You might have seen or heard about the testing scenarios. There, real-world data plays an important role. However, with the evolving times, a new strategy has developed, leaving real data behind. That is the usage of synthetic data.
Synthetic data means the information that is generated with the help of AI rather than real-world events.
To put it simply, this artificial data is created using algorithms. With that, you don’t need to use the test data sets of operational or production data.
So, organizations use this data to train their machine learning (ML) models, approve mathematical models and much more.
In this article, we’ll discuss them all. Also, you’ll learn about its importance, examples, use cases, and advantages. Along with that, in the end, we’ll be discussing its historical background, too.
What’s the importance of synthetic data?
Before we start discussing the importance, let’s have a glance at the definition of synthetic data first:
Things get easier to understand when we look at the definition of the synthetic term, which means fake or artificial.
So, synthetic data is a data that looks like real-world data sets, but in actuality, it’s not because it is artificially created.
The use of synthetic data has become popular, and with time, it will keep growing. It’s just because it exceeds the real-world data. According to Gartner, there will be almost 60% of artificially generated data by 2024 to develop AI and analytics.
When it comes to noting down the biggest use of synthesized data, it is primarily for training neural networks and machine learning models. Developers of these models require accurately labeled data sets that could have thousands to millions of items.
That way, companies can generate a significant amount of the training data. Also, it saves their time and money when collecting real data.
The co-founder of AI.Reverie, Paul Walborsky, says that an image from a traditional labeling service that could cost 6$ can be generated artificially with a cost of 6 cents.
The best part of using this data? It can help to keep the sensitive data of the users intact as well as to comply with privacy laws. This is the best call for industries like healthcare. Along with that, it helps to reduce the bias in data sets, which ensures that users can access a large amount of data that reflects the real world.
How to generate synthetic data?
For synthetic data generation, the process may vary in the context of the different synthetic data generation tools, algorithms, and particular use cases. However, there are three major techniques that can be used to generate this data. Let’s delve into all of them:
1. Drawing numbers from a distribution
The first one is about randomly choosing the numbers from a distribution. While this technique doesn’t take full details from real-world data, it can generate a data distribution that closely reflects real-world data properties.
2. Agent-based modeling
This technique is about creating unique agents that interact with each other. So, using this technique can be a big win when inspecting the interaction between different agents such as people, phones, or computer programs in complex systems.
Python packages like Mesa can be really helpful in the development of these agent-based models. Just because these packages are pre-built core components so, using them makes the development process of the models as easy as pie.
3. Generative models
The production of real-world data has statistical properties, right? Using generative models can generate synthetic data sets that mimic the statistical properties of the real data. But that’s not a one-click process. This means that before creating fake data, generative models use training data. During that process, they learn different statistical patterns and then present the final artificially generated stats.
The best examples of generative models are generative adversarial networks and variational autoencoders.
What advantages does synthetic data offer?
Here is a drop-down of the best advantages of AI synthetic data:
Customizable data
Not every organization has the same requirements. That’s why this data can be customized to meet unique requirements. In the above section, we’ve mentioned that we can use synthetic data for machine learning. But that’s not just what it offers; it even goes beyond that. For example, organizations can also use it for quality assurance and software testing purposes. That means killing two birds with a single stone, isn’t it?
Cost-effective
Collecting the real data can demand a lot of cost. For example, the automaker has to pay a significant cost in order to collect real vehicle crash data. On the flip side, with the availability of simulated data, they don’t have to hassle with data collection.
Faster production
Also, you don’t need to gather synthetic data from real events. So, using the right software and technology, you can create data sets in no time. This way, there’ll be a significant amount of testing data in a few moments.
Data labeling
Even when there’s synthetic data, it might not come with labels. Labeling lots of instances manually for tasks like supervised learning takes a lot of time and can have mistakes, too. However, synthetically labeled data can help develop models faster. In addition, it ensures the accuracy of labeling.
Data privacy
Using the production data may leak the users’ private data. But, the characteristic of synthetic data to protect sensitive information makes it a must-have. This is the reason, this data fits best for major industries like healthcare and pharmaceuticals.
Users have full control
What really makes using this data worthwhile is that users have full control of this data. They can manage it in their own ways. For example, ML professionals have control over different factors like the degree of class separations, different sample sizes, and noise levels in the data set.
What are the use cases of synthetic data?
Here are the major areas where the use cases of synthetic data shines:
Testing
Synthetic data is much easier than rules-based tests. With that, you can generate diverse synthetic data. As a result, you can test software with different test scenarios.
AI/ML model training
Whenever you see an AI model, you should thank the synthetic data. This means that AI models get trained with this data. To train the AI model, the real data may not be enough, and that’s where artificially generated data comes in. This is the reason super AI models have appeared in the market.
Synthetic training data enhances the performance of the model. This removes the bias factor and feeds even new knowledge domains in it.
Moreover, it also increases the original data. How? Because of the AI-powered synthetization process.
Privacy regulations
The interesting factor is that when using this data, data scientists stick with data privacy laws. This can be the Health Insurance Portability and Accountability Act and General Data Protection Regulation. So, it keeps businesses safe from lawsuits or hefty fines when dealing with sensitive data for testing.
What are the best examples of synthetic data?
Different industries use synthetic data AI for different purposes. Let’s explore the best examples of this data:
Media data
You must have heard about the development of Amazon Alexa’s language system. That language system is trained on synthetic data. That shows this data is also helpful in generating synthetic audio, videos, and images. This all happens using computer graphics and image processing algorithms.
Text data
2023 is the year when ChatGpt was at its peak. You would also have used this Chatbot. So, chatbots also get trained on this data to mimic real-world data.
Tabular data
This includes synthetically generated data tables that show their best in model training, data analysis, and other applications.
Unstructured data
This data includes images, video, and audio that are used in different fields, such as speech recognition and autonomous vehicle technology. Plus, Google’s Waymo trains its self-driving cars based on this data.
Financial services data
Not to mention, to make others safe from fraud or credit risks, fake data has proved its mettle as the best safeguard. That’s why JPMorgan and American Express are taking help from synthetic financial data to reduce fraud cases.
Manufacturing data
The manufacturing industry is also getting the best of this data world. They use it for predictive maintenance and quality control testing. For example, a German company named Provinzial uses it for predictive analytics.
What is the difference between synthetic data and real data?
Two major industries such as healthcare and financial services reap the benefits of synthetic data techniques to the utmost. The techniques can be used to create synthetic data similar to the real data of the users. When this data becomes available, data professionals use and share it freely.
The best example is when using synthetic data, healthcare professionals enable record-level data publically. But they maintain the confidentiality of the patients.
Similarly, in the financial sector, synthetic data helps to avoid fraudulent activities in debit and credit card payments. With this data, data scientists create different test scenarios to break the system. If the system gets accessed, the data helps to improve that. Furthermore, the scientists also create new edge cases to detect fraudulent actions.
To overview synthetic financial data sets, you can go through a crowdsourced platform, Kaggle. The platform presents analytics competitions and predictive modeling.
On top of that, DevOps teams use synthetic data sets for different purposes, such as QA and software testing. Instead of using the production data, the teams use AI-generated data. The teams also use masking techniques in synthetic data. That’s just because the production data is full of complex relationships. This makes it difficult to create a correct representation in a short time and free of cost.
What’s the role of synthetic data in machine learning?
Synthetic data has gained immense popularity in the realm of machine learning. Since ML algorithms need a vast amount of data to be trained, collecting the labeled training data that is essential can be too costly.
In that aspect, synthesized data come in handy to build data repositories to train ML models. Even companies and researchers can use these repositories for pre-train models. The technique behind all this is “transfer learning”.
Researchers are striving to advance the AI models. Their efforts have come to an end with a light at the end of the tunnel. For example, the data professionals at the Massachusetts Institute of Technology Laboratory have exposed their fruitful results with their Synthetic Data Vault. Now, the vault can create ML models that will automatically generate and offer their own synthetic data.
The representation of these efforts has also pushed many companies to use synthetic data techniques. For instance, professionals at Deloitte LLC have used this data to develop an accurate model. They did that by artificially creating 80% of the training data, whereas real data served as seed data.
As well as robotics and image recognition are also domains where fake data takes the stage.
What is the history of synthetic data?
The origin of synthetic data links with the advent of computing in the 1970s. It was data that made many initial systems and algorithms work. However, they had restricted processing capacity, difficulty in collecting a significant amount of data, and users’ privacy concerns. That’s where synthetic data generation came into being.
Let’s recall the 2012 ImageNet competition, which has another name, “Big Bang of AI“. In that competition, Geoff Hinton led a team of researchers. They won the competition by successfully training an artificial neural network. It was about the image classification challenge. Well, the team used artificial data to train the network, which could identify things faster than humans.