The global synthetic data generation market size was valued at USD 194.5 million in 2022 and is projected to reach USD 3,400 million by 2031, registering a CAGR of 37.3% during the forecast period (2023-2031).
Synthetic data generation creates artificial data that resembles data from the actual world. It generates data instances with comparable statistical properties, patterns, and associations as the original data. It can be used as a substitute or supplement for real data in various applications, especially when access to real data is restricted, costly, or privacy-sensitive.
The global synthetic data generation market share will increase significantly in the future years. The market for synthetic data generation is propelled by the rising demand for data privacy, the need for large and diverse datasets for machine learning, and the rising adoption of artificial intelligence and data-driven technologies across multiple industries. The demand for simulated data has risen among industry participants in response to the increasing prevalence of the privacy-protection solution. In addition, the exponential growth of machine learning has transferred the focus to synthetic data. Utilizing AI and machine learning technology, artificial data accesses enormous data sets.
Regulations such as the General Data Protection Regulation (GDPR) in the European Union and the California Consumer Privacy Act (CCPA) in California have emphasized data privacy and compliance. These rules impose standards on enterprises regarding personal data collection, processing, and protection. High-profile data breaches have emphasized the need for enhanced data privacy and security safeguards. Companies that suffer data breaches suffer considerable financial and reputational harm. Data breaches can result in significant expenses, including legal fines, loss of consumer trust, and prospective litigation. For example, in 2017, the Equifax data breach exposed the personal information of nearly 147 million people. Equifax later agreed to a $700 million settlement to resolve numerous legal claims arising from the incident. Such occurrences highlight the significance of data privacy and enterprises' need to take proactive steps to protect sensitive information. The Synthetic data generation market trend demonstrates the rising importance of data protection and compliance. Thereby driving the growth of the market.
Organizations suffer financial losses and additional expenditures due to data breaches and sensitive information leaking. Remediation operations, such as incident response, forensic investigations, alerting impacted persons, and adopting better security measures, need substantial time, resources, and financial investments. The financial cost of these accidents might stymie market development and expansion ambitions. IBM claims the global average cost of a data breach climbed by USD 0.11 million in 2022 to USD 4.35 million, the most in the report's history. The 2.6% rise from USD 4.24 million in the 2021 report to USD 4.35 million in the 2022 report. This includes incident response expenses, legal fees, regulatory fines, customer notification, reputational harm, and potential company loss. Small and medium-sized firms (SMEs) with limited resources may bear the brunt of the financial consequences.
To improve operational efficiency, businesses are employing technologically enhanced ways. Artificial intelligence (AI), machine learning (ML), and nanotechnologies are propelling the growth of the synthetic data production solutions market. Organizations are leveraging new and developing technologies to establish their presence in the global market and generate additional income opportunities. Furthermore, synthetic data will be critical in addressing data management concerns like privacy, predictive analytics, security, and overall data-centricity. Synthetic data generation market report demonstrates that today's AI-powered synthetic data generation algorithms consume actual data, learn its characteristics, correlations, and patterns in great detail, and then produce endless quantities of wholly false, synthetic data that match the statistical properties of the original ingested dataset. Modern, synthetic datasets are scalable, privacy-compliant, and retain all of the original meaning while removing the weight of sensitive information. Such innovations will propel the synthetic data generation market growth in the next years.
The global synthetic data generation market is segmented based on component, end-use, technology, application, model, and region.
The market is divided into Tabular Data, Text Data, Image Video Data, and Others based on the data type.
Over the projection period, the Tabular Data is likely to generate the most revenue.
Tabular data refers to structured data in databases or spreadsheets organized in rows and columns. Using synthetic data generation techniques, it is possible to generate artificial tabular datasets that replicate tabular data's statistical properties and relationships from the actual world. This can be useful for data augmentation, model training, and maintaining data privacy when sharing sensitive information.
The image and video data segment is anticipated to contribute considerably to the market share of synthetic data generation due to the growing demand for database expansion. In addition, synthetic media as a drop-in replacement for the original data has become prevalent in developing and developed nations. Synthetic images and recordings have gained immense popularity in the automotive industry.
Based on Modeling, the market is divided into Direct Modeling, Agent-Based Modeling.
The Agent-Based Modeling segment generated the most revenue and is anticipated to grow significantly during the forecast period.
Agent-Based Modeling has gained popularity for its ability to create a physical, real-world data model and reproduce data using the same model. In recent years, agent-based Modeling has surpassed traditional models in the financial sector. It is in high demand for simulating business transactions to test and develop fraud detection systems. Participants in the industry are anticipated to rely on ABMs to model various types of networks. Additionally, ABMs have garnered prominence in simulating consumer interactions, innovations, automobiles, and roads.
Based on the offering, the market is divided into Fully Synthetic, Partially Synthetic, and Hybrid Synthetic Data.
The Fully Synthetic Data segment is the highest contributor to the market and is estimated to grow significantly during the forecast period.
Fully synthetic data refers to datasets wholly generated artificially, with no reliance on data from the actual world. There are no genuine observations from the original dataset in the generated data. Generative synthetic data is generated using AI models and algorithms, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). This service is useful when data is limited or inaccessible or when there are privacy concerns regarding using actual data.
Based on application, the market is divided into Data Protection, Data Sharing, Predictive Analytics, Natural Language Processing, Computer Vision Algorithms, and Others.
The Natural Language Processing (NLP) segment owns the largest market share and is anticipated to grow significantly during the forecast period.
Synthetic data has increased exponentially in natural language processing as it facilitates the development of new language releases. Amazon announced variants of Alexa in Spanish, Hindi, and Brazilian Portuguese in October 2019. The company has emphasized synthetic data to optimize and augment the training data for its natural language understanding (NLU) systems. Recent advancements in NLP will accelerate the need for synthetic data to accelerate enterprise operations.
Predictive analytics has emerged as a prospective application segment fueled by robust demand from the BFSI industry. By generating additional synthetic data, organizations can improve the accuracy and robustness of their predictive models and augment their training datasets. Synthetic data can assist in resolving issues associated with unbalanced datasets, small sample sizes, and situations where real data collection would be costly or time-consuming.
Based on the end-user, the market is divided into BFSI, Healthcare and Life Sciences, Transportation and Logistics, Retail and E-Commerce, Manufacturing, Consumer Electronics, and Others.
Healthcare and Life Sciences segment is leading the market and is estimated to grow significantly during the forecast period.
Some healthcare and life sciences applications include medical imaging, medication development, patient data analysis, and healthcare research. Without jeopardizing patient privacy, synthetic datasets may be utilized to generate realistic medical imaging, imitate patient data for research reasons, and provide different datasets for training AI models.
The global synthetic data generation market research is analyzed in North America, Europe, Asia-Pacific, the Middle East and Africa, and Latin America.
North America holds the largest market share and is expected to expand at a CAGR of 34.26% during the forecast period. The United States and Canada have emerged as lucrative regions as end-use industries have shown a growing preference for fraud detection, natural language processing, and image data. J.P. Morgan, American Express, Amazon, and Google's Waymo have all increased their investments in synthetic data. For example, Amazon introduced Amazon SageMaker Ground Truth in June 2022 to generate labeled synthetic image data. These industry participants will favor synthetic data for machine learning training, payment data for fraud detection, and anti-money laundering practices.
Moreover, the expanding footprint of computer vision will also bode well for the North American market forecast for synthetic data generation. Manufacturing, geospatial imagery, and physical security have gained much popularity. In March 2022, for instance, Datagen, a company with facilities in New York and Tel Aviv, raised USD 50 million in Series B funding to promote the development of synthetic data solutions for computer vision teams. In addition, the increasing prevalence of autonomous vehicles has boosted simulation data throughout the region. With simulation data, autonomous vehicles have gained ground, allowing companies to test extreme cases and reduce the likelihood of accidents. Advanced economies, such as the United States, have bolstered the autonomous simulation platform in response to stringent training requirements and the development of autonomous vehicles.
Asia-Pacific is expected to grow at a CAGR of 36.84%, becoming the fastest-growing region. In Asia-Pacific, the adoption of artificial intelligence is rapidly expanding. Significant AI adoption occurs in the finance, retail, and high-tech industries, accounting for over one-third of China's AI market. In the tech industry, for instance, ByteDance and Alibaba, both of which are ubiquitous names in China, are renowned for their AI-driven consumer applications that are highly customized. Most AI applications widely adopted in China thus far have been in consumer-facing businesses, driven by the world's largest internet user base and the ability to engage with customers in novel ways to increase revenue, customer loyalty, and market valuations.
Europe is expected to rise at a CAGR of 32.89%. Germany dominated the European market for synthetic data generation by country. European nations have a very robust electronics industry. According to the government of the United Kingdom, the annual contribution of the electronics industry to the British economy is 16 billion Pounds. The industry has a robust intellectual property rights framework and legal structure, developed intellectual property rights development, the ability to swiftly deliver products to the market, a substantial software sector, and a research community comprised of universities, corporations, and industry.
The Middle East and Africa (MEA) have developed an interest in artificial intelligence (AI) and its applications in various industries. Synthetic data generation has the potential to resolve data privacy concerns and facilitate AI model training and development as AI adoption increases. Data privacy and compliance regulations are gaining traction in the Middle East and Africa. Countries like the United Arab Emirates and Saudi Arabia have enacted data protection laws to protect personal information. This increasing emphasis on data privacy and compliance may increase demand for privacy-protecting solutions such as synthetic data generation. Latin American nations have enacted data protection regulations to preserve privacy rights like other regions. In 2020, Brazil instituted the General Data Protection Law (LGPD), which correlates with the European GDPR's principles. Compliance with these regulations may necessitate the development of privacy-enhancing technologies.
|Report Coverage||Revenue Forecast, Competitive Landscape, Growth Factors, Environment & Regulatory Landscape and Trends|
"Find new revenue generation opportunities"