No one can stress enough how data is one of the most valuable resources these days, not only in business but in our daily lives as well. But like the real world, data also isn’t perfect. It’s hard and costly to collect data, and it comes with its own set of shortcomings, not to mention sensitivity and the complexity of its nature. For those reasons, synthetic data was introduced as an alternative to be used in training machine learning models.
What is synthetic data?
Synthetic data is information that is artificially generated. It’s not taken from real-world happenings, but it’s created with simulations and algorithms. And it’s used to test mathematical models and train data for machine learning methods. Its purpose is to mimic real-world observations and events.
Synthetic data was introduced for multiple reasons, primarily to provide a reliable source of information for data modeling and minimize the costs and uncertainty that come with real data. It is used for testing applications, protecting sensitive data, training machine learning models, and validating systems at scale. Also, real data needs labeling which takes time, a lot of effort, and is costly. Synthetic data comes already labeled, and it’s labeled correctly. It was introduced also to fight data scarcity and unavailability. Most importantly, it contains no sensitive, private, or personal data points or values.
Types of synthetic data
Partial synthetic data
This is a data set that includes synthetic data and real data from existing occurrences and data sets, but it omits sensitive information.
Full synthetic data
This type of synthetic data has no connections to real data and it’s fully synthetic. This means that all the required variables are available, yet the data is not identifiable.
Hybrid synthetic data
This data is partially synthetic and partially real. It contains both, sensitive data and synthetic values, but still provides security in handling data since values can’t be tracked back to the original source.
But, if we look at data in detail, three more types of synthetic data can be identified based on the content and form:
Synthetic text – Used mostly in natural language processing, synthetic text is great since it hides sensitive information.
Synthetic media – This refers to media like videos or images, used mostly in object detection and real-world applications and recognition tasks.
Synthetic tabular data – This type of synthetic data imitates real data for data science projects by providing data structured in rows and columns to fill in the missing values in real data.
Benefits of synthetic data
Synthetic data brings loads of benefits over real-world data, and some we have mentioned thought the post so far. But security, privacy, and reliability stand out the most.
Higher quality data and format of the dataset – As we said before, synthetic data comes at a higher quality than real-world data because it diminishes the possibility of having bad data, missing values, or irregularities. It is created for the purpose to mimic real data but without the fuss of cleaning or labeling data.
Faster project development – Collecting real data is time-consuming. Synthetic data speeds up the process and availability time of quality data, which in turn helps for faster development and a shorter time-to-market.
Increased data privacy and security – Synthetic data means that private information isn’t connected to the real data source and can’t be re-engineered back to an individual.
Lower costs – Creating synthetic data is far cheaper than collecting, cleaning, and transforming real data to fit certain criteria. Even then, the imperfections of such data are what can affect ML models.
Let’s talk about GANs and VAEs
Who doesn’t love a good abbreviation? Especially when it sounds important.Well, if you are diving into synthetic data, you would want to know how, images for example, are generated. Besides, autoregressive models which are dedicated to synthetic time series data, we have Generative adversarial networks (GANs) and Variational Autoencoders (VAEs).
Generative adversarial networks (GANs) consist of two sub-models: generator and discriminator. The generator creates fake data, and the discriminator defines whether the data is fake or real. The discriminator is trained on real data to identify if new data inputs are real or fake. The generator then identifies more realistic data points that the discriminator won’t be able to identify as fake. It’s a sort of circular process where each model works against the other, one creates data until the other can’t distingue real from synthetic.
Variational Autoencoders (VAEs) use GANs but with an additional encoder. They learn from data and use real data points to reconstruct them or to create additional variations. It is similar to real data in structure and characteristics, and it’s highly realistic. Parts of this model are the encoder and decoder.
Why is synthetic data changing the ML and AI game?
If you look at some of the biggest players in the data market, like Google, Meta, and other sites that collect huge amounts of real user data, they have ruled the business so far. This has discouraged small companies from getting into the race. But it all changed when synthetic data entered the game. It leveled up the playing field since no one will be the sole owner of such data. This started the process of democratizing access to data. It also stimulated innovations and AI development as we know it today.
Besides the obvious benefits synthetic data brings, it offers even more possibilities for using it to progress ML and AI models. Certain data limitations that were here before, are getting slowly erased and new capabilities unravel. Imagine a scenario where you are not limited by data, issues, missing values, or bad data! Imagine what kind of ML and AI projects you could do with sufficient reliable data sets! Options are unlimited, and results are more accurate.
Of course, you would want to access ML and AI based on real-world data that represents actual scenarios. That’s true. And you will. But for the purpose of training those models, you will need synthetic data if you want them to work seamlessly, or almost seamlessly. It gets risks and uncertainty connected to real data out of the way. Synthetic data also creates every feasible variation of needed information (text, image, video,…) for models to work properly, which is far easier than collecting every possible scenario or alternative from real environments.
Augmentation vs anonymization vs synthetic data
Data augmentation is used to artificially increase the data set by modifying existing data. It is used mostly in terms of images, by creating variations of an image, we create more data entries, for example, flipping the image, changing colors, etc. So it’s not a completely new data set, but rather a variation of existing ones.
Data anonymization is when we redact or obscure private information from data, and it’s used in text data. This data cannot be traced back to an individual, later on. It’s about protecting personal identity.
So, we can see the difference between those three. Where augmentation and anonymization just slightly differ data from the original or create sort of variations of the existing data set, synthetic data is a completely new set of information. In this case, synthetic data looks like real data but doesn’t compromise privacy while allowing easier sharing and utilization.
Where do we stand
Honestly, if you are still training models on real data, you are bringing in more complexity than needed. The effort you will put in towards cleaning, sorting, and labeling data will come at a cost and a longer time to finish the work you set out to do.
Synthetic data slowly is becoming the norm and a, well, reasonable way to go. It was created with a purpose, to make work easier and to provide unlimited possibilities in ML and AI that will change the way we view the world and help us achieve the impossible. Not only does it help in the business world, but it also allows affecting the well-being of each individual on Earth through health, environmental impact predictions, and social responsibility.
It is said, any ML or AI model or system is as good as the data it was trained on.