Synthetic Data Generation Market Analysis

Data Type (Tabular Data, Text Data, Image & Video Data)

Based on data type, tabular data in the synthetic data generation market is anticipated to hold largest revenue share of about 50% during the forecast period. Recently, privacy concerns have made it difficult for businesses to get real-life data. Due to these difficulties, synthetic data that resembles real data is produced and can be kept in an organized tabular manner. This increases the need for tabular data, which is anticipated to increase at a notable CAGR over the course of the projected period. Businesses can improve operational data security and privacy by utilizing Generative Adversarial Networks (GANs) to create synthetic tabular data.
Research analysts predict that by 2030, the use of artificial tabular data to train AI models will expand at a rate that is around three times faster than that of real structured data.

Application (AI Training & Development, Test Data Management, Data Sharing & Retention, Data Analytics)

Based on application, test data management segment in the synthetic data generation market is attributed to hold largest share of about 35% during the forecast period. The market will be driven by the requirement for representative, varied, and high-quality data for testing and validation. Synthetic data can help businesses improve the efficacy and efficiency of their testing procedures, which will improve product quality, accelerate time-to-market, and save costs compared to standard test data management techniques. Due to the test data manager's growing requirement for the lowest collection of data for data testing and data masking, this market segment has the biggest share. It also seeks to avert GDPR-related legal issues. Due to the challenge’s businesses face when exchanging data across borders, the corporate data sharing market is expanding significantly.

Our in-depth analysis of the global synthetic data generation market includes the following segments: