The global Synthetic Data Generation Market size to grow from USD 0.3 billion in 2023 to USD 2.1 billion by 2028, at a Compound Annual Growth Rate (CAGR) of 45.7% during the forecast period.
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The global synthetic data generation market has various applications such as data democratization, AI/ML training and development, data anonymization, test data management, enterprise data sharing, data analytics and visualization, data monetization, and others. The major end-users of the Synthetic Data Generation market include BFSI, Healthcare & Life sciences, Retail & E-commerce, Automotive & Transportation, Government & Defense, IT and ITeS, Energy and Utilities, Manufacturing, and Other Verticals.
Stricter regulations, and limitations on the use of real-world data due to increasing concerns about data privacy and security have created a demand for synthetic data as a viable alternative. Synthetic data generation enables organizations to generate and utilize data without compromising sensitive information, addressing real-world data privacy and security challenges. Businesses are increasingly relying on data-driven decision-making to gain a competitive edge.
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Among vertical, the BFSI segment is expected to dominate the market during the forecast period
Based on vertical, the BFSI segment of the synthetic data generation market is projected to hold a larger market size during the forecast period. The adoption of synthetic data generation drives the BFSI (Banking, Financial Services, and Insurance) vertical due to increasing concerns about data privacy and compliance regulations. Synthetic data provides a solution for generating realistic datasets without compromising sensitive information, allowing organizations in the BFSI sector to meet regulatory requirements. It enables improved risk management, fraud detection, model development, and customer analytics, facilitating more accurate predictions.
By data type, image and video segment to record the highest market share during the forecast period
Image and video data represent visual information in the form of images and videos. Synthetic data generation for image and video data involves creating artificial visual content that simulates real-world scenarios. This process is driven by the need for training computer vision models, object detection, image recognition, and video analysis. Synthetic image and video data enable organizations to generate diverse datasets that cover a wide range of scenarios, lighting conditions, and object variations. It supports the development and validation of algorithms for autonomous vehicles, surveillance systems, medical imaging, and virtual reality applications.
Asia Pacific to record the highest growth during the forecast period.
The synthetic data generation market in the Asia Pacific region is experiencing significant growth driven by rapid digital transformation, increasing data privacy regulations, growing adoption of AI and ML technologies, rising cybersecurity concerns, and a thriving startup ecosystem. Organizations in the region are leveraging synthetic data generation to address data-driven challenges, comply with regulations, enhance AI and ML model performance, strengthen cybersecurity measures, and drive innovation. With the region's focus on digitalization and the emerging need for data-driven solutions, Asia Pacific's synthetic data generation market is poised for continued expansion and opportunities.
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Market Players
Major vendors in the synthetic data generation market include Microsoft (US), Google (US), IBM (US), AWS (US), NVIDIA (US), OpenAI (US), Informatica (US), Broadcom (US), Sogeti (France), Mphasis (India), Databricks (US), MOSTLY AI (Austria), Tonic (US), MDClone (Israel) TCS (India), Hazy (UK), Synthesia (UK), Synthesized (UK), Facteus (US), Anyverse (Spain), Neurolabs (Scotland), Rendered.ai (US), Gretel (US), OneView (Israel), GenRocket (US), YData (US), CVEDIA (UK), Syntheticus (Switzerland), AnyLogic (US), Bifrost AI (US), Anonos (US).
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Back in the old days, many teams of actuaries and underwriters did months of groundwork to unlock and analyse the information they had to calculate financial risks in order to issue an insurance policy.
Nowadays, I imagine, a lot of the grunt work is done by computers, allowing firms to hire fewer actuaries in order to compete in this crowded insurance space.
Maybe it's a tad bit easier to get hold of data on existing policy holders, but with GDPR and growing lack of trust in organisations, it is fair to say people aren't queuing up to share their personal data.
But my key takeaway from this discussion was that information I volunteered to give up was not only extremely private, but also very valuable (in the hands of profit-seeking insurance companies).
Secondly, any personal data collected - privately or publicly - will still need to be anonymised before it can be used.
Compliance becomes even more costly when you consider situations where data needs to be shared between different departments, team members working remotely in different locations, or even storing it in a cloud environment.
