Blog Post

Quality measurement of synthetic data

Data is the new oil in today’s digital age, but a slim few are so lucky as to have access to a gusher. That’s why so many companies are making their own digital fuel in the form of synthetic data, which is both inexpensive to produce and highly effective at training machine learning (ML) models.

What is synthetic data?

Synthetic data is a fast-growing trend and a valuable tool in the field of data science. In short, synthetic data is any data that isn’t based on real-world events. Instead, it’s artificially generated using computer programs, processes, and tools.

It’s cheap to produce, versatile in its applications, and highly robust. This makes synthetic data highly useful for its primary purpose: For the training of ML models.

While synthetic data is artificial, it reflects real-world data and research has shown that it can be as good as or even better than data based on real-world information. This is why developers and data scientists are increasingly relying on synthetic data to train their ML models, especially in the field of computer vision.

According to Gartner, by 2024, 60% of the data used in artificial intelligence (AI) and ML development will be synthetically generated. By 2030, the same report predicts that most of it will be synthetic. “The fact is you won’t be able to build high-quality, high-value AI models without synthetic data,” the report says. 

Why is synthetic data important?

Developers need easy access to large and carefully annotated datasets to train their ML models. The more accurate and diverse training data is, the more accurate a model will be.

When you use real-world data, however, you’re physically limited by the data that’s available. A lack of sample size and diversity are especially problematic, particularly for more niche or specialist applications and ML models.

This is where the value of synthetic data comes in. Since it’s entirely machine generated, datasets can be completely customized and built to be as diverse and representative as is needed for a given application. This can result in ML models that are better trained and more accurate than they would have otherwise been if fed with real-world data.

Synthetic data challenges

While the use of synthetic data brings many advantages, there are also some challenges.

The primary challenge is one of quality: The quality of synthetic data can vary greatly. Synthetic data is typically generated by generative algorithms that are supported by input data, meaning that the quality of the output can depend highly on the quality of the input. If the input data is biased, for example, the output can reflect this bias and skew the ML model.

Secondly, synthetic data needs to be sufficiently realistic so that it appears as natural, real-world content. In the use case of a clothing advertisement, for example, marketers may want to change the model’s ethnicity to match that of the user by using AI. The resulting output, therefore, needs to look natural. This is especially important where images include humans due to a phenomenon known as ‘uncanny valley’.

Synthetic data validation

As a result of these and other challenges, it’s highly important to validate the quality of synthetic data. This is especially true for complex datasets. One way to validate synthetic data and ensure its accuracy is to compare it with real-world data. This method helps to highlight inconsistencies in synthetic data when trying to replicate complex real-world data.

Another way validation can be achieved is by having a random crowd of people review a fixed set of images and evaluate their accuracy and appearance.

Returning to the example of using synthetic human models in a clothing advertisement, validation of a generative algorithm could be achieved by turning to the judgments of a multinational crowd of thousands of people.

By asking the crowd a question like, “Is the model in this image a realistic representation of a human being?” with a yes/no answer, it becomes possible to quickly gather responses and aggregate judgments relative to reach a set agreement level.

This is the approach we use at In conjunction with our dynamic judgments feature, this approach helps to reduce the time and cost of data validation while simultaneously improving the accuracy and reliability of ML models.

Want to learn more about how the Tasq platform can help you extract more value from your synthetic datasets? Sign up for a 30-minute demo today!

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