Synthetic Data Software Market Outlook

The global synthetic data software market size was valued at approximately USD 1.2 billion in 2023 and is projected to reach USD 7.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 22.4% during the forecast period. The growth of this market can be attributed to the increasing demand for data privacy and security, advancements in artificial intelligence (AI) and machine learning (ML), and the rising need for high-quality data to train AI models.

One of the primary growth factors for the synthetic data software market is the escalating concern over data privacy and governance. With the rise of stringent data protection regulations like GDPR in Europe and CCPA in California, organizations are increasingly seeking alternatives to real data that can still provide meaningful insights without compromising privacy. Synthetic data software offers a solution by generating artificial data that mimics real-world data distributions, thereby mitigating privacy risks while still allowing for robust data analysis and model training.

Another significant driver of market growth is the rapid advancement in AI and ML technologies. These technologies require vast amounts of data to train models effectively. Traditional data collection methods often fall short in terms of volume, variety, and veracity. Synthetic data software addresses these limitations by creating scalable, diverse, and accurate datasets, enabling more effective and efficient model training. As AI and ML applications continue to expand across various industries, the demand for synthetic data software is expected to surge.

The increasing application of synthetic data software across diverse sectors such as healthcare, finance, automotive, and retail also acts as a catalyst for market growth. In healthcare, synthetic data can be used to simulate patient records for research without violating patient privacy laws. In finance, it can help in creating realistic datasets for fraud detection and risk assessment without exposing sensitive financial information. Similarly, in automotive, synthetic data is crucial for training autonomous driving systems by simulating various driving scenarios.

From a regional perspective, North America holds the largest market share due to its early adoption of advanced technologies and the presence of key market players. Europe follows closely, driven by stringent data protection regulations and a strong focus on privacy. The Asia Pacific region is expected to witness the highest growth rate owing to the rapid digital transformation, increasing investments in AI and ML, and a burgeoning tech-savvy population. Latin America and the Middle East & Africa are also anticipated to experience steady growth, supported by emerging technological ecosystems and increasing awareness of data privacy.

Component Analysis

When examining the synthetic data software market by component, it is essential to consider both software and services. The software segment dominates the market as it encompasses the actual tools and platforms that generate synthetic data. These tools leverage advanced algorithms and statistical methods to produce artificial datasets that closely resemble real-world data. The demand for such software is growing rapidly as organizations across various sectors seek to enhance their data capabilities without compromising on security and privacy.

On the other hand, the services segment includes consulting, implementation, and support services that help organizations integrate synthetic data software into their existing systems. As the market matures, the services segment is expected to grow significantly. This growth can be attributed to the increasing complexity of synthetic data generation and the need for specialized expertise to optimize its use. Service providers offer valuable insights and best practices, ensuring that organizations maximize the benefits of synthetic data while minimizing risks.

The interplay between software and services is crucial for the holistic growth of the synthetic data software market. While software provides the necessary tools for data generation, services ensure that these tools are effectively implemented and utilized. Together, they create a comprehensive solution that addresses the diverse needs of organizations, from initial setup to ongoing maintenance and support. As more organizations recognize the value of synthetic data, the demand for both software and services is expected to rise, driving overall market growth.

BackgroundClinical data is instrumental to medical research, machine learning (ML) model development, and advancing surgical care, but access is often constrained by privacy regulations and missing data. Synthetic data offers a promising solution to preserve privacy while enabling broader data access. Recent advances in large language models (LLMs) provide an opportunity to generate synthetic data with reduced reliance on domain expertise, computational resources, and pre-training.ObjectiveThis study aims to assess the feasibility of generating realistic tabular clinical data with OpenAI’s GPT-4o using zero-shot prompting, and evaluate the fidelity of LLM-generated data by comparing its statistical properties to the Vital Signs DataBase (VitalDB), a real-world open-source perioperative dataset.MethodsIn Phase 1, GPT-4o was prompted to generate a dataset with qualitative descriptions of 13 clinical parameters. The resultant data was assessed for general errors, plausibility of outputs, and cross-verification of related parameters. In Phase 2, GPT-4o was prompted to generate a dataset using descriptive statistics of the VitalDB dataset. Fidelity was assessed using two-sample t-tests, two-sample proportion tests, and 95% confidence interval (CI) overlap.ResultsIn Phase 1, GPT-4o generated a complete and structured dataset comprising 6,166 case files. The dataset was plausible in range and correctly calculated body mass index for all case files based on respective heights and weights. Statistical comparison between the LLM-generated datasets and VitalDB revealed that Phase 2 data achieved significant fidelity. Phase 2 data demonstrated statistical similarity in 12/13 (92.31%) parameters, whereby no statistically significant differences were observed in 6/6 (100.0%) categorical/binary and 6/7 (85.71%) continuous parameters. Overlap of 95% CIs were observed in 6/7 (85.71%) continuous parameters.ConclusionZero-shot prompting with GPT-4o can generate realistic tabular synthetic datasets, which can replicate key statistical properties of real-world perioperative data. This study highlights the potential of LLMs as a novel and accessible modality for synthetic data generation, which may address critical barriers in clinical data access and eliminate the need for technical expertise, extensive computational resources, and pre-training. Further research is warranted to enhance fidelity and investigate the use of LLMs to amplify and augment datasets, preserve multivariate relationships, and train robust ML models.

Motivation

This dataset was created to pilot techniques for creating synthetic data from datasets containing sensitive and protected information in the local government context. Synthetic data generation replaces actual data with representative data generated from statistical models; this preserves the key data properties that allow insights to be drawn from the data while protecting the privacy of the people included in the data. We invite you to read the Understanding Synthetic Data white paper for a concise introduction to synthetic data.

This effort was a collaboration of the Urban Institute, Allegheny County’s Department of Human Services (DHS) and CountyStat, and the University of Pittsburgh’s Western Pennsylvania Regional Data Center.

Collection

The source data for this project consisted of 1) month-by-month records of services included in Allegheny County's data warehouse and 2) demographic data about the individuals who received the services. As the County’s data warehouse combines this service and client data, this data is referred to as “Integrated Services data”. Read more about the data warehouse and the kinds of services it includes here.

Preprocessing

Synthetic data are typically generated from probability distributions or models identified as being representative of the confidential data. For this dataset, a model of the Integrated Services data was used to generate multiple versions of the synthetic dataset. These different candidate datasets were evaluated to select for publication the dataset version that best balances utility and privacy. For high-level information about this evaluation, see the Synthetic Data User Guide.

For more information about the creation of the synthetic version of this data, see the technical brief for this project, which discusses the technical decision making and modeling process in more detail.

Recommended Uses

This disaggregated synthetic data allows for many analyses that are not possible with aggregate data (summary statistics). Broadly, this synthetic version of this data could be analyzed to better understand the usage of human services by people in Allegheny County, including the interplay in the usage of multiple services and demographic information about clients.

Known Limitations/Biases

Some amount of deviation from the original data is inherent to the synthetic data generation process. Specific examples of limitations (including undercounts and overcounts for the usage of different services) are given in the Synthetic Data User Guide and the technical report describing this dataset's creation.

Feedback

Please reach out to this dataset's data steward (listed below) to let us know how you are using this data and if you found it to be helpful. Please also provide any feedback on how to make this dataset more applicable to your work, any suggestions of future synthetic datasets, or any additional information that would make this more useful. Also, please copy wprdc@pitt.edu on any such feedback (as the WPRDC always loves to hear about how people use the data that they publish and how the data could be improved).

Further Documentation and Resources

1) A high-level overview of synthetic data generation as a method for protecting privacy can be found in the Understanding Synthetic Data white paper.
2) The Synthetic Data User Guide provides high-level information to help users understand the motivation, evaluation process, and limitations of the synthetic version of Allegheny County DHS's Human Services data published here.
3) Generating a Fully Synthetic Human Services Dataset: A Technical Report on Synthesis and Evaluation Methodologies describes the full technical methodology used for generating the synthetic data, evaluating the various options, and selecting the final candidate for publication.
4) The WPRDC also hosts the Allegheny County Human Services Community Profiles dataset, which provides annual updates on human-services usage, aggregated by neighborhood/municipality. That data can be explored using the County's Human Services Community Profile web site.

Data Description

We release the synthetic data generated using the method described in the paper Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models (ACL 2024 Findings). The external knowledge we use is based on LLM-generated topics and writing styles.

  Generated Datasets

The original train/validation/test data, and the generated synthetic training data are listed as follows. For each dataset, we generate 5000… See the full description on the dataset page: https://huggingface.co/datasets/ritaranx/clinical-synthetic-text-llm.

Synthetic Data Video Generator Market Outlook

According to our latest research, the global synthetic data video generator market size reached USD 1.32 billion in 2024 and is anticipated to grow at a robust CAGR of 38.7% from 2025 to 2033. By the end of 2033, the market is projected to reach USD 18.59 billion, driven by rapid advancements in artificial intelligence, the growing need for high-quality training data for machine learning models, and increasing adoption across industries such as autonomous vehicles, healthcare, and surveillance. The surge in demand for data privacy, coupled with the necessity to overcome data scarcity and bias in real-world datasets, is significantly fueling the synthetic data video generator market's growth trajectory.

One of the primary growth factors for the synthetic data video generator market is the escalating demand for high-fidelity, annotated video datasets required to train and validate AI-driven systems. Traditional data collection methods are often hampered by privacy concerns, high costs, and the sheer complexity of obtaining diverse and representative video samples. Synthetic data video generators address these challenges by enabling the creation of large-scale, customizable, and bias-free datasets that closely mimic real-world scenarios. This capability is particularly vital for sectors such as autonomous vehicles and robotics, where the accuracy and safety of AI models depend heavily on the quality and variety of training data. As organizations strive to accelerate innovation and reduce the risks associated with real-world data collection, the adoption of synthetic data video generation technologies is expected to expand rapidly.

Another significant driver for the synthetic data video generator market is the increasing regulatory scrutiny surrounding data privacy and compliance. With stricter regulations such as GDPR and CCPA coming into force, organizations face mounting challenges in using real-world video data that may contain personally identifiable information. Synthetic data offers an effective solution by generating video datasets devoid of any real individuals, thereby ensuring compliance while still enabling advanced analytics and machine learning. Moreover, synthetic data video generators empower businesses to simulate rare or hazardous events that are difficult or unethical to capture in real life, further enhancing model robustness and preparedness. This advantage is particularly pronounced in healthcare, surveillance, and automotive industries, where data privacy and safety are paramount.

Technological advancements and increasing integration with cloud-based platforms are also propelling the synthetic data video generator market forward. The proliferation of cloud computing has made it easier for organizations of all sizes to access scalable synthetic data generation tools without significant upfront investments in hardware or infrastructure. Furthermore, the continuous evolution of generative adversarial networks (GANs) and other deep learning techniques has dramatically improved the realism and utility of synthetic video data. As a result, companies are now able to generate highly realistic, scenario-specific video datasets at scale, reducing both the time and cost required for AI development. This democratization of synthetic data technology is expected to unlock new opportunities across a wide array of applications, from entertainment content production to advanced surveillance systems.

From a regional perspective, North America currently dominates the synthetic data video generator market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The strong presence of leading AI technology providers, robust investment in research and development, and early adoption by automotive and healthcare sectors are key contributors to North America's market leadership. Europe is also witnessing significant growth, driven by stringent data privacy regulations and increased focus on AI-driven innovation. Meanwhile, Asia Pacific is emerging as a high-growth region, fueled by rapid digital transformation, expanding IT infrastructure, and increasing investments in autonomous systems and smart city projects. Latin America and Middle East & Africa, while still nascent, are expected to experience steady uptake as awareness and technological capabilities continue to grow.

Component Analysis

The synthetic data video generator market by comp

Snapshot img

Synthetic Data Generation Market Size 2025-2029

The synthetic data generation market size is forecast to increase by USD 4.39 billion, at a CAGR of 61.1% between 2024 and 2029.

The market is experiencing significant growth, driven by the escalating demand for data privacy protection. With increasing concerns over data security and the potential risks associated with using real data, synthetic data is gaining traction as a viable alternative. Furthermore, the deployment of large language models is fueling market expansion, as these models can generate vast amounts of realistic and diverse data, reducing the reliance on real-world data sources. However, high costs associated with high-end generative models pose a challenge for market participants. These models require substantial computational resources and expertise to develop and implement effectively. Companies seeking to capitalize on market opportunities must navigate these challenges by investing in research and development to create more cost-effective solutions or partnering with specialists in the field. Overall, the market presents significant potential for innovation and growth, particularly in industries where data privacy is a priority and large language models can be effectively utilized.

What will be the Size of the Synthetic Data Generation Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free SampleThe market continues to evolve, driven by the increasing demand for data-driven insights across various sectors. Data processing is a crucial aspect of this market, with a focus on ensuring data integrity, privacy, and security. Data privacy-preserving techniques, such as data masking and anonymization, are essential in maintaining confidentiality while enabling data sharing. Real-time data processing and data simulation are key applications of synthetic data, enabling predictive modeling and data consistency. Data management and workflow automation are integral components of synthetic data platforms, with cloud computing and model deployment facilitating scalability and flexibility. Data governance frameworks and compliance regulations play a significant role in ensuring data quality and security. Deep learning models, variational autoencoders (VAEs), and neural networks are essential tools for model training and optimization, while API integration and batch data processing streamline the data pipeline. Machine learning models and data visualization provide valuable insights, while edge computing enables data processing at the source. Data augmentation and data transformation are essential techniques for enhancing the quality and quantity of synthetic data. Data warehousing and data analytics provide a centralized platform for managing and deriving insights from large datasets. Synthetic data generation continues to unfold, with ongoing research and development in areas such as federated learning, homomorphic encryption, statistical modeling, and software development. The market's dynamic nature reflects the evolving needs of businesses and the continuous advancements in data technology.

How is this Synthetic Data Generation Industry segmented?

The synthetic data generation industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. End-userHealthcare and life sciencesRetail and e-commerceTransportation and logisticsIT and telecommunicationBFSI and othersTypeAgent-based modellingDirect modellingApplicationAI and ML Model TrainingData privacySimulation and testingOthersProductTabular dataText dataImage and video dataOthersGeographyNorth AmericaUSCanadaMexicoEuropeFranceGermanyItalyUKAPACChinaIndiaJapanRest of World (ROW)

By End-user Insights

The healthcare and life sciences segment is estimated to witness significant growth during the forecast period.In the rapidly evolving data landscape, the market is gaining significant traction, particularly in the healthcare and life sciences sector. With a growing emphasis on data-driven decision-making and stringent data privacy regulations, synthetic data has emerged as a viable alternative to real data for various applications. This includes data processing, data preprocessing, data cleaning, data labeling, data augmentation, and predictive modeling, among others. Medical imaging data, such as MRI scans and X-rays, are essential for diagnosis and treatment planning. However, sharing real patient data for research purposes or training machine learning algorithms can pose significant privacy risks. Synthetic data generation addresses this challenge by producing realistic medical imaging data, ensuring data privacy while enabling research

Dataset Card for synthetic-data-generation-with-llama3-405B

This dataset has been created with distilabel.

  Dataset Summary

This dataset contains a pipeline.yaml which can be used to reproduce the pipeline that generated it in distilabel using the distilabel CLI: distilabel pipeline run --config "https://huggingface.co/datasets/lukmanaj/synthetic-data-generation-with-llama3-405B/raw/main/pipeline.yaml"

or explore the configuration: distilabel pipeline info… See the full description on the dataset page: https://huggingface.co/datasets/lukmanaj/synthetic-data-generation-with-llama3-405B.

Surveillance data play a vital role in estimating the burden of diseases, pathogens, exposures, behaviors, and susceptibility in populations, providing insights that can inform the design of policies and targeted public health interventions. The use of Health and Demographic Surveillance System (HDSS) collected from the Kilifi region of Kenya, has led to the collection of massive amounts of data on the demographics and health events of different populations. This has necessitated the adoption of tools and techniques to enhance data analysis to derive insights that will improve the accuracy and efficiency of decision-making. Machine Learning (ML) and artificial intelligence (AI) based techniques are promising for extracting insights from HDSS data, given their ability to capture complex relationships and interactions in data. However, broad utilization of HDSS datasets using AI/ML is currently challenging as most of these datasets are not AI-ready due to factors that include, but are not limited to, regulatory concerns around privacy and confidentiality, heterogeneity in data laws across countries limiting the accessibility of data, and a lack of sufficient datasets for training AI/ML models. Synthetic data generation offers a potential strategy to enhance accessibility of datasets by creating synthetic datasets that uphold privacy and confidentiality, suitable for training AI/ML models and can also augment existing AI datasets used to train the AI/ML models. These synthetic datasets, generated from two rounds of separate data collection periods, represent a version of the real data while retaining the relationships inherent in the data. For more information please visit The Aga Khan University Website.

The size of the Synthetic Data Generation Market market was valued at USD 45.9 billion in 2023 and is projected to reach USD 65.9 billion by 2032, with an expected CAGR of 13.6 % during the forecast period. The Synthetic Data Generation Market involves creating artificial data that mimics real-world data while preserving privacy and security. This technique is increasingly used in various industries, including finance, healthcare, and autonomous vehicles, to train machine learning models without compromising sensitive information. Synthetic data is utilized for testing algorithms, improving AI models, and enhancing data analysis processes. Key trends in this market include the growing demand for privacy-compliant data solutions, advancements in generative modeling techniques, and increased investment in AI technologies. As organizations seek to leverage data-driven insights while mitigating risks associated with data privacy, the synthetic data generation market is poised for significant growth in the coming years.

In this research, we create synthetic data with features that are like data from IoT devices. We use an existing air quality dataset that includes temperature and gas sensor measurements. This real-time dataset includes component values for the Air Quality Index (AQI) and ppm concentrations for various polluting gas concentrations. We build a JavaScript Object Notation (JSON) model to capture the distribution of variables and structure of this real dataset to generate the synthetic data. Based on the synthetic dataset and original dataset, we create a comparative predictive model. Analysis of synthetic dataset predictive model shows that it can be successfully used for edge analytics purposes, replacing real-world datasets. There is no significant difference between the real-world dataset compared the synthetic dataset. The generated synthetic data requires no modification to suit the edge computing requirements. The framework can generate correct synthetic datasets based on JSON schema attributes. The accuracy, precision, and recall values for the real and synthetic datasets indicate that the logistic regression model is capable of successfully classifying data

Test Data Generation Tools Market Outlook

The global market size for Test Data Generation Tools was valued at USD 800 million in 2023 and is projected to reach USD 2.2 billion by 2032, growing at a CAGR of 12.1% during the forecast period. The surge in the adoption of agile and DevOps practices, along with the increasing complexity of software applications, is driving the growth of this market.

One of the primary growth factors for the Test Data Generation Tools market is the increasing need for high-quality test data in software development. As businesses shift towards more agile and DevOps methodologies, the demand for automated and efficient test data generation solutions has surged. These tools help in reducing the time required for test data creation, thereby accelerating the overall software development lifecycle. Additionally, the rise in digital transformation across various industries has necessitated the need for robust testing frameworks, further propelling the market growth.

The proliferation of big data and the growing emphasis on data privacy and security are also significant contributors to market expansion. With the introduction of stringent regulations like GDPR and CCPA, organizations are compelled to ensure that their test data is compliant with these laws. Test Data Generation Tools that offer features like data masking and data subsetting are increasingly being adopted to address these compliance requirements. Furthermore, the increasing instances of data breaches have underscored the importance of using synthetic data for testing purposes, thereby driving the demand for these tools.

Another critical growth factor is the technological advancements in artificial intelligence and machine learning. These technologies have revolutionized the field of test data generation by enabling the creation of more realistic and comprehensive test data sets. Machine learning algorithms can analyze large datasets to generate synthetic data that closely mimics real-world data, thus enhancing the effectiveness of software testing. This aspect has made AI and ML-powered test data generation tools highly sought after in the market.

Regional outlook for the Test Data Generation Tools market shows promising growth across various regions. North America is expected to hold the largest market share due to the early adoption of advanced technologies and the presence of major software companies. Europe is also anticipated to witness significant growth owing to strict regulatory requirements and increased focus on data security. The Asia Pacific region is projected to grow at the highest CAGR, driven by rapid industrialization and the growing IT sector in countries like India and China.

Synthetic Data Generation has emerged as a pivotal component in the realm of test data generation tools. This process involves creating artificial data that closely resembles real-world data, without compromising on privacy or security. The ability to generate synthetic data is particularly beneficial in scenarios where access to real data is restricted due to privacy concerns or regulatory constraints. By leveraging synthetic data, organizations can perform comprehensive testing without the risk of exposing sensitive information. This not only ensures compliance with data protection regulations but also enhances the overall quality and reliability of software applications. As the demand for privacy-compliant testing solutions grows, synthetic data generation is becoming an indispensable tool in the software development lifecycle.

Component Analysis

The Test Data Generation Tools market is segmented into software and services. The software segment is expected to dominate the market throughout the forecast period. This dominance can be attributed to the increasing adoption of automated testing tools and the growing need for robust test data management solutions. Software tools offer a wide range of functionalities, including data profiling, data masking, and data subsetting, which are essential for effective software testing. The continuous advancements in software capabilities also contribute to the growth of this segment.

In contrast, the services segment, although smaller in market share, is expected to grow at a substantial rate. Services include consulting, implementation, and support services, which are crucial for the successful deployment and management of test data generation tools. The increasing complexity of IT inf

Title: Rule-based Synthetic Data for Japanese GEC. Dataset Contents:This dataset contains two parallel corpora intended for the training and evaluating of models for the NLP (natural language processing) subtask of Japanese GEC (grammatical error correction). These are as follows:Synthetic Corpus - synthesized_data.tsv. This corpus file contains 2,179,130 parallel sentence pairs synthesized using the process described in [1]. Each line of the file consists of two sentences delimited by a tab. The first sentence is the erroneous sentence while the second is the corresponding correction.These paired sentences are derived from data scraped from the keyword-lookup site

Synthetic Data Generation Engine Market Outlook

According to our latest research, the global Synthetic Data Generation Engine market size reached USD 1.42 billion in 2024, reflecting a rapidly expanding sector driven by the escalating demand for advanced data solutions. The market is expected to achieve a robust CAGR of 37.8% from 2025 to 2033, propelling it to an estimated value of USD 21.8 billion by 2033. This exceptional growth is primarily fueled by the increasing need for high-quality, privacy-compliant datasets to train artificial intelligence and machine learning models in sectors such as healthcare, BFSI, and IT & telecommunications. As per our latest research, the proliferation of data-centric applications and stringent data privacy regulations are acting as significant catalysts for the adoption of synthetic data generation engines globally.

One of the key growth factors for the synthetic data generation engine market is the mounting emphasis on data privacy and compliance with regulations such as GDPR and CCPA. Organizations are under immense pressure to protect sensitive customer information while still deriving actionable insights from data. Synthetic data generation engines offer a compelling solution by creating artificial datasets that mimic real-world data without exposing personally identifiable information. This not only ensures compliance but also enables organizations to accelerate their AI and analytics initiatives without the constraints of data access or privacy risks. The rising awareness among enterprises about the benefits of synthetic data in mitigating data breaches and regulatory penalties is further propelling market expansion.

Another significant driver is the exponential growth in artificial intelligence and machine learning adoption across industries. Training robust and unbiased models requires vast and diverse datasets, which are often difficult to obtain due to privacy concerns, labeling costs, or data scarcity. Synthetic data generation engines address this challenge by providing scalable and customizable datasets for various applications, including machine learning model training, data augmentation, and fraud detection. The ability to generate balanced and representative data has become a critical enabler for organizations seeking to improve model accuracy, reduce bias, and accelerate time-to-market for AI solutions. This trend is particularly pronounced in sectors such as healthcare, automotive, and finance, where data diversity and privacy are paramount.

Furthermore, the increasing complexity of data types and the need for multi-modal data synthesis are shaping the evolution of the synthetic data generation engine market. With the proliferation of unstructured data in the form of images, videos, audio, and text, organizations are seeking advanced engines capable of generating synthetic data across multiple modalities. This capability enhances the versatility of synthetic data solutions, enabling their application in emerging use cases such as autonomous vehicle simulation, natural language processing, and biometric authentication. The integration of generative AI techniques, such as GANs and diffusion models, is further enhancing the realism and utility of synthetic datasets, expanding the addressable market for synthetic data generation engines.

From a regional perspective, North America continues to dominate the synthetic data generation engine market, accounting for the largest revenue share in 2024. The region's leadership is attributed to the strong presence of technology giants, early adoption of AI and machine learning, and stringent regulatory frameworks. Europe follows closely, driven by robust data privacy regulations and increasing investments in digital transformation. Meanwhile, the Asia Pacific region is emerging as the fastest-growing market, supported by expanding IT infrastructure, government-led AI initiatives, and a burgeoning startup ecosystem. Latin America and the Middle East & Africa are also witnessing gradual adoption, fueled by the growing recognition of synthetic data's potential to overcome data access and privacy challenges.

Objective: Biomechanical Machine Learning (ML) models, particularly deep-learning models, demonstrate the best performance when trained using extensive datasets. However, biomechanical data are frequently limited due to diverse challenges. Effective methods for augmenting data in developing ML models, specifically in the human posture domain, are scarce. Therefore, this study explored the feasibility of leveraging generative artificial intelligence (AI) to produce realistic synthetic posture data by utilizing three-dimensional posture data.Methods: Data were collected from 338 subjects through surface topography. A Variational Autoencoder (VAE) architecture was employed to generate and evaluate synthetic posture data, examining its distinguishability from real data by domain experts, ML classifiers, and Statistical Parametric Mapping (SPM). The benefits of incorporating augmented posture data into the learning process were exemplified by a deep autoencoder (AE) for automated feature representation.Results: Our findings highlight the challenge of differentiating synthetic data from real data for both experts and ML classifiers, underscoring the quality of synthetic data. This observation was also confirmed by SPM. By integrating synthetic data into AE training, the reconstruction error can be reduced compared to using only real data samples. Moreover, this study demonstrates the potential for reduced latent dimensions, while maintaining a reconstruction accuracy comparable to AEs trained exclusively on real data samples.Conclusion: This study emphasizes the prospects of harnessing generative AI to enhance ML tasks in the biomechanics domain.

Synthetic Data Generation Market Outlook 2032

The global synthetic data generation market size was USD 378.3 Billion in 2023 and is projected to reach USD 13,800 Billion by 2032, expanding at a CAGR of 31.1 % during 2024–2032. The market growth is attributed to the increasing demand for privacy-preserving synthetic data across the world.



Growing demand for privacy-preserving synthetic data is expected to boost the market. Synthetic data, being artificially generated, does not contain any personal or sensitive information, thereby ensuring data privacy. This has propelled organizations to adopt synthetic data generation methods, particularly in sectors where data privacy is paramount, such as healthcare and finance.

Impact of Artificial Intelligence (AI) in Synthetic Data Generation Market

Artificial Intelligence (AI) has significantly influenced the synthetic data generation market, transforming the way businesses operate and make decisions. The integration of AI in synthetic data generation has enhanced the efficiency and accuracy of data modeling, simulation, and analysis. AI algorithms, through machine learning and deep learning techniques, generate synthetic data that closely mimics real-world data, thereby providing a safe and effective alternative for data privacy concerns.



AI has led to the increased adoption of synthetic data in various sectors such as healthcare, finance, and retail, among others. Furthermore, AI-driven synthetic data generation aids in overcoming the challenges of data scarcity and bias, thereby improving the quality of predictive models and decision-making processes. The impact of AI on the synthetic data generation market is profound, fostering innovation, enhancing data security, and driving market growth. For instance,

• 
  

  In October 2023, K2view

Synthetic Data Generation Market Outlook

According to our latest research, the global synthetic data generation market size reached USD 1.6 billion in 2024, demonstrating robust expansion driven by increasing demand for high-quality, privacy-preserving datasets. The market is projected to grow at a CAGR of 38.2% over the forecast period, reaching USD 19.2 billion by 2033. This remarkable growth trajectory is fueled by the growing adoption of artificial intelligence (AI) and machine learning (ML) technologies across industries, coupled with stringent data privacy regulations that necessitate innovative data solutions. As per our latest research, organizations worldwide are increasingly leveraging synthetic data to address data scarcity, enhance AI model training, and ensure compliance with evolving privacy standards.

One of the primary growth factors for the synthetic data generation market is the rising emphasis on data privacy and regulatory compliance. With the implementation of stringent data protection laws such as the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States, enterprises are under immense pressure to safeguard sensitive information. Synthetic data offers a compelling solution by enabling organizations to generate artificial datasets that mirror the statistical properties of real data without exposing personally identifiable information. This not only facilitates regulatory compliance but also empowers organizations to innovate without the risk of data breaches or privacy violations. As businesses increasingly recognize the value of privacy-preserving data, the demand for advanced synthetic data generation solutions is set to surge.

Another significant driver is the exponential growth in AI and ML adoption across various sectors, including healthcare, finance, automotive, and retail. High-quality, diverse, and unbiased data is the cornerstone of effective AI model development. However, acquiring such data is often challenging due to privacy concerns, limited availability, or high acquisition costs. Synthetic data generation bridges this gap by providing scalable, customizable datasets tailored to specific use cases, thereby accelerating AI training and reducing dependency on real-world data. Organizations are leveraging synthetic data to enhance algorithm performance, mitigate data bias, and simulate rare events, which are otherwise difficult to capture in real datasets. This capability is particularly valuable in sectors like autonomous vehicles, where training models on rare but critical scenarios is essential for safety and reliability.

Furthermore, the growing complexity of data types—ranging from tabular and image data to text, audio, and video—has amplified the need for versatile synthetic data generation tools. Enterprises are increasingly seeking solutions that can generate multi-modal synthetic datasets to support diverse applications such as fraud detection, product testing, and quality assurance. The flexibility offered by synthetic data generation platforms enables organizations to simulate a wide array of scenarios, test software systems, and validate AI models in controlled environments. This not only enhances operational efficiency but also drives innovation by enabling rapid prototyping and experimentation. As the digital ecosystem continues to evolve, the ability to generate synthetic data across various formats will be a critical differentiator for businesses striving to maintain a competitive edge.

Regionally, North America leads the synthetic data generation market, accounting for the largest revenue share in 2024, followed closely by Europe and Asia Pacific. The dominance of North America can be attributed to the strong presence of technology giants, advanced research institutions, and a favorable regulatory environment that encourages AI innovation. Europe is witnessing rapid growth due to proactive data privacy regulations and increasing investments in digital transformation initiatives. Meanwhile, Asia Pacific is emerging as a high-growth region, driven by the proliferation of digital technologies and rising adoption of AI-powered solutions across industries. Latin America and the Middle East & Africa are also expected to experience steady growth, supported by government-led digitalization programs and expanding IT infrastructure.

Synthetic dataset for A Deep Learning Approach to Private Data Sharing of Medical Images Using Conditional GANs

Dataset specification:

• MRI images of Vertebral Units labelled based on region
• Dataset is comprised of 10000 pairs of images and labels
• Image and label pair number k can be selected by: synthetic_dataset['images'][k] and synthetic_dataset['regions'][k]
• Images are 3D of size (9, 64, 64)
• Regions are stored as an integer. Mapping is 0: cervical, 1: thoracic, 2: lumbar

Arxiv paper: https://arxiv.org/abs/2106.13199
Github code: https://github.com/tcoroller/pGAN/

Abstract:

Sharing data from clinical studies can facilitate innovative data-driven research and ultimately lead to better public health. However, sharing biomedical data can put sensitive personal information at risk. This is usually solved by anonymization, which is a slow and expensive process. An alternative to anonymization is sharing a synthetic dataset that bears a behaviour similar to the real data but preserves privacy. As part of the collaboration between Novartis and the Oxford Big Data Institute, we generate a synthetic dataset based on COSENTYX Ankylosing Spondylitis (AS) clinical study. We apply an Auxiliary Classifier GAN (ac-GAN) to generate synthetic magnetic resonance images (MRIs) of vertebral units (VUs). The images are conditioned on the VU location (cervical, thoracic and lumbar). In this paper, we present a method for generating a synthetic dataset and conduct an in-depth analysis on its properties of along three key metrics: image fidelity, sample diversity and dataset privacy.

The Synthetic Data Platform market is experiencing robust growth, driven by the increasing need for data privacy, escalating data security concerns, and the rising demand for high-quality training data for AI and machine learning models. The market's expansion is fueled by several key factors: the growing adoption of AI across various industries, the limitations of real-world data availability due to privacy regulations like GDPR and CCPA, and the cost-effectiveness and efficiency of synthetic data generation. We project a market size of approximately $2 billion in 2025, with a Compound Annual Growth Rate (CAGR) of 25% over the forecast period (2025-2033). This rapid expansion is expected to continue, reaching an estimated market value of over $10 billion by 2033. The market is segmented based on deployment models (cloud, on-premise), data types (image, text, tabular), and industry verticals (healthcare, finance, automotive). Major players are actively investing in research and development, fostering innovation in synthetic data generation techniques and expanding their product offerings to cater to diverse industry needs. Competition is intense, with companies like AI.Reverie, Deep Vision Data, and Synthesis AI leading the charge with innovative solutions. However, several challenges remain, including ensuring the quality and fidelity of synthetic data, addressing the ethical concerns surrounding its use, and the need for standardization across platforms. Despite these challenges, the market is poised for significant growth, driven by the ever-increasing need for large, high-quality datasets to fuel advancements in artificial intelligence and machine learning. The strategic partnerships and acquisitions in the market further accelerate the innovation and adoption of synthetic data platforms. The ability to generate synthetic data tailored to specific business problems, combined with the increasing awareness of data privacy issues, is firmly establishing synthetic data as a key component of the future of data management and AI development.

crate

This repository provides the necessary data and Python code to replicate the experiments and generate the figures presented in our manuscript: "Supporting data and code: Beyond Economic Dispatch: Modeling Renewable Purchase Agreements in Production Cost Models".

Contents:

• pownet.zip: Contains PowNet version 3.2, the specific version of the simulation software used in this study.
• inputs.zip: Contains essential modeling inputs required by PowNet for the experiments, including network data, and pre-generated synthetic load and solar time series.
• scripts.zip: Contains the Python scripts used for installing PowNet, optionally regenerating synthetic data, running simulation experiments, processing results, and generating figures.
• thai_data.zip (Reference Only): Contains raw data related to the 2023 Thai power system. This data served as a reference during the creation of the PowNet inputs for this study but is not required to run the replication experiments themselves. Code to process the raw data is also provided.

System Requirements:

• Python version 3.10+
• pip package manager

Setup Instructions:

1. Download and Unzip Core Files: Download pownet.zip, inputs.zip, scripts.zip, and thai_data.zip. Extract their contents into the same parent folder. Your directory structure should look like this:

   Parent_Folder/
   ├── pownet/    # from pownet.zip
   ├── inputs/    # from inputs.zip
   ├── scripts/    # from scripts.zip
   ├── thai_data.zip/    # from scripts.zip
   ├── figures/    # Created by scripts later
   ├── outputs/    # Created by scripts later
   

2. Install PowNet:

   • Open your terminal or command prompt.
   • Navigate into the pownet directory that you just extracted:

cd path/to/Parent_Folder/pownet

pip install -e .

• • These commands install PowNet and its required dependencies into your active Python environment.

Workflow and Usage:

Note: All subsequent Python script commands should be run from the scripts directory. Navigate to it first:

cd path/to/Parent_Folder/scripts

1. Generate Synthetic Time Series (Optional):

• This step is optional as the required time series files are already provided within the inputs directory (extracted from inputs.zip). If you wish to regenerate them:
• Run the generation scripts:

  python create_synthetic_load.py
  
  python create_synthetic_solar.py
  

• Evaluate the generated time series (optional):

  python eval_synthetic_load.py
  
  python eval_synthetic_solar.py
  

2. Calculate Total Solar Availability:

• Process solar scenarios using data from the inputs directory:

  python process_scenario_solar.py
  

3. Experiment 1: Compare Strategies for Modeling Purchase Obligations:

• Run the base case simulations for different modeling strategies:
  • No Must-Take (NoMT):

    python run_basecase.py --model_name "TH23NMT"
    

  • Zero-Cost Renewables (ZCR):

    python run_basecase.py --model_name "TH23ZC"
    

  • Penalized Curtailment (Proposed Method):

    python run_basecase.py --model_name "TH23"
    

• Run the base case simulation for the Minimum Capacity (MinCap) strategy:

  python run_min_cap.py
  
  This is a new script because we need to modify the objective function and add constraints.

4. Experiment 2: Simulate Partial-Firm Contract Switching:

• Run simulations comparing the base case with the partial-firm contract scenario:
  • Base Case Scenario:

    python run_scenarios.py --model_name "TH23"
    

  • Partial-Firm Contract Scenario:

    python run_scenarios.py --model_name "TH23ESB"
    

5. Visualize Results:

• Generate all figures presented in the manuscript:

  python run_viz.py
  

• Figures will typically be saved in afigures directory within the Parent_Folder.