Synthetic Data: Exploring Definition and Common Use Cases
Understanding Synthetic Data
Synthetic data, generated by artificial intelligence, closely imitates real or original data. It is created through data synthesis, AI data modeling, and complex training data to closely mirror traditional data. Techniques such as generative adversarial networks (GANs) and variational autoencoders (VAEs) are commonly used to generate synthetic data.
Fully vs. Partially Synthetic Data
Fully synthetic data consists solely of artificially generated data, while partially synthetic data includes a combination of real and synthetic data. Partially synthetic data is often generated through multiple imputation methods, such as mean and regression imputation. The choice between fully or partially synthetic data depends on the specific needs and requirements of the organization.
Synthetic Data Use Cases
Synthetic data has various applications in healthcare research and analytics, private and regulated industries, computer vision development, research and development projects, machine learning model development, cybersecurity training, and more.
Benefits of Using Synthetic Data
Using synthetic data offers several benefits, including enhanced data privacy and compliance, supplementing existing datasets, accessible test data, cost savings, and highly scalable data creation.
Drawbacks of Using Synthetic Data
Despite its benefits, synthetic data has some drawbacks to consider. These include limited transparency, difficulty in capturing real-world data complexities, potential for bias in training data and algorithms, and the possibility of overfitting.
Top Synthetic Data Companies
The article highlights some of the top synthetic data companies, such as MOSTLY AI, Syntho, GenRocket, Hazy, and Synthesis AI, each offering their own unique synthetic data generation platforms and services.
Bottom Line: Using Synthetic Data
While synthetic data is a useful tool in various business projects, organizations must be aware of its potential risks, biases, and shortcomings. It is crucial to carefully assess the training data and processes involved in synthetic data generation. Working with a reputable synthetic data company can help ensure accurate and reliable results.
As organizations continue to prioritize data privacy and compliance, the use of synthetic data is expected to grow across a range of industries and applications.
Analyst comment
Positive news: The use of synthetic data is expected to grow across industries due to its benefits such as enhanced data privacy and compliance, cost savings, and accessible test data. However, organizations must be aware of potential risks and biases associated with synthetic data. Top synthetic data companies like MOSTLY AI, Syntho, GenRocket, Hazy, and Synthesis AI offer unique platforms and services for synthetic data generation.
