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The global synthetic data generation market size was valued at USD 288.5 million in 2022 and is projected to grow from USD 351.2 million in 2023 to USD 2,339.8 million by 2030, exhibiting a CAGR of 31.1% during the forecast period.
Synthetic data generation is a process through which data is created algorithmically or artificially and isn’t based on real-world phenomena. Synthetic data is a distorted version of the original data that can be created through statistical modeling and simulation processes using proper tools and cost-effective data augmentation techniques.
According to industry experts, by 2024, almost 60% of data used to develop AI and analytics projects will be synthetically generated. This data can be generated using various methods, including simulations, statistical sampling, and Generative Adversarial Networks (GAN) and is used as a substitute test dataset for production or operational data to validate mathematical models and train machine learning models. The synthetic data generation process is helpful when collecting real-world data is challenging or impractical.
Increased Use of AI and ML Technologies to Synthesize Complex Database Amid Pandemic Boosted Market Growth
Growing Artificial Intelligence (AI) and ML technology penetration across different industrial sectors, including BFSI, healthcare, media & entertainment, automotive, and others, helps secure confidential public information from cyber threats. Synthetic data encourages the organization's internal data-sharing process, which significantly helps store the highly complex structural data by following all the security norms. Thus, using synthetic data ensured data privacy and imitated the statistical properties of the operational data without putting the privacy of an individual and enterprise at risk during the COVID -19 situation.
In June 2020, the National Institutes of Health (NIH) launched the National COVID Cohort Collaborative (N3C) effort to collect a deep database of COVID-19 patients across the U.S. and helped to capture relevant data from healthcare providers present across the country. Syntegra, a synthetic healthcare data provider, generates a synthetic version of the entire N3C COVID-19 database, which provides rapid database access without violating privacy.
Thus, as mentioned above, the exponential usage of synthetic data during the pandemic situation propelled market growth.
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Surge in Deployment of Large Language Models (LLM) to Augment the Market Growth
Large Language Models (LLM) are learning algorithms that help translate, generate, and predict text and other types of content based on large datasets and the continuous development of websites and various solutions that use language models. Generative Pre-trained Transformer (GPT) is a language model that generates text data using GPT-1, GPT-2, and GPT-3 models. GPT-3 is the most complex model and has reached 175 million machine learning parameters to create a large dataset of conversational data.
The continuous development of websites and other database solutions leverages the demand for language models across various industries, which include retail, healthcare, tech, and others. These language models are used by different end-users for text generation, image annotation, fraud detection, conversational AI, and code generation.
Hence, the rise in deployment of Large Language Models (LLM) is anticipated to drive market growth during the forecast period.
Growing Demand for Data Privacy and Security to Fuel Market Growth
Real-world data cannot be accessed due to privacy concerns or compliance risks along with the regulations imposed by General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), and Health Insurance Portability and Accountability Act (HIPAA). The rise in privacy risks for collecting real-world datasets generates demand for synthetic data, a realistic version of the real data set with similar statistical properties. This synthesized data can be used as an alternative to real data and offers several advantages regarding privacy, scalability, and diversity.
For instance, in April 2023, Betterdata, a Singapore-based startup declared to use synthetic data that has similar characteristics and structure to real-world dataset without disclosing sensitive or private information of an individual to secure confidential data and enhance machine learning models.
Lack of Data Accuracy and Realism Hinders Market Growth
Synthetic data generation creates virtual replicas of datasets that can be tested and shared with users. Moreover, this process faces difficulty capturing the minute details of real-world images and specialized models.
As synthetic data depends on real-world data and changes due to innovations and developments, keeping the synthetic dataset constant over time is challenging. Hence, organizations should regularly ensure the synthetic data's accuracy and reliability.
This factor hampers the synthetic data's accuracy and realism, significantly hindering the synthetic data generation market growth.
Tabular Data Exhibits Prominent CAGR by Addressing Privacy Concerns with Artificial Data
Based on data type, the market is segmented into text data, image & video data, tabular data, and others. Recently, companies are facing challenges in collecting real-life data due to privacy concerns. These challenges lead to generating artificial data that mimics real world data, which can be stored in structured tabular format. This boosts the demand for tabular data, which is expected to grow with a prominent CAGR during the forecast period. Synthetic tabular data can be created using Generative Adversarial Network (GAN) to help businesses enhance operational data privacy and security.
According to research analysts, using synthetic tabular data to train Artificial Intelligence (AI) models will grow approximately three times faster than real structured data by 2030.
Furthermore, the text data segment is projected to grow with the largest market share due to increasing usage of natural language generation systems with new machine learning models.
Increasing Need of Test Data Management by Test Managers Contributing to Segmental Growth
Based on application, the market is divided into test data management, AI training & development, enterprise data sharing, and data analytics & visualization. The test data management segment holds the largest market share due to increasing need of the smallest set of data by the test data manager for data testing & data masking. It also aims to avoid legal problems associated with GDPR.
The enterprise data sharing segment grows steadily as enterprises are facing difficulty during cross-border data sharing.
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BFSI Industry Dominates Owing to Rise in Number of Fraud Cases and Usage of Algorithmic Trading
On the basis of industry, the market is divided into healthcare, manufacturing, media & entertainment, automotive, BFSI, retail & e-commerce, IT & telecommunication, and others. Increasing usage of synthetic data across BFSI industry helps enhance the fraud detection technique, risk analysis, and algorithmic trading to validate complex data structures. Thus, the BFSI segment leads to enhance the usage of synthetic data to deliver data-driven banking experiences to global customers.
Similarly, the healthcare segment leads with the second-position in the market as increasing usage of synthetic data in the healthcare industry helps to perform clinical trials, scientific research, generate medical images, and predict rare diseases. Thus, the healthcare segment grows with highest CAGR during the forecast period.
North America Synthetic Data Generation Market Size, 2022 (USD Million)
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The global market scope is classified across five regions, North America, Europe, Asia Pacific, the Middle East & Africa, and South America.
North America holds the largest synthetic data generation market share, owing to the presence of multiple market players. The rising number of AI startups, research institutes, and high-tech companies generates demand for high-quality synthetic data to conduct research and experiments. This factor fuels the market growth across the region.
Asia Pacific is expected to grow with the highest CAGR during the forecast period. It is due to the rising penetration of advanced technologies such as AI/ML and the growing adoption of cloud-based services among different industries to build secure business infrastructure. Increasing investment in generative AI and the rising focus of companies on AI technology are anticipated to propel the demand for synthetic data generation processes in Asia Pacific during the forecast period.
Europe is expected to grow with a significant CAGR during the forecast period due to the presence of multiple synthetic data vendors and tremendous growth in funding for structured synthetic data vendors to bring developments in the in-house synthetic data capabilities of organizations. This factor is projected to propel the market growth during the forecast period.
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The Middle East & Africa and South America are growing due to increasing digital transformation initiatives across BFSI, healthcare, automotive, and media & entertainment. Integrating artificial intelligence and machine learning technologies with finance and the automotive industry to generate reliable synthetic data fuels the market growth of synthetic data generation across both regions.
Key Players Focus on Generating Synthetic Data to Strengthen their Position
Synthetic data generation companies include Datagen, MOSTLY AI, TonicAI, Inc., Synthesis AI, GenRocket, Inc., Gretel Labs, Inc., and K2view Ltd., among others. Increasing investments in generation of synthetic data for different industry verticals are helping key players maintain their competitive edge. These companies also engage in strategic partnerships, acquisitions, and collaborations to expand their business and distribution network and maintain market growth.
An Infographic Representation of Synthetic Data Generation Market
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The report provides a detailed analysis of the market and focuses on key aspects such as leading companies, product/service types, and leading applications of the product. Moreover, the report offers insights into the market trends and highlights key synthetic data generation industry developments. In addition to the factors above, the report encompasses several factors that have contributed to the growth of the market in recent years.
CAGR of 31.1% from 2023 to 2030
Value (USD Million)
By Data Type, Application, Industry, and Region
By Data Type
The market is projected to reach USD 2,339.8 million by 2030.
In 2022, the market was valued at USD 288.5 million.
The market is projected to grow at a CAGR of 31.1% during the forecast period.
The test data segment is expected to lead the market.
Growing demand for data privacy and security to fuel market growth.
Datagen, MOSTLY AI, TonicAI, Inc., Synthesis AI, GenRocket, Inc., Gretel Labs, Inc., K2view Ltd., Sogeti, and Hazy Limited are the top players in the market.
North America is expected to hold the highest market share.
The healthcare segment is expected to grow with a remarkable CAGR during the forecast period.
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