In today’s world, data is indeed driving the world and when dealing with sensitive data, it is an obligation to make privacy paramount. Anonymization is no longer enough to guarantee data privacy. To ensure that the data-driven activities in the organizations put no individuals at risk, there’s a need for realistic data that doesn’t link back to original user data.
Synthetic data is data artificially generated by an algorithm that’s been trained on a real data set. The algorithm essentially creates new data that has all the same characteristics of the original data — leading to the same answer, but it’s impossible to get back to the original data used, from either the algorithm or the synthetic data it has created. Synthetic data is a boon for organizations & researchers who work extensively with data.
The first major benefit of synthetic data is its ability to support machine learning/deep learning model development. Synthetic data assists in faster iteration of model training and experimenting. Using synthetic data ML practitioners gain complete control over the dataset.
The second major benefit of synthetic data is that it can provide data privacy. Real data contains sensitive and private user information that cannot be freely shared and is legally bound. Synthetic datasets can be more openly published, shared, analyzed, without revealing the original data.
Use of Synthetic Data in Machine Learning and AI:
Machine Learning (ML) and Artificial Intelligence (AI) help develop many industries worldwide. A successful AI project cannot be run without a high-quality, diverse, and unbiased dataset. The challenges for most companies are:
They don’t have enough real-world data.
They have data but the quality is not good.
They can’t use data due to privacy regulations.
Many projects fail due to these obstacles even before they start. Let’s look at the most common obstacles:
Data held up due to lengthy data access procedures: Machine Learning models need a lot of training data to provide viable outcomes. This can be problematic, because before using real data for ML purposes, the company must go through lengthy & time-consuming data access procedures that can take up to 6 months. As a result, AI/ML projects can get either postponed or can fail.
Data bias problem: Bias in Machine Learning is an error that results from wrong assumptions in the learning algorithm. To build a ML application, you’d have to get a strong pattern and separate the data into different clusters that have specific characteristics. The bias problem doesn’t only result in AI inefficiencies, but it can also reinforce discrimination.
Data held up due to privacy regulations: Real data could serve ML algorithms to solve many business problems. But Personally Identifiable Information (PII) or Personal Health Information (PHI) is also subject to various privacy regulations such as General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), or Health Insurance Portability and Accountability Act (HIPAA). These regulations restrict how you can collect and use real world data.
Synthetic Data help resolve these problems and enhance the performance of AI projects:
Synthetic data opens several possibilities for AI projects that make use of ML algorithms.
Synthetic dataset resembles the quality of the real-world sensitive data and retains the statistical distribution.
The use of synthetic data generation depends on the use case and industry.
Synthetic data can be generated using GANs (Generative Adversarial Networks), VAEs (variational autoencoders) or the combination of both.
Before using the data in ML algorithms, data science teams spend some time cleaning the data. This process is time-consuming and is crucial in determining the success of the AI projects. The generation of synthetic data can help streamline the data cleaning process.
Synthetic data is also ready-to-use and hence it doesn’t need cleaning or formatting.
Synthetic data can revolutionize Machine Learning algorithms and speed up AI projects.
Role of Synthetic Data in Removing Privacy Constraints:
Synthetic data contains no personally identifiable information and hence poses no risk to user privacy. Also, it’s not subject to any existing privacy regulations.
The compliance process can be streamlined by creation of synthetic data with the right privacy guarantees. The legal constraints around data processing are much lenient because privacy-preserving synthetic data doesn’t contain real world data or sensitive personal data.
Use of synthetic data opens new cooperation possibilities for organizations and makes collaboration with a third party easy.
With synthetic data, financial institutions can operate on safe and compliant financial datasets.
Other Benefits of Using Synthetic Data:
Address the current challenges with synthetic data will help companies to gain a competitive edge and will help them to:
Operate more on autopilot.
Contribute to new findings.
Come up with more accurate, case-tailored predictions for the future.
Overcome Data Scarcity: Synthetic data can help to solve the common problem of data scarcity. Without sufficient data, training AI models is very difficult. Data is typically difficult to procure and time-consuming. In some cases, data is highly regulated.
Grow their business by adopting a viable option. Synthetic data produces quality data sets that can be scaled up as required, and at a very reasonable cost. Smaller organizations can compete with far larger ones. Producing this data is far more cost-effective than gathering information from the real world.
Move Synthetic datasets to the cloud, which is a more cost-effective option than on-premises hosting.
Greater & efficient use of synthetic data will give rise to federated learning that allows organizations to create intelligent systems trained on other entities’ data sets, democratizing data while respecting privacy and security.