What is Synthetic Data?

Gathering data from the real world can be a challenging, costly and lengthy process, however, synthetic data technology allows users to generate the data quickly, conveniently and digitally in whatever quantity they require, tailored to their precise needs.

Synthetic data is important because it allows organizations to create accurate datasets from scratch, without having to rely on real-world data which can often be incomplete, biased or inaccurate. Synthetic data can be generated quickly and cost-effectively, and can be used for testing machine learning models, training AI systems, and simulating customer journeys without any privacy risks.

Synthetic data is heavily employed in the training of neural networks and ML models, as the developers of these models require labeled data sets that vary in size from a few thousand to tens of millions of items. By artificially generating data that mimics real data sets, companies can generate a substantial amount of training data without great expense or effort.

Synthetic data can be a beneficial tool for safeguarding user privacy and adhering to privacy regulations, especially when dealing with delicate health and individual information. Furthermore, it can help reduce any prejudice in data sets by guaranteeing that consumers are provided with varied data that reflects reality.

Generally, it involves creating datasets from scratch by combining real data with artificial data, or by using algorithms that generate data that mimics real-world scenarios.

Which Techniques are Used to Generate synthetic data?

Here are three techniques that are often employed to generate synthetic data:

• It can be generated quickly and cost-effectively

• It can be used to train machine learning models

• IIt can be used to test systems and applications in a safe and controlled environment

• It can be used to create large datasets for research

• IIt can be used to develop data-driven products and services

• IIt can be used to create realistic simulations and scenarios

What are the Disadvantages of Synthetic Data?

Synthetic data offers many advantages, including being customizable, cost-effective, and allowing for faster production and complete annotation. It also provides data privacy, as it does not contain any information that could be used to identify real data, and users have full control over the data set. This makes it suitable for dissemination and can be especially useful for industries such as healthcare and pharmaceuticals. However, synthetic data does come with some drawbacks, such as inconsistencies and an inability to replace authentic data.

• Data augmentation

• Data privacy/anonymization

• Data exploration

• Data balancing

Synthetic Data is used in Machine learning model training and testing

• AI/ML model training: synthetic data is used to train AI/ML models, providing improved performance and bias elimination.

• Testing: synthetic test data is used to test applications, websites and software, offering scalability, flexibility and realism.

• Privacy regulations: synthetic data enables organizations to abide by data privacy laws and regulations, such as the Health Insurance Portability and Accountability Act, General Data Protection Regulation and California Consumer Privacy Act.

• Health and privacy: synthetic data is used to extract health-related information without invading people's privacy. It is also used in data masking techniques to reduce privacy-related risks.

Synthetic data can also be used to supplement real data and generate new datasets that can be used for testing and validation purposes. Synthetic data can also be used to simulate real-world scenarios, such as pandemics or natural disasters, which are too costly or dangerous to study in the real world.

What is a real world example of how synthetic data is used?

Synthetic data allows healthcare data specialists to make record-level data available to the public while still preserving patient privacy.

Data scientists can utilize synthetic data sets, such as debit and credit card payments, from Kaggle to evaluate and test fraud detection systems, as well as create new methods for detecting fraud in the financial sector. These data sets replicate typical transaction data and can help to uncover fraudulent activity.

DevOps teams typically use either synthetic data or data masking techniques for software testing and QA. Synthetic data involves artificially generating data and plugging it into a process without taking real data out of production. However, some experts suggest that data masking techniques may be a better choice than synthetic data due to the complexity of production data sets that can make it difficult to create an accurate representation quickly and cost-effectively.

Synthetic data is cost-effective

Synthetic data offers a cost-effective alternative to manually gathering labeled data. This data is created by algorithms that generate realistic simulations of real-world data. By leveraging synthetic data, ML models can be trained faster, more efficiently, and with greater accuracy than with traditional data sources.

Synthetic data can be used to create data sets which can then be used to train and pre-train Machine Learning models, a process known as transfer learning. These data sets are essential for companies and researchers to progress in the field.

Members of the Data to AI Lab at the Massachusetts Institute of Technology Laboratory for Information and Decision Systems have undertaken research to progress the use of synthetic data in ML. As an example, they have documented the successful results of their Synthetic Data Vault, an ML model which has been designed to create and extract its own synthetic data automatically.

Which companies are using Synthetic Data?

Companies have started to utilize synthetic data techniques in their work. As an example, a Deloitte LLC team was able to create an accurate model by artificially producing 80% of the training data, with the help of real data as the base. Furthermore, computer vision, image recognition, and robotics are a few of the many other areas that have seen an improvement due to synthetic data.

Synthetic data is computer generated data that aims to mimic real-world datasets and can be used to develop, train and test algorithms without the need for real data.

After 2012's groundbreaking ImageNet competition, which is often referred to as the 'Big Bang' of AI, a team of researchers headed by Geoff Hinton achieved a remarkable victory in an image classification challenge. This success demonstrated the potential of neural networks to recognize items faster than humans, prompting researchers to focus more seriously on artificial data.

By using synthetic data, machine learning can eliminate bias, make data accessible to everyone, protect privacy and lower costs. Discover how synthetic data can address the issues of bias and privacy in machine learning.