It’s 2024, and according to Gartner, by this year, more than 60% of the data used to train AI and ML models will be generated artificially. Why is that? Because real data has its concerns. Real data needs to be protected. Think about it. You have your whole history written in hospital documents and it got leaked or used for a data science experiment. Would you be ok with it? Not.
Especially in the health sector, there are some strict privacy laws against the use of real data in any experiment: The Health Insurance Portability and Accountability (HIPAA) Act is a federal law that claims that individual information should be protected and not be discriminated against.
For this reason, a substitute for real data, that is called synthetic data, has been used to fill the gap that is created due to the unavailability of real data. And the process of generating synthetic data sets, statistically and mathematically, is known as ‘Synthetic test data generation.’
Importance of synthetic data generation
Synthetic data is the way of the future when it comes to data-driven decision-making. Generating realistic yet artificial data allows organizations to explore new opportunities and drive innovation without sacrificing privacy and security. This guide will provide you with an in-depth look at synthetic data generation – what it means, what it can be used for, and the real-world benefits it provides.
Whether you’re a data scientist, a business analyst, or an executive looking to use data to power your business, this guide will help you get started.
Why do we need synthetic data generation?
In a world where everything is fact-based, useful data plays an important role in many areas, especially in AI and ML. However, real data has its limitations. Privacy concerns, insufficient information, and ethical considerations can impede the development of robust AI models. That’s where synthetic data generation comes in.
What is synthetic data?
“Synthetic data is an artificially generated data that has the same attributes as real data both statistically and mathematically”
Synthetic data can be structured as rows and columns of data, or it can be unstructured as images, videos, or text. You can use it to create an “authentic” data set by building realistic data simulations, similar to what you would find in a real data set. Synthetic data is not a “replica,” but it can be a powerful substitute for the characteristics and relationships that you need in a real record. This means that you can create robust, flexible records that you can use for a wide range of things, especially when training a machine learning model.
The process behind synthetic data generation
Synthetic data can be generated in a variety of ways with no restrictions on size, time, or location. Data can be generated from real events, objects, or people through computer simulation or algorithms.
One of the most common ways to produce synthetic data is through the use of open source synthetic data generation tools. These tools are available free of charge or at a cost. The tools are used to create the synthetic dataset.
When a scientist uses a data generation tool and goes through the data generation process, he or she models off the information that has already been created from the real-world dataset and works from that to generate a new dataset.
Methods to create data synthetically
There are several methods commonly used to generate synthetic data:
1- Variational Autoencoders (VAE)
VAEs belong to the class of generative models where an encoder-decoder network pair is trained to reconstruct data distribution.
2- Statistical Modeling
This method includes finding statistical distributions that will fit the real data well and then synthesizing new data points from these distributions so that they inherit the same statistical properties.
3- Rule-Based Methods
This group of methods have a set of rules under which the generation of synthetic data falls. This technique is usually applied to come up with structured sets of data having clear relationships between attributes.
4 – Generative Adversarial Networks (GANs)
GAN creates fake images that are indistinguishable from real ones. Despite its simplicity, GANs find wide application during model training as they can produce realistic high-fidelity representations.
Generative Adversarial Networks (GANs) is a machine learning technique that consists of two neural networks in its setup. The goal of the generator network is to fabricate fake outputs; let’s take the instance of flowers. Real random flowers are fed to this network, and it creates artificial flowers as output.
5- Copula-Based Methods
Copulas are tools of mathematics, which permit the separate modeling of marginal distributions and dependence structures within data. This technique is especially helpful in generating artificial data sets with intricate relationships between variables.
Synthetic data sets
A synthetic data set refers to a set of artificially created data points that imitate the statistical features and connections found in real datasets. These types of sets can serve various functions including:
• Building and testing machine learning models
- Software application testing and simulation
- Data analytics and visualization
- Forecasting risk assessment
Types of Synthetic Data
Synthetic data has been used in multiple sectors because of its availability, accuracy, and ability to generate large training data set to train neural networks in no time, plus its cost-effectiveness is a cherry on top. And the reason why data scientists are preferring artificial data is because it saves them the hassle and effort of manual data labeling.
There are three main types of synthetic data that we create as a substitute to real data.
- Fully synthetic
- Partially synthetic
- Hybrid
1- Fully synthetic data
In this technique, data scientists produce synthetic data sets without inducing real data. They simply produce artificial data by using the density function of the features or the characteristics of real data to generate data sets that statistically resemble real data.
This type of fully synthetic data offers a high level of privacy because the data sets are generated randomly. Therefore sensitive information has been protected and preserved.
2- Partially synthetic data
In this technique, the data is not truly synthetic. Data scientists carefully replace some of the highly sensitive columns in the original data due to privacy concerns to protect newly generated data. Therefore synthesizing only a small part of data by retaining the one-to-one mapping between original and synthetic data.
3- Hybrid data
Hybrid data provides a high level of security and privacy protection of valuable data. Because it is generated by combining both real-world and synthetic data sets. This type of data contains both private information as well as synthetic values. With its lots of benefits, there comes the drawback too. Hybrid data has high memory usage and long processing times.
Benefits of synthetic data generation
Why is artificially generated data most popular among data scientists? Let explore some key benefits of synthetic data generation over real-world data:
1- Data Augmentation:
The field of data science is booming. Every day scientists are performing new experiments. For that, they need a consistent supply of data sets. However real data is limited in size and number. On the other hand, synthetic data generation has no limit. You can generate thousands of data sets without any privacy and security concerns. And these synthetic data sets then feed ML models that require large volumes of data to perform optimally.
2- Privacy protection:
Synthetic data offers a high level of privacy protection. Because synthetic data sets are not only anonymized but also align with the privacy regulations of GDPR (General Data Protection Regulation) and CCPA (California Consumer Privacy Act), because they do not contain real-private information about individuals.
3- Stress testing and edge case exploration:
With synthetic data, you have a controlled environment in which you can test algorithms under different scenarios. Such as edge cases that you may not have access to in real data. These edge cases are especially important for stress testing systems in the finance and cybersecurity industries. Where predicting results in hypothetical scenarios is critical.
4- Reduced bias:
Synthetic data not only reduces biases but also enhances data security. It helps create more representative samples that reflect the actual population, lowering the risk of biased results and supporting fairer decision-making.
5- Quicker development cycles:
Lack of data is one of the biggest barriers in AI development. Synthetic data solves this problem by generating a large volume of data sets to train and develop AI and Machine learning models in a faster way.
Considerations for Using Synthetic Data
Apart from the multiple benefits of synthetic data generation, there are still some things to consider.
Explainability
While using synthetic data it’s important to consider the transparency and potential biases the data could create. Therefore, it’s important to check how this specific data is affecting the AI model’s behavior or if it has any potential impact on the results.
Evaluation
Just like we evaluate real data before performing any experiment. We must check the quality of synthetic data. If the data is accurate, uniform, and has all the required properties to generate the results we expected.
Data Quality
The accuracy of the synthetic data depends on the accuracy of the real-time data used to train the generative models. For best results, make sure the source data is correct and representative.
Conclusion
Synthetic data generation has the potential to transform the way we solve data problems in the era of artificial intelligence. AI trained with synthetic data is doing a lot for the betterment of society. With artificial data, we are addressing privacy issues, overcoming data gaps, and accelerating innovation cycles. To ensure a safe future it’s on us to use this technology reasonably and ethically. And it is essential to ensure that this technology is developed and deployed responsibly, with transparency, clarity, and ethical considerations at every step.