Why Use Synthetic Data?: 3 Critical Necessities and 3 Stellar Excellences
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Synthetic Data – Many people believe that synthetic data is only needed when the amount of real data is small. However, that is a misunderstanding of data. Synthetic data has value beyond being a supplement to real data. In this post, I will talk about three necessities and three advantages of why artificial generated data is used even in situations where there is a lot of real data.
Needs for Synthetic Data
1. Personal information protection
Data protection is an important issue. Many legal and ethical issues can arise when using real data containing personal information. If you use generated data, you can effectively lower the probability of leaking information contained in real data.
2. Data accessibility
Certain types of data can be difficult to access, and data collection can be very expensive. In these cases, generating synthetic data is a quick and easy way to generate the data you need.
3. Diversity and complexity of data
Artificial generated data can model a variety of scenarios that do not appear in real data. This can improve the diversity and complexity of your dataset.
Advantages of synthetic data
1. Quality and accuracy control
Artificial generated data allows precise control over specific characteristics and patterns during its creation. In other words, generated data can be used to improve data quality and accuracy and optimize data for specific research or development purposes.
2. Provide a safe testing environment
Generated data can provide a secure testing environment that mimics real operating environments. For example, if you need to test a system in the financial, medical, or automotive industries, you can create an environment similar to the real environment by generating data to enable effective testing while reducing risk.
3. Scalability
Real data takes a lot of time and money to collect and preprocess, but synthetic data can be easily generated in the desired form as needed, allowing various conditions to be simulated. In other words, it has wide expandability.
Artificial generated data addresses needs such as privacy, data accessibility, and increased diversity and complexity of data. Additionally, this allows us to provide a variety of test environments. Using synthetic data, safer and more efficient research and development will be possible.
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