BANGALORE, India , Aug. 9, 2023 /PRNewswire/ -- Synthetic Data Generation Market by Component (Solution, Services), by Deployment Mode (On-Premise, Cloud), by Data Type (Tabular Data, Text Data, Image and Video Data, Others), by Application (AI Training and Development, Test Data Management, Data Sharing and Retention, Data Analytics, Others), by Industry Vertical (BFSI, Healthcare and Life Sciences, Transportation and Logistics, Government and Defense, IT and Telecommunication, Manufacturing, Media and Entertainment, Others): Global Opportunity Analysis and Industry Forecast, 2022-2031.
The global synthetic data generation market was valued at USD 168.9 Million in 2021, and is projected to reach USD 3.5 Billion by 2031, growing at a CAGR of 35.8% from 2022 to 2031.
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Major Factors Driving the Growth of Synthetic Data Generation Market:
The market is expanding as a result of an increase in digitalization transformation across companies and an increase in the adoption of cutting-edge technologies like AI and ML. The market is also anticipated to benefit from lucrative growth prospects in the coming years due to the rise in demand for connected devices, IoT, and other technologies.
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TRENDS INFLUENCING THE GROWTH OF SYNTHETIC DATA GENERATION MARKET:
Stricter laws like GDPR and HIPAA have been created in response to the growing demand for data privacy and security. A solution is provided by synthetic data, which generates plausible but artificial data while preserving the statistical characteristics of genuine data and protecting sensitive information. In sectors like healthcare and banking that deal with sensitive data, this element is especially crucial.
The creation and training of AI and machine learning models may be hampered by the lack of and difficulty in gaining access to vast, diverse datasets. This problem is addressed by synthetic data creation, which offers a scalable method for producing different datasets that represent a range of scenarios and edge cases. This helps to enhance the generalization and resilience of AI models, which is essential for systems like autonomous vehicles and image recognition systems. Additionally, the requirement for testing and innovation in technology development happens quickly. Companies can replicate a variety of situations using synthetic data without being constrained by the limits of real-world data collecting. This shortens the development lifecycle, lowers expenses, and makes it possible to test new features and products quickly.
In businesses where genuine data collection is risky, expensive, or logistically difficult, synthetic data is very useful. For instance, in the healthcare industry, simulating medical scenarios can improve patient safety and lower risks when creating medical devices and educating medical workers. Another important element fueling the expansion of synthetic data generation is the rise of Industry 4.0 and the Internet of Things (IoT). Massive volumes of data are produced by IoT devices, making it difficult to conduct testing using real data. In order to ensure the functioning and security of IoT systems, synthetic data offers a solution for the creation of various testing environments.
Furthermore, efficient training requires a lot of data due to the increasing complexity of AI models like deep neural networks. Without the requirement for intensive data collecting activities, synthetic data augmentation approaches can aid in increasing the training dataset and enhancing model performance.
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SYNTHETIC DATA GENERATION MARKET SHARE ANALYSIS
The projection period is expected to see the highest growth in Asia-Pacific. This is ascribed to an increase in the usage of cloud-based services and improved technology, which both drive the growth of the synthetic data production market in this area.
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Key Companies:
- Datagen
- Meta
- Synthesis AI
- Mostly AI
- NVIDIA Corporation
- IBM Corporation
- Microsoft Corporation
- com, Inc
- CVEDIA Inc
- Gretel Labs
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