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Synthetic Data Generation Market Size 2024-2028

The synthetic data generation market size is forecast to increase by USD 2.88 billion at a CAGR of 60.02% between 2023 and 2028.

The global synthetic data generation market is expanding steadily, driven by the growing need for privacy-compliant data solutions and advancements in AI technology. Key factors include the increasing demand for data to train machine learning models, particularly in industries like healthcare services and finance where privacy regulations are strict and the use of predictive analytics is critical, and the use of generative AI and machine learning algorithms, which create high-quality synthetic datasets that mimic real-world data without compromising security.
This report provides a detailed analysis of the global synthetic data generation market, covering market size, growth forecasts, and key segments such as agent-based modeling and data synthesis. It offers practical insights for business strategy, technology adoption, and compliance planning. A significant trend highlighted is the rise of synthetic data in AI training, enabling faster and more ethical development of models. One major challenge addressed is the difficulty in ensuring data quality, as poorly generated synthetic data can lead to inaccurate outcomes.
For businesses aiming to stay competitive in a data-driven global landscape, this report delivers essential data and strategies to leverage synthetic data trends and address quality challenges, ensuring they remain leaders in innovation while meeting regulatory demands

What will be the Size of the Market During the Forecast Period?

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Synthetic data generation offers a more time-efficient solution compared to traditional methods of data collection and labeling, making it an attractive option for businesses looking to accelerate their AI and machine learning projects. The market represents a promising opportunity for organizations seeking to overcome the challenges of data scarcity and privacy concerns while maintaining data diversity and improving the efficiency of their artificial intelligence and machine learning initiatives. By leveraging this technology, technology decision-makers can drive innovation and gain a competitive edge in their respective industries.

Market Segmentation

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

End-user

  Healthcare and life sciences
  Retail and e-commerce
  Transportation and logistics
  IT and telecommunication
  BFSI and others


Type

  Agent-based modelling
  Direct modelling


Data

  Tabular Data
  Text Data
  Image & Video Data
  Others


Offering Band

  Fully Synthetic Data
  Partially Synthetic Data
  Hybrid Synthetic Data


Application

  Data Protection
  Data Sharing
  Predictive Analytics
  Natural Language Processing
  Computer Vision Algorithms
  Others


Geography

  North America

    US
    Canada
    Mexico


  Europe

    Germany
    UK
    France
    Italy


  APAC

    China
    Japan
    India


  Middle East and Africa



  South America

By End-user Insights

The healthcare and life sciences segment is estimated to witness significant growth during the forecast period. In the thriving healthcare and life sciences sector, synthetic data generation is gaining significant traction as a cost-effective and time-efficient alternative to utilizing real-world data. This market segment's rapid expansion is driven by the increasing demand for data-driven insights and the importance of safeguarding sensitive information. One noteworthy application of synthetic data generation is in the realm of computer vision, specifically with geospatial imagery and medical imaging.

For instance, in healthcare, synthetic data can be generated to replicate medical imaging, such as MRI scans and X-rays, for research and machine learning model development without compromising patient privacy. Similarly, in the field of physical security, synthetic data can be employed to enhance autonomous vehicle simulation, ensuring optimal performance and safety without the need for real-world data. By generating artificial datasets, organizations can diversify their data sources and improve the overall quality and accuracy of their machine learning models.

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The healthcare and life sciences segment was valued at USD 12.60 million in 2018 and showed a gradual increase during the forecast period.

Regional Insights

North America is estimated to contribute 36% to the growth of the global market during the forecast period. Technavio's analysts have elaborately explained the regional trends and drivers that shape the m

Dataset Card for test-data-generator

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/franciscoflorencio/test-data-generator/raw/main/pipeline.yaml"

or explore the configuration: distilabel pipeline info --config… See the full description on the dataset page: https://huggingface.co/datasets/franciscoflorencio/test-data-generator.

The global synthetic data market size is projected to grow from USD 0.4 billion in the current year to USD 19.22 billion by 2035, representing a CAGR of 42.14%, during the forecast period till 2035

This dataset compiles estimated generator unavailability for eight countries in Northwest Europe, plus Spain. The advantages and limitations of the data are described in detail in the paper submitted to the PMAPS 2022 (Manchester) conference, “Comparing Generator Unavailability Models with Empirical Distributions from Open Energy Datasets” (submitted); the code used to generate the csvs in this dataset are provided at https://github.com/deakinmt/entsoe_outage_models

The dataset consists of forced, planned and total outages, calculated by aggregating the unavailabilities reported in an individual balancing zone. An estimate of the uncertainty due to apparent inconsistencies in outage reports is also provided (also described in the paper).

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 Outlook.

The Synthea generated data is provided here as a 1,000 person (1k), 100,000 person (100k), and 2,800,000 persom (2.8m) data sets in the OMOP Common Data Model format. SyntheaTM is a synthetic patient generator that models the medical history of synthetic patients. Our mission is to output high-quality synthetic, realistic but not real, patient data and associated health records covering every aspect of healthcare. The resulting data is free from cost, privacy, and security restrictions. It can be used without restriction for a variety of secondary uses in academia, research, industry, and government (although a citation would be appreciated). You can read our first academic paper here: https://doi.org/10.1093/jamia/ocx079

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Generator Market In Data Centers Size 2024-2028

The generator market in data centers size is forecast to increase by USD 4.26 billion at a CAGR of 8.56% between 2023 and 2028. In the realm of data center operations, power reliability emerges as a critical factor, driving the market's growth. Next-generation power monitoring and management software are increasingly being adopted to ensure uninterrupted power supply and enhance overall efficiency. However, the data center industry's carbon footprint is a significant concern, leading to the exploration of renewable energy sources such as wind, solar, and hydroelectric power. Micro-economic factors, including the rising cost of fossil fuels and the growing popularity of nuclear energy, are also influencing market dynamics. Edge computing sites are gaining traction, necessitating the need for power solutions that cater to their unique requirements.

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The market play a pivotal role in the digital transformation of businesses, enabling the storage, processing, and dissemination of critical information. However, power interruptions and system downtime can lead to significant information loss and revenue damage. To mitigate these risks, data center operators are increasingly investing in power backup solutions. Power density, the amount of power used per unit area, is a critical factor in data center design. Edge data centers, which are smaller and closer to the source of data generation, require innovative power backup solutions due to their limited space.

Moreover, 5G technology and edge computing are driving the growth of edge data centers, necessitating the development of compact, efficient power backup systems. Power costs are a significant expense for data center operators. Fuel cells, solar-powered data parks, natural gas generators, and diesel generators are among the power backup solutions that offer cost-effective alternatives to traditional grid power. Li-ion batteries are gaining popularity as they provide high energy density and long cycle life. Colocation service providers offer customized capacity solutions to meet the unique power requirements of their clients. Power backup solutions, including backup power systems and power loss prediction technologies, are essential components of their offerings.

Furthermore, these solutions ensure uninterrupted power supply and enhance data center reliability. Electricity is the primary power source for data centers. Power backup solutions provide a safety net against power interruptions, ensuring business continuity. Power loss prediction technologies enable data center operators to anticipate power outages and take preventive measures. The generator market is witnessing significant growth due to the increasing demand for power backup solutions. Fuel cells, solar-powered data parks, natural gas generators, and diesel generators are among the generator types that cater to the power backup needs of data centers. In conclusion, power backup solutions are a critical component of data center infrastructure.

Market Segmentation

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Type

  Diesel
  Gas


Capacity

  Less than 1MW
  1MW-2MW
  More than 2MW


Geography

  North America

    US


  Europe

    UK


  APAC

    China
    Japan


  South America



  Middle East and Africa

By Type Insights

The diesel segment is estimated to witness significant growth during the forecast period.In the data center industry, diesel generators play a significant role in providing power during fluctuating or transient scenarios. Their high-torque performance characteristic makes them an ideal choice for data centers with high power density requirements. Diesel generators come in various capacity ranges, making them a versatile option for data centers of all sizes. The diesel generator system consists of several components, including the diesel engine, generating unit, fuel storage/supply, and electrical switchgear. These generators are popular due to their reliability, safety, and minimal maintenance requirements. The output power capacity of diesel generators is greater than other types, making them suitable for large data center infrastructure.

Furthermore, diesel fuel is the most commonly used fuel in generators installed in data centers. The cost-effectiveness of diesel generators is another reason for their popularity. However, electricity prices and taxes can impact the overall cost of operating a data center with diesel generators. Edge data centers and colocation service providers are increasingly adopting 5G technology, which may require even more power density

The HazWaste database contains generator (companies and/or individuals) site and mailing address information, waste generation, the amount of waste generated etc. of all the hazardous waste generators in Vermont. Database was developed in early 1990's for program management and to meet EPA Authorization requirements. The database has been updated to more modern data systems periodically.�

What contender will emerge as the next big creator economy company? To find out, we've built a database of more than 500 global startups serving the millions of individuals making money off their online followings. Many founders see an opportunity to help creators connect with fans. Others have developed artificial intelligent tools or financial management services for creators. U.S. creator startups have raised more than $9.8 billion since early 2021, and creator startups based outside the U.S. have raised more than $4 billion in that period. The database comes from our reporting, founders and investors, and estimates from PitchBook.

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.

Data Creator (2010) is a company. It is located in Redditch, the United Kingdom and was founded in 2010. The company is part of the Information Technology sector, specifically in the Software industry.

The Data Center Backup Generator Market Report is Segmented by Product Type (Diesel, Natural Gas, and Other Product Types), Capacity (Less Than 1MW, 1-2MW, Greater Than 2MW), Tier (Tier I and II, Tier III, Tier IV), and Geography (North America, Europe, Asia-Pacific, Latin America, and Middle East and Africa). The Market Sizes and Forecasts are Provided in Terms of Value (USD) for all the Above Segments.

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.

The Synthetic Data Generation Marketsize was valued at USD 288.5 USD Million in 2023 and is projected to reach USD 1920.28 USD Million by 2032, exhibiting a CAGR of 31.1 % during the forecast period.Synthetic data generation stands for the generation of fake datasets that resemble real datasets with reference to their data distribution and patterns. It refers to the process of creating synthetic data points utilizing algorithms or models instead of conducting observations or surveys. There is one of its core advantages: it can maintain the statistical characteristics of the original data and remove the privacy risk of using real data. Further, with synthetic data, there is no limitation to how much data can be created, and hence, it can be used for extensive testing and training of machine learning models, unlike the case with conventional data, which may be highly regulated or limited in availability. It also helps in the generation of datasets that are comprehensive and include many examples of specific situations or contexts that may occur in practice for improving the AI system’s performance. The use of SDG significantly shortens the process of the development cycle, requiring less time and effort for data collection as well as annotation. It basically allows researchers and developers to be highly efficient in their discovery and development in specific domains like healthcare, finance, etc. Key drivers for this market are: Growing Demand for Data Privacy and Security to Fuel Market Growth. Potential restraints include: Lack of Data Accuracy and Realism Hinders Market Growth. Notable trends are: Growing Implementation of Touch-based and Voice-based Infotainment Systems to Increase Adoption of Intelligent Cars.

The synthetic data generation market size is projected to grow from USD 307.42 million to USD 18.23 billion, witnessing a CAGR of over 36.9% during the forecast period, between 2025 and 2037. North America region is attributed to hold the largest revenue share of about 33% by 2037 due to the increasing technological advancements in the region.

A 2022 survey of adults in the United States found that over 50 percent of them expected the companies to handle their collected data securely, and only that did not make them have a better opinion of a company. When it came to different generations, Gen Z was the less concerned group, with 31 percent of respondents not knowing or having no opinion regarding this. On the other hand, baby boomers were more interested in their data's safety, with 75 percent stating that keeping their data secure is their basic expectation from the companies.

Analyze the market segmentation of the Synthetic Data Generation (SDG) industry. Gain insights into market share distribution with a detailed breakdown of key segments and their growth.

Explore the historical Whois records related to data-generator.com (Domain). Get insights into ownership history and changes over time.

Test Data Management Market size was valued at USD 1.54 Billion in 2024 and is projected to reach USD 2.97 Billion by 2031, growing at a CAGR of 11.19% from 2024 to 2031.

Test Data Management Market Drivers

Increasing Data Volumes: The exponential growth in data generated by businesses necessitates efficient management of test data. Effective TDM solutions help organizations handle large volumes of data, ensuring accurate and reliable testing processes.

Need for Regulatory Compliance: Stringent data privacy regulations, such as GDPR, HIPAA, and CCPA, require organizations to protect sensitive data. TDM solutions help ensure compliance by masking or anonymizing sensitive data used in testing environments.

Adoption of DevOps and Agile Methodologies: The shift towards DevOps and Agile development practices increases the demand for TDM solutions. These methodologies require continuous testing and integration, necessitating efficient management of test data to maintain quality and speed.

Explore the Data Center Generator Global Market Report 2025 Market trends! Covers key players, growth rate 7.5% CAGR, market size $8.99 Billion, and forecasts to 2033. Get insights now!