Synthetic Data Generation Market Size, Share & Trends Analysis Report By Data Type, By Modeling Type, By Offering, By Application, By End-use, By Region, And Segment Forecasts, 2023 - 2030

Report Overview

The global synthetic data generation market size garnered USD 163.8 million in 2022 and is expected to witness a CAGR of 35.0% from 2023 to 2030. An uptick in the synthetic generation of data following the rising penetration of Artificial Intelligence (AI) has spurred industry growth. For Instance, in August 2020, the White House reportedly announced an infusion of USD 1 billion in AI and quantum computing. Demand for data has become pronounced with a growing footfall of connected devices and IoT, further expediting a need for synthetic data to generate on-demand data. Industry players are expected to seek synthetic data to address a gap in data provision.

Synthetic data is also popular as fake data that can be used in place of real data to train AI models. Industry players have exhibited an increased demand for simulated data in the wake of a surging penetration of the privacy-preservation solution. Moreover, an exponential rise in machine learning has shifted the attention towards synthetic data. Artificial data leverages AI and machine learning technology by accessing massive data sets.

The urgency to adhere to privacy laws, including GDPR, will augur well for major companies gearing to foster their portfolios. Some other expanding applications of created data include training models amidst a shortage of real data and ramp-up model developments, among others. Prominently, artificial data can help train and foster models before the availability of real data and minimize costs.

AI stakeholders have exhibited increased synthetic data traction across emerging and advanced economies. For instance, in September 2021, a study from Synthesis AI in collaboration with Vanson Bourne suggested that 89% of technology decision-makers see synthetic data as a key to staying ahead. Tech executives will likely bank on artificial data to enhance data quality and bolster productivity. In the nascent stage, synthetic data generation is expected across industry verticals, including automotive and healthcare, to improve access, contain cost, and minimize the time taken to build AI models.

Market Dynamics

Hyperconnectivity is rapidly growing across various industries. With the advancement of technology in cybersecurity, automated marketing, customer service, and product management are augmenting market growth. Both implications and use of AI and ML are growing exponentially. However, when companies use AI and machine learning from third parties, it cannot be easy to find AI training for data. Although it may be challenging to obtain consumers' consent to use their data for analytics, the remaining information and insights are secure. Owing to privacy concerns, complex data is usually off-limits to both internal data science teams and external AI or analytics vendors. Data quality is still an issue even when data is available.

The increasing prominence of the Internet of Things (IoT) and Industrial Internet of Things (IIoT) for developing tangible systems and algorithms will combine artificial intelligence, distributed ledger technologies, hyperconnectivity, and edge computing. Future IoT applications will combine AI techniques like machine learning and neural networks to optimize the processing of information with drones, robotic devices, augmented and virtual reality (AR/VR), autonomous vehicles, and digital assistants. The creation of new goods, services, and experiences using these technologies will help numerous industries, markets, and consumers. A more human-centered perspective will enable us to maximize the effects of subsequent generations of Internet of Things/Internet of Things-related technologies.

Data Type Insights

In terms of revenue, the tabular data segment held largest share of over 38% in 2022. Stakeholders expect the tabular data segment to account for a significant share of the global market, mainly due to bullish demand from researchers. In October 2020, MIT researchers introduced a set of open-source data generation tools-Synthetic Data Vault.

The researchers asserted that users would get data for their projects in tables and time series formats. Moreover, in 2019, a team of researchers proposed conditional tabular GAN (CTGAN) to boost training procedures with mode-specific normalization and address data imbalance, among others. With researchers emphasizing tabular data, end-user sectors will likely bank on artificial data for data privacy protection.

The image & video data segment is anticipated to contribute significantly toward synthetic data generation market share on the back of soaring demand to boost the database. Furthermore, the use of synthetic media as a drop-in replacement for the original data has become noticeable across developing and developed countries. Prominently, synthetic images & videos have amassed massive popularity across the automotive sector. For instance, in December 2022, AWS partnered with Stability Al to develop open-source tools and models jointly. Stability Al, an open-source AI company driven by a community of contributors, has chosen AWS as its favored cloud provider to create and expand its AI models, covering image, language, audio, video, and 3D content generation. To expedite their efforts on open-source generative AI models, Stability Al will utilize Amazon SageMaker, AWS's comprehensive machine learning service, along with AWS's established computing infrastructure and storage solutions.

Modeling Type Insights

In terms of revenue, the agent-based modeling segment accounted for the highest share of 60% in 2022. Agent-based modeling (ABM) has garnered popularity for creating a physical model of real-world data and reproducing data using same model. Lately, agent-based modeling has gained ground over traditional models in the financial sector.

It has become highly sought after in generating business transactions for testing and developing fraud detection systems. Industry participants are expected to count on ABMs to leverage modelings of various sorts of networks. ABMs have also gained prominence in simulating consumer interactions, innovations, autos, and roadways.

Market players have prioritized ABMs due to their robust traffic control and management penetration. For instance, agent-based modeling has become trendier to emphasize car sharing or route choice and generate novel systems and strategies. Moreover, psychological characteristics have gained ground to foster the agent models. Agent-based simulation has also received impetus in sharing mobility research for information-transferring processes and returning effective feedback.

Offering Insights

The fully synthetic data segment led the synthetic data generation market with the largest revenue share of 35% in 2022. The hybrid synthetic data segment is poised to witness a notable CAGR during the forecast period. The upward growth trajectory is mainly attributed to privacy preservation with increased utility as it offers upsides of complete and partially synthetic data. While trend for hybrid synthetic data will be noticeable across end-use sectors, the need for longer processing time may challenge market growth.

The stakeholders anticipate a fully synthetic data segment contributing significantly to global market value. The upward growth trajectory is partly due to the need for increased privacy across emerging and advanced economies. Prominently, leading companies have augmented investments in fully synthetic to boost their penetration in the automotive industry.

For instance, in May 2022, Waymo was reported to have announced a building World’s Most Experienced Driver. The company claims it can generate fully synthetic data on a real-world scale, ramp up data generation rates, and enhance iteration speeds.

Application Insights

The natural language processing segment held a leading revenue share of over 26% in 2022. Synthetic data has witnessed an exponential use in natural language processing as it helps bootstrap new language releases. In September 2023, Amazon launched the Echo Show and Alexa mobile app to customers in the U.S., U.K., Germany, and Japan.

The company has increased its focus on synthetic data to streamline and complete training data of its natural language understanding (NLU) systems. Recent advances in NLP will further expedite the need for synthetic data to leverage enterprises to move faster.

Predictive analytics has also emerged as a promising application segment, driven by solid demand from the BFSI sector. Banks and financial sectors are likely to use synthetic data in predictive analytics for fraud detection. For instance, in September 2020, American Express reported testing technology to help create fake videos to combat financial fraud.

The company uses generative adversarial networks to identify credit card scams to generate fictitious financial data that look like credit card transactions. Moreover, the insurance sector has exhibited traction for predictive analytics to augment sales and minimize underwriting expenses. End-users are likely to use artificial data for predictive analytics to find the needs and demands of customers and boost their satisfaction.

End-use Insights

In terms of revenue, the healthcare & life sciences segment accounted for the highest share of 22% in 2022. The healthcare & life science sector is poised to show bullish demand for privacy-protecting synthetic data. Amidst challenges from data breach risks, patient privacy, regulatory frameworks, separate data sources, and artificial data generation tools have gained significant momentum.

For Instance, in May 2022, Anthem Inc. announced joining Alphabet Inc.’s Google Cloud to create 1.5 to 2 petabytes of synthetic data for better fraud detection and personalized services. The strong potential of synthetic data in healthcare for increased agility and privacy regulations will continue to foster the position of leading companies in the global market.

Artificial data has provided a fillip to the retail and e-commerce sector to train AI models and expedite data sharing within the organization and outside enterprises. Brands and retailers use synthetic data to streamline data exchange with vendors and propel advertising and promotions.

Moreover, retailers are also cashing in on tech companies using synthetic business data for analytics and training. Lately, using artificial data has also gained ground for efficient inventory and warehousing management. With a surge in online purchases, e-commerce players could further propel investment in synthetic data generation software.

Regional Insights

In terms of revenue, North America held the leading share of 35% in 2022. The U.S. and Canada have emerged as lucrative regions as end-use sectors have shown an increased inclination toward fraud detection, NLP, and image data. Several companies, including J.P. Morgan, American Express, Amazon, and Google’s Waymo, have upped investments in synthetic data.

For instance, in June 2022, Amazon introduced Amazon SageMaker Ground Truth to generate labeled synthetic image data. These industry players will show an inclination toward synthetic data to train machine learning, payment data for fraud detection, and anti-money laundering behaviors.

Furthermore, the expanding footprint of computer vision will also fare well in the North America synthetic data generation market forecast. Manufacturing, geospatial imagery, and physical security have garnered pronounced traction. For instance, in March 2022, Datagen, with offices in New York and Tel Aviv, raised USD 50 million in Series B to foster synthetic data solution growth for computer vision teams.

Besides, the growing prominence of autonomous vehicles has provided an impetus to simulation data across the region. Autonomous vehicles have gained ground with simulation data, enabling companies to test edge cases and keep risk of accidents at bay. Advanced economies, such as the U.S., have reinforced autonomous simulation platforms for rigorous training demands and the development of self-driving vehicles.

Key Companies & Market Share Insights

The competitive scenario refers to developing and developed countries emphasizing organic and inorganic growth strategies. Leading companies will likely provide synthetic data products and services to overcome security concerns, governance processes, and legacy infrastructure issues. Further, the rising prominence of data sharing, computer vision algorithms, NLP, and predictive analytics will redefine the global landscape.

In the emerging synthetic data space, growth opportunities could be galore in the ensuing period. Infusion of funds into mergers & acquisitions, product launches, innovations, and R&D activities could be noticeable. To illustrate, in April 2022, Synthesis AI raised USD 17 million in Series A to generate synthetic data for computer vision AI, bringing the total funding to more than USD 24 million.

The company contemplates bolstering research with an emphasis on mixed training (synthetic and real), neural rendering, and complex human behavior modeling. Besides, In May 2023, Databricks acquired Okera, a data governance platform specializing in AI. This acquisition will empower Databricks to introduce new APIs that its data governance partners can leverage to offer enhanced solutions to their customers. Some prominent players in global synthetic data generation market include:

• Mostly AI

• Synthesis AI

• Statice

• YData

• Ekobit d.o.o.

• Hazy

• Kinetic Vision, Inc.

• Kymera-labs

• MDClone

• Neuromation

• TwentyBN

• DataGen Technologies

• Informatica Test Data Management

Synthetic Data Generation Market Report Scope

Global Synthetic Data Generation Market Segmentation

This report forecasts revenue growth at the global, regional, and country levels and provides an analysis of the latest industry trends in each of the sub-segments from 2017 to 2030. For this study, Grand View Research has segmented the global synthetic data generation market report based on data type, modeling type, offering, application, end-use, and region:

• Data Type Outlook (Revenue, USD Million, 2017 - 2030)

  • Tabular Data

  • Text Data

  • Image & Video Data

  • Others (Audio, Time Series, etc.)

• Modeling Type Outlook (Revenue, USD Million, 2017 - 2030)

  • Direct Modeling

  • Agent-based Modeling

• Offering Outlook (Revenue, USD Million, 2017 - 2030)

  • Fully Synthetic Data

  • Partially Synthetic Data

  • Hybrid Synthetic Data

• Application Outlook (Revenue, USD Million, 2017 - 2030)

  • Data Protection

  • Data Sharing

  • Predictive Analytics

  • Natural Language Processing

  • Computer Vision Algorithms

  • Others

• End-use Outlook (Revenue, USD Million, 2017 - 2030)

  • BFSI

  • Healthcare & Life Sciences

  • Transportation & Logistics

  • IT & Telecommunication

  • Retail and E-commerce

  • Manufacturing

  • Consumer Electronics

  • Others

• Regional Outlook (Revenue, USD Million, 2017 - 2030)

  • North America

    • U.S.

    • Canada

    • Mexico

  • Europe

    • U.K.

    • Germany

    • France

  • Asia Pacific

    • China

    • Japan

    • India

  • South America

    • Brazil

    • MEA