to give clarity. There are generally four categories to think about in a synthetic data definition.

• Data Imputation: This involves filling in gaps in an existing dataset. Advanced methods today go way beyond simple averages, using machine learning algorithms to make generated values useful.
• User Creation: This technique generates entirely new user profiles and behaviors, useful when scaling or safeguarding is needed. It’s valuable for training models in sensitive fields.
• Insights Modeling: This method preserves the statistical integrity of real data without including actual identifiable records, which is good for data protection. For example, market research can generate extensive models.
• Manufactured Outcomes: This approach is used for generating synthetic data to simulate scenarios that do not yet exist, like with self-driving car companies needing scenarios to simulate on the road.

How Synthetic Data is Made

Creating data isn’t a simple matter of pushing a button, although some companies might give the illusion that that is the way things are done. A synthetic data generator utilizes advanced techniques.

Here’s the overall process in simplified steps:

1. Model Training: First, a machine learning model is trained on a real dataset. This model learns the underlying patterns, relationships, and statistical distributions within the data.
2. Data Generation: Once the model is trained, it can be used to generate synthetic data points. This involves techniques like Generative Adversarial Networks (GANs) or Variational Autoencoders (VAEs).
3. Validation: The generated synthetic data needs validation to test for accuracy and to make sure that it represents the structure of the real data. This often involves comparing the statistical properties of the synthetic data to the original data.

Why Synthetic Data Matters

Synthetic data isn’t just a clever tech trick. It’s a valuable tool in solving issues ranging from data scarcity to regulatory compliance.

If you have projects where you want to find creative insights that your real data could bring you, data restrictions won’t slow you down. You’re still using information gathered from people.

The Benefits of Synthetic Data

Here are the typical benefits of using this type of information.

• Privacy: The benefit that you hear the most about with synthetic data has to deal with data privacy. Since it doesn’t contain actual personal data, it allows for testing, development, and research.
• Data Augmentation: If you are needing more, you can simply add to your datasets. You can do this for rare situations, without risking exposure of personal data.
• Cost and Efficiency: It’s almost always faster and more cost-effective to generate synthetic data. For self-driving cars, AI in healthcare or finance, or new market testing, it would take a tremendous amount of data for the research to be complete and accurate.
• Overcoming Limitations: Sometimes getting real data simply isn’t an option. Maybe it’s too rare, too expensive, or too dangerous to collect the raw data.

Here is a good visual representation:

The Limitations and Challenges of Synthetic Data

So, what are the downsides? Synthetic data seems too good to be true, and like everything, it also has its limits and problems.

• Bias: Just as a student is shaped by their teachers, data can be a reflection of what it was created from. If the original data set has biases, the synthetic dataset likely will too.
• Overfitting: This means the data looks like too perfect of a picture of real data, which, in reality, is rarely picture perfect. So, this is why it is good to understand overfitting with your data science team.
• Not a Mirror of Real Data: Synthetic data should always act as a solid “twin”. Although, not being able to mimic the full messiness or complexities of life’s real situations can mean a failure of machine learning models.
• Privacy Concerns: There’s a slight chance synthetic data could give away some of the data it learned from. Regulations such as GDPR or CCPA still need to be respected, and in case of a potential risk of exposure, companies still need to provide data protection.

Real-World Applications of Synthetic Data

Companies like IBM are exploring this idea of computer-generated data for all kinds of practical problems. Generative models create many possibilities.

Synthetic Data in Finance

Say you are a bank who uses the data to test its programs that detect risk, while maintaining safety standards. A synthetic dataset can really help with risk management.

Imagine this: A team of AI engineers can feed simulated datasets into your fraud detection algorithms without ever getting near your bank account. No real customer details are exposed.

Healthcare Uses

Another example is seen within health industries. Doctors and research experts might be hesitant about exchanging X-rays of individuals dealing with serious conditions such as brain or heart complications.

But there is something else these healthcare researchers might feel safer sending. The solution? Fake images that still mirror the stats, and they provide this in a way that supports learning for future diagnosis and training.

Self-Driving Cars

It’s no surprise the automotive industry has been leading. You could be building a self-driving AI.

Where are you going to safely test all the potential real-world challenges a driver could face without huge amounts of danger or other factors? Enter simulated worlds with time series data and edge cases.

These digital realities can crank out limitless driving situations and rare pedestrian movements without anyone being at any kind of risk. AI models can then be trained on all these datasets.

Synthetic Data Definition in the Future

With its current uses, there is growing attention for good reasons. Generative AI is quickly improving.

But the thing with synthetic data, you’re essentially “teaching” a computer to mimic reality. It will go as we program and tell it to, including the use of natural language processing.

Conclusion

The world of machine learning is changing fast, and knowing the full scope of what the synthetic data definition covers is key for tech founders and marketers. Fully synthetic data and partially synthetic data both have many use cases.

It isn’t magic, and it certainly comes with things to be careful of. While it is an important advancement to solving big limitations and data analysis, make sure your data scientists consider all factors.