With the rapid advancement of technology, data collection and processing methods have significantly changed. This shift has increased the importance of synthetic data and labeled synthetic data. In this article, we will explore the advantages of synthetic data and labeled synthetic data, and explain why they have become popular in the business world.

Advantages of Synthetic Data: Synthetic data refers to data sets that are generated without being derived from real data sources. Here are the advantages of synthetic data:

Creating large data sets: Synthetic data can be used to create large data sets when access to real data sources is limited. This saves time and cost.

Generating complex scenarios: Simulating real-life scenarios can be difficult or expensive. Synthetic data can be used to create scenarios involving sensitive situations such as medical interventions or dangerous conditions.

Creating customized data scenarios: When you want to focus on a specific data set or scenario, synthetic data can be customized and tailored to your needs.

Advantages of Labeled Synthetic Data: Labeled synthetic data refers to labeled synthetic data sets used for training machine learning models. Here are the advantages of labeled synthetic data:

Training machine learning models: Synthetic data facilitates the training of machine learning models when there is no access to real data or an insufficient amount of labeled data.

Simulating rare events: Rare events may have a low probability of occurrence and may be rarely present in real data sets. Using labeled synthetic data, rare events can be simulated, enabling the model to accurately recognize such situations.

Addressing data imbalances: Real data sets may exhibit imbalances among different classes. Labeled synthetic data can increase the representation of underrepresented classes and address these imbalances.

Synthetic data and labeled synthetic data offer several advantages, including creating large data sets, simulating complex scenarios, and facilitating the training of machine learning models. These advantages hold significant importance in areas such as data analytics, artificial intelligence, and autonomous systems within the business world. However, it is important to understand the differences between real and synthetic data and ensure their accuracy.