Synthetic data is a powerful tool for data scientists, as it allows them to generate data on demand. This can be text for natural language processing, images, audio or rows and columns of tabular data.

These methods also help businesses comply with privacy regulations, such as GDPR and California’s CPPA. They also allow them to uncover valuable insights without revealing private information.

Cost-effectiveness

A growing number of companies are turning to synthetic data as an alternative to real-world raw data. Using this type of data can help them save time and money while protecting their sensitive customer information. It can also help them avoid costly fines and settlements.

In addition to being cost-effective, synthetic data can be used to create and test machine learning models without compromising real data or violating privacy laws. Several startups are providing specialized artificial data services, with many focusing on specific markets or techniques. A few specialize in healthcare, for example, by creating privacy-preserving digital copies of patient data that are then used to train AI models.

Some of these solutions use generative algorithms to model real-world scenarios and then generate synthetic data based on that model. This process can be more cost-effective than traditional methods like Monte Carlo. However, the resulting data is still only an approximation of the original dataset. As a result, it may not be suitable for some use cases.

Scalability

Data scientists need large amounts of high-utility data to test and train machine learning models. However, access to real raw data is often limited due to privacy regulations and the time-consuming process of de-identification. In these cases, synthetic data can be a powerful tool.

The scalability of synthetic data makes it possible for companies to create safe, low-risk data sets without violating data retention policies. This can save significant costs, improve operational efficiency, and reduce risk exposure. In addition, scalability can help data scientists develop better algorithms.

Synthetic data can be used in a variety of applications, including testing software and hardware. For example, a graphics rendering engine can generate a virtual environment for robots to explore and interact with, or simulate dominoes that must be positioned in a certain way to stack correctly. This kind of data can also be used to test security systems. For example, an attacker could attempt to reenter a network after a password breach by using brute force on a dictionary or a list of common words.

Privacy

Synthetic data is an excellent alternative to real-world data sets for testing and development. It helps businesses abide by strict privacy regulations such as HIPAA, GDPR, CCPA and more. This enables them to use sensitive data without impacting their customers’ privacy. It also helps them avoid risky operations and costly mistakes that come with real-world data.

Moreover, it can be used to fill gaps in the available data set. For instance, it is difficult to collect rare cases in real data and may be impractical or unethical to do so. Synthetic data can fill this gap, making model training more heterogenous and robust.

This is especially true for text-based synthetic data, which can be used to train chatbots and machine translation algorithms. It can also be used to generate tabular data. This type of data is important in a variety of fields, including computer vision, speech recognition and self-driving cars. It also allows startups to level the playing field with established players like Google’s Waymo, which has spent billions of dollars on collecting real-world driving data.

Reliability

Many companies use synthetic data generation to train their machine learning models. They do so in order to avoid compromising real, personal customer data. This is especially true for companies in regulated industries, such as finance and healthcare.

Another benefit of this kind of data is that it can be generated much faster than real-world data. It can also be more accurate, because it is free from human error and biases. This makes it ideal for testing new algorithms.

Several different methods can be used to generate synthetic data, but the most popular are generative adversarial networks (GANs). GANs consist of two sub-models, a generator and a discriminator, which work against each other. They can be used to create a variety of data types, including tabular and time-series.

Businesses can also use machine learning to fit a distribution of their actual data and then use that model to generate random data. Decision trees and autoregressive models are examples of these methods.