The latest adage of data being the new oil is true, and just like your regular fuel, it is becoming hard to come by.
Yet, real-world data fuels any organization’s machine learning and AI initiatives. However, getting quality training data for their projects is a challenge. It is because only a few companies can access a data stream while the rest makes their own. And this self-made training data called synthetic data is effective, inexpensive, and available.
But what exactly is synthetic data? How can a business generate this data, overcome the challenges and leverage its advantages?
What is Synthetic Data?
Synthetic data is computer-generated data fast becoming an alternative to real-world data. Instead of being gathered from real-world documentation, computer algorithms generate synthetic data.
Synthetic data is artificially generated by algorithms or computer simulations that statistically or mathematically reflect real-world data.
Synthetic data, according to research, has the same predictive properties as actual data. It is generated by modeling the statistical patterns and properties of real-world data.
Industry Trends?
According to Gartner research, synthetic data could be better for AI training purposes. It is being suggested that synthetic data could sometimes prove more beneficial than real data collected from actual events, people, or objects. This synthetic data efficiency is why deep learning neural network developers are increasingly using it to develop high-end AI models.
A report on synthetic data predicted that by 2030, most of the data used for machine learning model training purposes would be synthetic data generated through computer simulations, algorithms, statistical models, and more. However, synthetic data accounts for less than 1% of the market data currently, however by 2024 it is expected to contribute more than 60% of all the data generated.
Why Use Synthetic Data?
As advanced AI applications are being developed, companies find it difficult to acquire large quantities of quality datasets for training ML models. However, synthetic data is helping data scientists and developers tide over these challenges and develop highly credible ML models.
But why make use of synthetic data?
The time needed to generate synthetic data is much less than acquiring data from real events or objects. Companies can acquire synthetic data and develop a customized dataset for their project more quickly than real-world dependent datasets. So, within a concise period, companies can get their hands on annotated and labeled quality data.
For example, suppose you need data about events that rarely occur or those that have very little data to go by. In that case, it is possible to generate synthetic data based on real-world data samples, especially when data is required for edge cases. Another advantage of using synthetic data is it eliminates privacy concerns as the data is not based on any existing person or event.
Augmented and Anonymised Versus Synthetic Data
Synthetic data shouldn’t be confused with augmented data. Data augmentation is a technique developers use to add a new set of data to an existing dataset. For example, they might brighten an image, crop, or rotate.
Anonymized data removes all personal identifier information as per governmental policies and standards. Therefore, anonymized data is highly crucial when developing financial or healthcare models.
While anonymized or augmented data are not considered part of synthetic data. But developers can make synthetic data. By combining these two techniques, such as blending two images of cars, you can develop a completely new synthetic image of a car.
Types of Synthetic Data
Developers use synthetic data as it allows them to use high-quality data that masks personal confidential information while retaining the statistical qualities of real-world data. Synthetic data generally falls into three major categories:
Fully Synthetic
It contains no information from the original data. Instead, a data-generating computer program uses certain parameters from the original data, such as feature density. Then, using such a real-world characteristic, it randomly generates estimated feature densities based on generative methods, which ensures complete data privacy at the cost of data actuality.
Partially Synthetic
It replaces certain specific values of synthetic data with real-world data. In addition, partially synthetic data replaces certain gaps present in the original data, and data scientists employ model-based methodologies to generate this data.
Hybrid
It combines both real-world data and synthetic data. This type of data picks random records from the original dataset and replaces them with synthetic records. It provides the benefits of synthetic and partially synthetic data by combining data privacy with utility.
Use Cases for Synthetic Data?
Although generated by a computer algorithm, synthetic data represents real data accurately and reliably. Moreover, there are many use cases for synthetic data. However, its use is acutely felt as a substitute for sensitive data, especially in non-production environments for training, testing, and analysis. Some of the best use-cases of synthetic data are:
Training
The possibility of having an accurate and reliable ML model depends on the data it is being trained on. And, developers depend on synthetic data when real-world training data is hard to come by. Since synthetic data increases the value of real-world data and removes non-samples (rare events or patterns), it helps increase AI models’ efficiency.
Testing
When data-driven testing is critical to the development and success of the ML model, synthetic data must be used. The reason being synthetic data is much easier to use and faster to procure than rule-based data. It is also scalable, reliable, and flexible.
Analysis
Synthetic data is free from bias that is typically present in real-world data. It makes synthetic data a much-suited dataset for stress-testing AI models of rare events. It also analyses the data model behavior possible.
Advantages of Synthetic Data
Data scientists are always looking for high-quality data that is reliable, balanced, free of bias and represents identifiable patterns. Some of the advantages of using synthetic data include:
- Synthetic data is easier to generate, less time-consuming to annotate, and more balanced.
- Since synthetic data supplements real-world data, it makes it easier to fill data gaps in real-world
- It is scalable, flexible, and ensures privacy or personal information protection.
- It is free from data duplications, bias, and inaccuracies.
- There is access to data related to edge cases or rare events.
- Data generation is faster, cheaper, and more accurate.
Challenges of Synthetic Datasets
Similar to any new data collection methodology, even synthetic data comes with challenges.
The first major challenge is synthetic data doesn’t come with outliers. Although removed from datasets, these naturally occurring outliers present in real-world data help train the ML models accurately.
The quality of synthetic data can vary throughout the dataset. Since the data is generated using seed or input data, synthetic data quality depends on the quality of seed data. If there is bias in the seed data, you can safely assume that there will be bias in the final data.
Human annotators should check synthetic datasets thoroughly to ensure accuracy by using some quality control methods.
Methods for Generating Synthetic Data
A reliable model that can mimic authentic dataset has to be developed to generate synthetic data. Then, depending on the data points present in the real dataset, it is possible to generate similar ones in the synthetic datasets.
To do this, data scientists make use of neural networks capable of creating synthetic data points similar to the ones present in the original distribution. Some of how neural networks generate data are:
Variational Autoencoders
Variational autoencoders or VAEs take up an original distribution, convert it into latent distribution and transform it back into the original condition. This encoding and decoding process brings about a ‘reconstruction error’. These unsupervised data generative models are adept at learning the innate structure of data distribution and developing a complex model.
Generative Adversarial Networks
Unlike variational autoencoders, an unsupervised model, generative adversarial networks, or GAN, is a supervised model used to develop highly realistic and detailed data representations. In this method, two neural networks are trained – one generator network will generate fake data points, and the other discriminator will try to identify real and fake data points.
After several training rounds, the generator will become adept at generating completely believable and realistic fake data points that the discriminator won’t be able to identify. GAN works best when generating synthetic unstructured data. However, if it’s not constructed and trained by experts, it can generate fake data points of limited quantity.
Neural Radiance Field
This synthetic data generation method is used when creating new views of an existing partially seen 3D scene. Neural Radiance Field or NeRF algorithm analyses a set of images, determines focal data points in them, and interpolates and adds new viewpoints on the images. By looking at a static 3D image as a moving 5D scene, it predicts the entire content of each voxel. By being connected to the neural network, NeRF fills missing aspects of the image in a scene.
Although NeRF is highly functional, it is slow to render and train and might generate low-quality unusable images.
So, where can you get synthetic data?
So far, only a few highly advanced training dataset providers have been able to deliver high-quality synthetic data. You can get access to open-source tools such as Synthetic Data Vault. However, if you want to acquire a highly-reliable dataset, Shaip is the right place to go, as they offer a wide range of training data and annotation services. Moreover, thanks to their experience and established quality parameters, they cater to a wide industry vertical and provide datasets for several ML projects.
