Newark, May 08, 2024 (GLOBE NEWSWIRE) -- The Brainy Insights estimates that the global synthetic data generation market will grow from USD 316.11 Million in 2023 to USD 6,262.27 Million by 2033. Synthetic data generation has many applications across different domains and uses cases. In healthcare, it presents an opportunity for researchers and practitioners to overcome issues with privacy and access while developing innovative solutions without the risk of disclosing sensitive patient information. Likewise, synthetic data in finance is valuable as it enables more accurate assessment in training predictive models, which can aid investment strategies. All this is done while preserving confidentiality around financial transactions. Furthermore, within cybersecurity contexts- generating artificial or "synthetic" datasets helps create simulated attack scenarios where security measures are tested to fortify systems against threats, both contemporary & emerging, alongside staving off vulnerabilities effectively.
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Report Coverage Details
| Report Coverage | Details |
| Forecast Period | 2024 -2033 |
| Base Year | 2023 |
| Market Size in 2022 | USD 316.11 Million |
| Market Size in 2032 | USD 6,262.27 Million |
| CAGR | 34.8% |
| No. of Pages in Report | 237 |
| Segments Covered | Modelling Type, Offering, Application |
| Drivers | Data Scarcity and Quality |
| Opportunities | Demand Across Industries |
Key Insight of the Synthetic data generation Market
Asia Pacific region is expected to grow at the highest CAGR during the forecast period.
During the forecast period, Asia Pacific is expected to grow at the highest CAGR within the global Synthetic data generation market. The Asia Pacific region will significantly expand. The utilization of artificial intelligence is gaining momentum in the Asia-Pacific region, particularly in finance, retail and high-tech industries. These sectors constitute more than 33% of China's AI marketplace. Renowned brands such as ByteDance and Alibaba have successfully implemented tailor-made consumer applications powered by advanced AI technology within the tech industry market segment. To take advantage of the world's largest population with internet connectivity, most Chinese businesses adopting AI tend to focus on enhancing customer experience across various touch-points such that revenue generation increases coupled with augmenting their valuation figures are achieved effectively through novel engagement techniques while maintaining superior customer loyalty standards for long-term gains.
The direct modeling segment is expected to grow at the highest CAGR during the forecast period.
The modelling type segment includes direct modeling, and agent-based modeling (ABM). The services segment is expected to grow at the highest CAGR during the forecast period. With Direct Modeling, users can exert accurate authority over the produced data. They have the freedom to meticulously determine their preferred characteristics, spreads, and associations. By setting definitive guidelines and limitations, they can customize synthetic data in accordance with specific usage cases and application needs. By automating the process of generating synthetic data, Direct Modeling provides effective and adaptable solutions that are especially useful in situations where there is a clear understanding of the underlying distribution patterns, which can be manipulated based on rules. By harnessing computational resources, organizations have created an approach to massive volumes of synthetic data production with nominal manual intervention, resulting in less time-to-market and minimized resource overheads.
The hybrid synthetic data segment is expected to grow at the highest CAGR during the forecast period.
The type segment includes fully synthetic data, partially synthetic data, and hybrid synthetic data. The hybrid synthetic data segment is expected to grow at the highest CAGR during the forecast period. Generating data sets that balance privacy, diversity, and utility can be made flexible and adaptable through the use of hybrid synthetic data. Organizations can tailor synthetic data generation for different applications and domains by dynamically adjusting the proportion of real-world elements with those that are synthetic. Incorporating a mixture of real-time observations and synthetic components, known as hybrid synthetic data, elevates the resilience and adaptability of machine learning models. To enhance predictive analytics and decision-making systems, organizations can amalgamate varied information sources to minimize partiality and boost model aptitude, along with reliability and scalability aspects.
The retail & e-commerce segment is expected to grow at the highest CAGR during the forecast period.
The industry segment includes BFSI, healthcare, transportation & logistics, IT & telecommunication, retail & e-commerce, consumer electronics and others. The retail & e-commerce segment is expected to grow at the highest CAGR during the forecast period. Synthetic data has proven advantageous for retail and e-commerce, as it accelerates data transfer within and beyond business. Synthetic data enables brands to expedite supplier interactions, elevate marketing initiatives, and quicken promotions. Furthermore, merchants benefit from tech companies employing synthetic data for analytic purposes or training models. Moreover, synthetic data's value has recently increased due to its effective stocking management across warehouses. To further advance online sales growth potential by encouraging investment toward software designed solely for synthesizing valuable insights will be crucial among e-commerce business enterprises.
The predictive analysis segment is expected to grow at the highest CAGR during the forecast period.
The application segment is bifurcated into data protection, data sharing, predictive analytics, natural language processing, computer vision algorithms, and others. The predictive analysis segment is expected to grow at the highest CAGR during the forecast period. The financial industry, including banks, will likely employ predictive analytics to detect fraud. For example, American Express has reported testing technology that utilizes generative adversarial networks to detect credit card fraud by creating fake transactions in the form of films. Similarly, insurance companies have also experienced positive outcomes using predictive analytics through cost reduction and revenue increase strategies. The end-users may use artificial data with predictive analytic tools to enhance customer satisfaction and gain insight into consumer needs and desires.
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Recent Developments:
• In May 2023: Okera is an AI-focused data governance platform that Databricks acquired. Through the acquisition, Databricks will be able to make more APIs available so that its partners may utilize them to offer solutions to their clients.
Market Dynamics
Driver: Cost and Resource Efficiency
Conventional data collection and annotation processes require substantial expenses and resources, including manual labelling by human annotators or costly acquisition endeavours. These approaches often have logistical restrictions and financial limitations that restrict their scalability. However, creating synthetic data is economical for generating a significant amount of labelled information in significantly less time than traditional methodologies such as manual annotation or collecting raw data.
Restraint: Computational and Resource Constraints
Generating synthetic data can be demanding and requires significant computational resources, such as specialized hardware accelerators and extensive data processing capabilities. Unfortunately, many organizations—particularly small-to-medium-sized businesses (SMEs) and academic research institutions—do not have the essential know-how or access to this infrastructure. Deploying advanced generative models and simulation techniques requires considerable computing power. This presents scalability issues, thereby hindering the usefulness of synthetic data generation in low-resource settings.
Opportunity: Accelerating AI and ML Innovation
AI and ML technologies have rapidly progressed, causing a higher need for varied data sets that are representative. Nonetheless, access to such data is restricted due to regulatory limitations or confidentiality concerns, which severely affect the availability of necessary datasets. However, synthetic data generation has been introduced as an innovative way to promote AI and ML innovation by providing high-quality training materials across various industries and domains. Creating simulated models representing real-life situations assists organizations in crafting more comprehensive frameworks capable of handling diverse environments effectively, thus resulting in better performance levels all around with superior generalization capabilities.
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Some of the major players operating in the Synthetic data generation Market are:
• Amazon.com Inc.
• AnyLogic North America LLC
• Neurolaboratories Ltd.
• DADoES Inc.
• MDClone Ltd.
• Gretel Labs Inc.
• International Business Machines Corp.
• Microsoft Corp.
• GenRocket Inc.
• OpenAI L.L.C.
• Synthesia Ltd.
• NVIDIA Corp.
Key Segments Cover in the Market:
By Type:
• Fully Synthetic Data
• Partially Synthetic Data
• Hybrid Synthetic Data
By Modeling Type:
• Direct Modeling
• Agent-based Modeling
By Application:
• Data Protection
• Data Sharing
• Predictive Analytics
• Natural Language Processing
• Computer Vision Algorithms
• Others
By Industry:
• BFSI
• Healthcare
• Transportation & Logistics
• IT & Telecommunication
• Retail and E-commerce
• Consumer Electronics
• Others
By Region
• North America (U.S., Canada, Mexico)
• Europe (Germany, France, the U.K., Italy, Spain, Rest of Europe)
• Asia-Pacific (China, Japan, India, Rest of APAC)
• South America (Brazil and the Rest of South America)
• The Middle East and Africa (UAE, South Africa, Rest of MEA)
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About the report:
The market is analyzed based on value (USD Million). All the segments have been analyzed worldwide, regional, and country basis. The study includes the analysis of more than 30 countries for each part. The report analyzes driving factors, opportunities, restraints, and challenges to gain critical market insight. The study includes porter's five forces model, attractiveness analysis, product analysis, supply, and demand analysis, competitor position grid analysis, distribution, and marketing channel analysis.
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