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.

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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

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.

The synthetic data generation market is experiencing explosive growth, driven by the increasing need for high-quality data in various applications, including AI/ML model training, data privacy compliance, and software testing. The market, currently estimated at $2 billion in 2025, is projected to experience a Compound Annual Growth Rate (CAGR) of 25% from 2025 to 2033, reaching an estimated $10 billion by 2033. This significant expansion is fueled by several key factors. Firstly, the rising adoption of artificial intelligence and machine learning across industries demands large, high-quality datasets, often unavailable due to privacy concerns or data scarcity. Synthetic data provides a solution by generating realistic, privacy-preserving datasets that mirror real-world data without compromising sensitive information. Secondly, stringent data privacy regulations like GDPR and CCPA are compelling organizations to explore alternative data solutions, making synthetic data a crucial tool for compliance. Finally, the advancements in generative AI models and algorithms are improving the quality and realism of synthetic data, expanding its applicability in various domains. Major players like Microsoft, Google, and AWS are actively investing in this space, driving further market expansion. The market segmentation reveals a diverse landscape with numerous specialized solutions. While large technology firms dominate the broader market, smaller, more agile companies are making significant inroads with specialized offerings focused on specific industry needs or data types. The geographical distribution is expected to be skewed towards North America and Europe initially, given the high concentration of technology companies and early adoption of advanced data technologies. However, growing awareness and increasing data needs in other regions are expected to drive substantial market growth in Asia-Pacific and other emerging markets in the coming years. The competitive landscape is characterized by a mix of established players and innovative startups, leading to continuous innovation and expansion of market applications. This dynamic environment indicates sustained growth in the foreseeable future, driven by an increasing recognition of synthetic data's potential to address critical data challenges across industries.

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.

Synthetic Data Generation Market Size

As per the latest insights from Market.us, the Global Synthetic Data Generation Market is set to reach USD 6,637.98 million by 2034, expanding at a CAGR of 35.7% from 2025 to 2034. The market, valued at USD 313.50 million in 2024, is witnessing rapid growth due to rising demand for high-quality, privacy-compliant, and AI-driven data solutions.

North America dominated in 2024, securing over 35% of the market, with revenues surpassing USD 109.7 million. The region’s leadership is fueled by strong investments in artificial intelligence, machine learning, and data security across industries such as healthcare, finance, and autonomous systems. With increasing reliance on synthetic data to enhance AI model training and reduce data privacy risks, the market is poised for significant expansion in the coming years.

https://market.us/wp-content/uploads/2025/03/Synthetic-Data-Generation-Market-Size.png" alt="Synthetic Data Generation Market Size" class="wp-image-143209">

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

Supplementary files for article A genetically-optimised artificial life algorithm for complexity-based synthetic dataset generation

Algorithmic evaluation is a vital step in developing new approaches to machine learning and relies on the availability of existing datasets. However, real-world datasets often do not cover the necessary complexity space required to understand an algorithm’s domains of competence. As such, the generation of synthetic datasets to fill gaps in the complexity space has gained attention, offering a means of evaluating algorithms when data is unavailable. Existing approaches to complexity-focused data generation are limited in their ability to generate solutions that invoke similar classification behaviour to real data. The present work proposes a novel method (Sy:Boid) for complexity-based synthetic data generation, adapting and extending the Boid algorithm that was originally intended for computer graphics simulations. Sy:Boid embeds the modified Boid algorithm within an evolutionary multi-objective optimisation algorithm to generate synthetic datasets which satisfy predefined magnitudes of complexity measures. Sy:Boid is evaluated and compared to labelling-based and sampling-based approaches to data generation to understand its ability to generate a wide variety of realistic datasets. Results demonstrate Sy:Boid is capable of generating datasets across a greater portion of the complexity space than existing approaches. Furthermore, the produced datasets were observed to invoke very similar classification behaviours to that of real data.

The Synthetic Data Generation Market size is expected to reach a valuation of USD 36.09 Billion in 2033 growing at a CAGR of 39.45%. The research report classifies market by share, trend, demand and based on segmentation by Data Type, Modeling Type, Offering, Application, End Use and Regional Outloo...

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.

Synthetic dataset:

generated.csv - synthetic datasets containing 41,185 clinical note samples spanning 219 ICD-10 codes.

Data field Description

idx Unique sample identifier.

ICD-10 The targeted ICD-10 code used for prior data sampling.

generation_model The model used for sample generation (GTP-3.5, GPT-4, LLaMA-7b, LLaMA-13b)

prompt Prompt used for sample generation.

prior Type of prior data used for sample generation.

example Bool variable for the presence or… See the full description on the dataset page: https://huggingface.co/datasets/Glebkaa/MedSyn-synthetic.

The synthetic data generation market is projected to be worth USD 0.3 billion in 2024. The market is anticipated to reach USD 13.0 billion by 2034. The market is further expected to surge at a CAGR of 45.9% during the forecast period 2024 to 2034.

Attributes	Key Insights
Synthetic Data Generation Market Estimated Size in 2024	USD 0.3 billion
Projected Market Value in 2034	USD 13.0 billion
Value-based CAGR from 2024 to 2034	45.9%

Country-wise Insights

Countries	Forecast CAGRs from 2024 to 2034
The United States	46.2%
The United Kingdom	47.2%
China	46.8%
Japan	47.0%
Korea	47.3%

Category-wise Insights

Category	CAGR through 2034
Tabular Data	45.7%
Sandwich Assays	45.5%

Report Scope

Attribute	Details
Estimated Market Size in 2024	US$ 0.3 billion
Projected Market Valuation in 2034	US$ 13.0 billion
Value-based CAGR 2024 to 2034	45.9%
Forecast Period	2024 to 2034
Historical Data Available for	2019 to 2023
Market Analysis	Value in US$ Billion
Key Regions Covered	North America Latin America Western Europe Eastern Europe South Asia and Pacific East Asia The Middle East & Africa
Key Market Segments Covered	Data Type Modeling Type Offering Application End Use Region
Key Countries Profiled	The United States Canada Brazil Mexico Germany France France Spain Italy Russia Poland Czech Republic Romania India Bangladesh Australia New Zealand China Japan South Korea GCC countries South Africa Israel
Key Companies Profiled	Mostly AI CVEDIA Inc. Gretel Labs Datagen NVIDIA Corporation Synthesis AI Amazon.com, Inc. Microsoft Corporation IBM Corporation Meta

Ainnotate’s proprietary dataset generation methodology based on large scale generative modelling and Domain randomization provides data that is well balanced with consistent sampling, accommodating rare events, so that it can enable superior simulation and training of your models.

Ainnotate currently provides synthetic datasets in the following domains and use cases.

Internal Services - Visa application, Passport validation, License validation, Birth certificates Financial Services - Bank checks, Bank statements, Pay slips, Invoices, Tax forms, Insurance claims and Mortgage/Loan forms Healthcare - Medical Id cards

Synthetic Data Generation Market size was valued at USD 0.4 Billion in 2024 and is projected to reach USD 9.3 Billion by 2032, growing at a CAGR of 46.5 % from 2026 to 2032.

The Synthetic Data Generation Market is driven by the rising demand for AI and machine learning, where high-quality, privacy-compliant data is crucial for model training. Businesses seek synthetic data to overcome real-data limitations, ensuring security, diversity, and scalability without regulatory concerns. Industries like healthcare, finance, and autonomous vehicles increasingly adopt synthetic data to enhance AI accuracy while complying with stringent privacy laws.

Additionally, cost efficiency and faster data availability fuel market growth, reducing dependency on expensive, time-consuming real-world data collection. Advancements in generative AI, deep learning, and simulation technologies further accelerate adoption, enabling realistic synthetic datasets for robust AI model development.

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

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.

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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.

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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.

Abstract

The dataset is a relational dataset of 8,000 households households, representing a sample of the population of an imaginary middle-income country. The dataset contains two data files: one with variables at the household level, the other one with variables at the individual level. It includes variables that are typically collected in population censuses (demography, education, occupation, dwelling characteristics, fertility, mortality, and migration) and in household surveys (household expenditure, anthropometric data for children, assets ownership). The data only includes ordinary households (no community households). The dataset was created using REaLTabFormer, a model that leverages deep learning methods. The dataset was created for the purpose of training and simulation and is not intended to be representative of any specific country.

The full-population dataset (with about 10 million individuals) is also distributed as open data.

Geographic coverage

The dataset is a synthetic dataset for an imaginary country. It was created to represent the population of this country by province (equivalent to admin1) and by urban/rural areas of residence.

Analysis unit

Household, Individual

Universe

The dataset is a fully-synthetic dataset representative of the resident population of ordinary households for an imaginary middle-income country.

Kind of data

ssd

Sampling procedure

The sample size was set to 8,000 households. The fixed number of households to be selected from each enumeration area was set to 25. In a first stage, the number of enumeration areas to be selected in each stratum was calculated, proportional to the size of each stratum (stratification by geo_1 and urban/rural). Then 25 households were randomly selected within each enumeration area. The R script used to draw the sample is provided as an external resource.

Mode of data collection

other

Research instrument

The dataset is a synthetic dataset. Although the variables it contains are variables typically collected from sample surveys or population censuses, no questionnaire is available for this dataset. A "fake" questionnaire was however created for the sample dataset extracted from this dataset, to be used as training material.

Cleaning operations

The synthetic data generation process included a set of "validators" (consistency checks, based on which synthetic observation were assessed and rejected/replaced when needed). Also, some post-processing was applied to the data to result in the distributed data files.

Response rate

This is a synthetic dataset; the "response rate" is 100%.