The Advantages of Synthetic Data Over Real Data
In recent years, there’s been a growing trend in the use of synthetic data. This is data that’s generated by algorithms rather than gathered from real-world sources. And while some may think that this kind of data is less useful than traditional data, there are actually many advantages to using synthetic data over real data. In this blog post, we will explore the advantages of synthetic data over real data. From privacy concerns to cost savings and more, read on to learn more about the benefits of synthetic data.
What is synthetic data?
There are two main types of synthetic data. On one side there is synthetic tabular data and on the oder side there is synthetic image data & synthetic video data.
Synthetic tabular data is often used in business applications, as it can be generated to mimic real-world data sets with a high degree of accuracy. This type of data can be used to test new software applications, or to create models of how a system will behave under different conditions.
Synthetic image data & synthetic video data are often used in machine learning applications like computer vision. This type of data can be used to train machine learning models for object detection and anomaly detection.
Why use synthetic data?
There are many advantages of synthetic data over real data. One advantage is that synthetic data can be generated much faster than real data. This is because there is no need to collect and process the data. Synthetic data can also be generated in large quantities. This is important for tasks such as training machine learning models which require a lot of data.
Another advantage of synthetic data is that it can be generated exactly to specification. This means that if you want to create a dataset with specific properties, such as certain types of noise or outliers, this can be easily done with synthetic data. With real data it can be very difficult or even impossible to find a dataset with the right properties.
A final advantage of synthetic data is that it can be used to generate datasets which are private and confidential. This is because the synthetic data is not created from real world data and so cannot contain any personal information. This is important when working with sensitive datasets such as medical records.
No data privacy concerns
The EDPS (European Data Protection Supervisor) puts it as follows:
Positive foreseen impacts on data protection:
- Enhancing privacy in technologies: from a data protection by design approach, this technology could provide, upon a privacy assurance assessment, an added value for the privacy of individuals, whose personal data does not have to be disclosed.
- Improved fairness: synthetic data might contribute to mitigate bias by using fair synthetic datasets to train artificial intelligence models. These datasets are manipulated to have a better representativeness of the world (to be less as it is, and more as society would like it to be). For instance, without gender-based or racial discrimination.
Extreme scalability and faster iteration
Synthetic data is computer-generated data that can be used to train machine learning models. Once optimized the process of generating synthetic data is extremely scalable and is therefore very cost efficient. It also allows for very fast iterations. This is very important in the fast-paced world of technology, where you want to iterate as quickly as possible.
- Extreme scalability: With synthetic data, it is possible to generate an unlimited amount of training data. This is particularly useful for deep learning models, which require large amounts of training data.
- Faster iteration: Synthetic data can be generated quickly, which enables faster iteration when developing machine learning models. This can help machine learning engineers deliver better solutions in less time.
Real data can be rare - Data collection is expensive
Real data can be rare, and it can be costly to acquire. It can also be difficult to find real data that is representative of the population you are trying to model. Synthetic data, on the other hand, can be generated at a fraction of the cost of real data.
The ability to create data for so-called “black swans” makes synthetic data very powerful. The black swan theory was coined by statistician and author of Fooled by Randomness Nassim Nicholas Taleb.
But Black swans are not rare on an aggregated level. They are more common than you would intuitively think. Pandemics, crashing housing markets or war in Europe seem like such unique events that they must be uncommon. But less media-attractive firsts and rare events happen all the time. As such, you should also expect black swans events. With the generation of synthetic data, you can very easily prepare and train your models for such events.
Synthetic data is fully user-controlled
Synthetic data is generated by algorithms, not people. That means it can be created to match any specifications the user desires, making it much more useful than real data.
If you’re looking for a specific type of data, chances are you can find synthetic data that fits your needs exactly. And because synthetic data is generated by computers, it can be created in large quantities quickly and easily.
Real data, on the other hand, is collected from the actual world and thus is limited by what does and does not exist in the world. It can also be time-consuming and expensive to gather enough real data to accurately represent a population or test a hypothesis.
With synthetic data, you have complete control over what variables are included and how they’re distributed. That makes it ideal for testing hypotheses and developing models without worrying about real-world constraints.
Where can you apply synthetic image data?
There are many uses for synthetic data. One example of the use of synthetic data is the development of computer vision models. Especially object detection and anomaly detection models depend on large amounts of image data. This is exactly where the generation of synthetic data can improve the performance of computer vision models.
As already mentioned, the potential of synthetic data and thus the number of possible applications is enormous. We have compiled a list of the 6 most popular fields of application.
- Transport
- Healthcare
- Production
- Construction
- Agriculture
- Retail trade
Conclusion
Synthetic data allows for faster, more flexible, and scalable data generation. In addition, it can also be used to model and generate information that doesn’t exist in the real world.
For many Fintech companies, anticipating market trends and potential financial crises is essential. Using synthetic data allows data scientists to make well-informed forecasts before anything happens which give them time to prepare ahead of time. In finance as well as various other fields like medicine or engineering, synthetic data has made it possible for scientists to solve problems from ‘what if’ scenarios to modeling alternate outcomes that are simply not possible with real-world records alone.
Having synthetic data makes our future–a world that is powered by technology–more manageable and adaptable. Synthetic data allows data scientists to perform new and inventive things that would be impossible with only real-world data, feeding the models that will influence how we all live in our data-driven future.
