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Premium Report Details
Base Year: 2024
Companies covered: 20
Tables & Figures: 200
Countries covered: 21
Pages: 180
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Synthetic Data Generation Market
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Synthetic Data Generation Market Size
The global synthetic data generation market size was valued at USD 310.5 million in 2024 and is projected to grow at a CAGR of 35.2% between 2025 and 2034. Due to the increasing demand for AI and ML model training, there has been a significant market growth. It’s no secret that artificial intelligence and machine learning algorithms require a lot of advanced and diversified data for training. However, due to scarcity of data, privacy issues, bias, among other reasons, acquiring real-world data becomes costly, tough and time-consuming.
In sectors like healthcare, autonomous cars and even finance, real-world data is not only difficult to obtain but is often illegal or unethical to acquire. To solve this issue, developers have begun relying on synthetic data that is generated to mimic real-world data while not relying on personal or sensitive information, making them a practical workaround. Such data is readily available while still being of high quality, diverse and compliant with privacy requirements, allowing companies to effectively decrease the cost and time in making AI and ML models.
In particular, at the end of December 2024, Mindtech Global launched their synthetic data generation platform called Chameleon 24.2. This platform was developed to assist in creating high quality, labelled training data for computer vision AI systems. The issue this computer system seeks to solve is the lack of diverse datasets that are needed to train advanced AI algorithms.
The utilization of synthetic data is becoming prevalent due to privacy concerns, strict compliance regulations, and growing data generation. With companies in the finance, healthcare, and e-commerce industries collecting sensitive data, they need to comply with strict regulations such as CCPA, GDPR, and HIPAA. It is where synthetic data comes in handy as it provides datasets for AI training while maintaining confidentiality and remaining PII compliant.
Synthetic Data Generation Market Trends
Taking into account the growing number of devices encompassing the Internet, the demand for synthetic data will only increase further. Such data is valuable for simulating environments and enhancing the performance of edge devices. Moreover, synthetic data can be employed to improve the working of AI systems for better decision-making in the ever-growing smart city industry.
Furthermore, the game development, augmented reality and virtual reality industry is boosting market expansion through the use of synthetic data. Such fields aim to build captivating and compelling experiences that need a large amount of data. In these sectors, synthetic data allows companies to create 3D models of settings and engagements that can be utilized for the development and training of AI algorithms to enhance user experience in virtual worlds.
Realistic and quality demands are serious limitations to the expansion of the market for creating synthetic data. The effectiveness of synthetic data as an AI training algorithm is highly proportional to how well the model reproduces real-life data. Even though synthetic data offers cost and space saving as well as privacy preservation advantages, its quality is still the major concern.
If synthetic data produced is not able to depict the intricacy and variability found in real-life data, it could severely affect the AI and produce biased models; for example, within AI training, it is still an obstacle to building virtual data resources for obscure and edge-case scenarios. For instance, in medicine where accurate artificial data is needed to determine diseases and predict outcomes in patients such as imaging, failure to leverage human biology in synthetic data construction could result in ineffective treatment and inaccurate diagnosis of the patient.
Synthetic Data Generation Market Analysis
Based on application, the market is segmented as AI/ML model training, privacy protection, test data management, data analytics and visualization, and others. In 2024, the AL/ML model training segment held a synthetic data generation market share of over 31% and is expected to exceed USD 2 billion by 2034. The AI/ML model training is the most prominent one due to the increasing requirements to train Artificial Intelligence (AI) and Machine Learning (ML) models using vast high-quality datasets at scale.
In real-life implementations, these models operate efficiently if a collection of varied data that is more representative is provided. However, real world data is difficult to obtain as it is elusive, often expensive, and sometimes even takes a longer time to obtain as well as comes with privacy limitations. Due to this there is a growing demand for synthetic data, which is data created artificially made to mimic real world data to help fill in gaps where actual data is difficult to collect.
Based on the data type, the synthetic data generation market is divided into image & video, tabular, text, and others. The text segment held around 34.5% of the market share in 2024. The largest share in type of data in the synthetic data generation industry is occupied by the text data owing to its application in mass in almost all industries, more specifically in NLP related AI model training.
With the increased adoption of artificial intelligence by businesses for services like customer interactions, content writing, sentiment assessment, and data analysis, the necessity and demand for vast volumes of rich and diverse text has increased. In order to development AI systems that could comprehend, manipulate, and produce text like a human language which is essential in developing modern tools such as chatbots, virtual assistants, machine translators, and information retrieval systems, aid is paramount.
North America dominated the global synthetic data generation market with a major share of over 34% in 2024 and U.S. holds a significant share of this region. The advancement of new technologies, favorable government regulations, and the economic boom have vastly spurred the demand for synthetic data generation in APAC, a demand which continues to grow at an exponential rate. Countries like China, India, Japan and South Korea have begun to heavily invest in AI and ML industries, which in turn has catalyzed the process of digital transformation.
AI models in the healthcare, automotive, and manufacturing industries are being modified to improve efficiency and automate mundane processes. However, almost all industries require massive quantities of quality data for AI and ML models, which is why Synthetic data provides a viable solution to complex problems such as privacy, data collection expenses, data shortage and a plethora of other challenges.
The U.S is the key highlight in the synthetic data generation market thanks to its investment capacity and prowess in AI, technology and data industries. Other tech behemoths who operate within the country are also conducting extensive research in machine learning and AI which has skyrocketed the demand for large sum and diverse datasets. Furthermore, research institutions and Government agencies are pumping in money into the development of artificial and machine learning technologies which has significantly uplifted the delivery of synthetic data generation methods.
Europe owing to the regulatory, technology, and industry factors. A prime factor is the stringent data privacy laws including GDPR that is becoming the benchmark for all European data protection laws and policies. Business sectors such as healthcare, finance, and retail have begun leveraging AI and machine learning to enhance customer data management.
Consequently, techniques like synthetic data generation are gaining popularity as a more secure approach to privacy. With the aid of artificial data, businesses can construct or train AI models, analyze information, and even test algorithms without needing to handle real sensitive data. This helps them comply with stringent data privacy laws while still gaining business intelligence to enhance AI models.
Synthetic Data Generation Market Share
In 2024, DataGen and Gretel together garnered more than 10% share in the synthetic data generation industry. DataGen and Gretel are among the leading players in the market of synthetic data generation. They have built their reputations on exceptional innovations and are situated in such fields as training AI/ML models, privacy protection, and data scaling.
DataGen is highly capable of producing high-fidelity synthetic data to train AI algorithms for use in computer vision and 3D scene rendering, eliminating the complications of real data. Gretel works with companies to produce vast amounts of synthetic data while ensuring that privacy regulations are met, thus making the trained machine learning models as efficient as possible.
Sagemaker and Sogeti have made different definitive offerings in the market to advance their penetration in the developing synthetic data generation market. Sagemaker has recently added the capacity to generate synthetic data into its arsenal of AI/ML tools. This results in organizations being able to create and utilize synthetic datasets for training, testing, and improving the AI models on a large scale.
On the other hand, Sogeti has specialized in implementing consulting services and technologies related to holographic and synthetic data solutions for healthcare, automotive, banking and finance industries. Data privacy, compliance and advanced AI integration with other industry sectors have shifted the balance of market power between the two companies and helped to expand their discontent with the wider marketplace.
Synthetic Data Generation Market Companies
Major players operating in the synthetic data generation industry are:
The global and the regional aged segments of the synthetic data generation market consist of International and Regional vendors. The segmentation allows the providers to cater to international, regional and local ends of automobiles, healthcare, finance and technology. The key international holders access the market through acquisitions and with the assortment of synthetic data solutions crafted to elevated AI model training, compliance to data privacy requirements and mass data generation.
They have also made great strides in innovations for example realistic data simulations and customization for varied domains enabling them stay competitive boosting global markets especially where use of AI and machine learning are ripe.
Regional providers continue active by leveraging their profound knowledge of local market conditions and offering inexpensive and bespoke solutions for some particular use cases such as compliance or industry specific requirements. Nevertheless, the growing requirement for rich-quality synthetic data in order to avoid possible privacy challenges, improve the performance of algorithms and enhance data-related economic activities induces regional players either to develop or associate with foreign companies.
The market is anticipated to be significantly consolidated as a result of the rising number of M&As due to domestic companies attempts to fill in the technological gap in order to compete with industry leaders. This consolidation is expected to transform the competitive environment of the synthetic data generation market and therefore enhance creativity and proliferation of the industry among others.
Synthetic Data Generation Industry News
The synthetic data generation market research report includes in-depth coverage of the industry with estimates & forecasts in terms of revenue ($Bn) from 2021 to 2034, for the following segments:
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Market, By Data Type
Market, By Offering
Market, By Generation Technique
Market, By Application
Market, By End Use
The above information is provided for the following regions and countries: