Synthetic Data Generation Market worth $2.1 billion by 2028, growing at a CAGR of 45.7% Report by MarketsandMarkets™

As per the report by MarketsandMarkets, the global Synthetic Data Generation Market size is projected to reach USD 2.1 billion by 2028, at a CAGR of 45.7%during the forecast period, 2023-2028

Chicago, July 05, 2023 (GLOBE NEWSWIRE) -- The global Synthetic Data Generation Market size to grow from USD 0.3 billion in 2023 to USD 2.1 billion by 2028, at a Compound Annual Growth Rate (CAGR) of 45.7% during the forecast period , according to a new report by MarketsandMarkets™. The growth of the synthetic data generation market can be attributed to several factors, including increasing concerns about data privacy and security. Also, the cost-effectiveness and time efficiency of generating synthetic data compared to collecting and labeling real-world data will drive the market's growth.

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269 - Tables
45 - Figures
372 - Pages

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Scope of the Report

Report MetricsDetails
Market size available for years2019 – 2028
Base year considered2022
Forecast period2023-2028
Forecast unitsValue (USD Billion)
Segments CoveredOffering (Solution/ Platform and Services), Data Type (Tabular, Text, Image and Video, Others), Application ( AI/ML Training and Development, Test Data Management, Data analytics & visualization, Enterprise Data Sharing,  Others), Vertical (Banking, Financial Services, and Insurance, Healthcare & Life sciences, Automotive & Transportation, Government & Defense, IT and ITeS, Manufacturing, Other Verticals) and Region
Regions coveredNorth America, Europe, Asia Pacific, Middle East & Africa, and Latin America
Companies coveredMicrosoft (US), Google (US), IBM (US), AWS (US), NVIDIA (US), OpenAI (US), Informatica (US), Broadcom (US),  Sogeti (France), Mphasis (India), Databricks (US), MOSTLY AI (Austria), Tonic (US), MDClone (Israel) TCS (India), Hazy (UK), Synthesia (UK), Synthesized (UK), Facteus (US), Anyverse (Spain), Neurolabs (Scotland), (US), Gretel (US), OneView  (Israel), GenRocket  (US), YData (US), CVEDIA (UK), Syntheticus (Switzerland), AnyLogic (US), Bifrost AI (US), Anonos (US)

Synthetic data is information generated on a computer to augment or replace real data to improve AI models, protect sensitive data, and mitigate bias. Synthetic data allows businesses to address concerns about data privacy and security by generating realistic and representative data without exposing sensitive information. Generating synthetic data is often more cost-effective and time-efficient than collecting and labeling real-world data. It eliminates the need for extensive data collection processes, reducing costs and accelerating development timelines.

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Synthetic data enables businesses to generate large volumes of diverse datasets, providing more comprehensive coverage of scenarios and use cases. This scalability and diversity are valuable for training and testing machine learning models. Synthetic data enables businesses to create datasets tailored to specific applications or edge cases that may be challenging to capture with real-world data. This customization helps improve model performance, accuracy, and generalization capabilities.

By data type, text data to segment to record the highest growth rate during the forecast period

By data type, the text data segment has the highest growth rate during the forecast period. The increased demand for artificial intelligence (AI) and machine learning (ML) applications requires large amounts of data to train and develop models, thus driving the text data segment.

Among applications, the Test data management segment recorded the second-highest market share during the forecast period.

Under the applications segment, the Test data management segment is expected to have the second-highest market share during the forecast period. The need for high-quality, diverse, and representative data for testing and validation purposes will drive the segment. Businesses can enhance the effectiveness and efficiency of their testing processes using synthetic data leading to improved product quality, faster time-to-market, and reduced costs associated with traditional test data management approaches.

Top Trends in Global Synthetic Data Generation Market

  • rising need for data that protects privacy Organisations are looking for strategies to preserve sensitive data while still being able to build and test their algorithms and models as data privacy legislation and concerns increase. Synthetic data production offers a practical answer by producing artificial data devoid of any personally identifiable information (PII) but retaining the statistical characteristics of real data.
  • The demand for high-quality training data has increased as a result of the expansion of AI and machine learning applications across numerous industries. Combining synthetic data creation methods with AI algorithms can produce a variety of large-scale datasets that can be used to more efficiently train and evaluate AI models.
  • Deep learning methods, such variational autoencoders (VAEs) and generative adversarial networks (GANs), have demonstrated considerable promise for producing synthetic data that is as realistic as possible. These techniques enable the development of more precise synthetic datasets by capturing complicated patterns and connections found in real data.
  • Many various businesses, including healthcare, banking, retail, and autonomous driving, are finding uses for synthetic data generation. Each sector has specific data needs, and by customizing synthetic data to mirror the traits and distributions of the target domain, it is possible to design and test AI systems more successfully.
  • Data providers and AI firms are collaborating and forming partnerships to address the rising need for high-quality synthetic data. In order to create more specialized and tailored synthetic data solutions, data suppliers contribute their skills in creating synthetic datasets, and AI businesses add their subject expertise and understanding of particular industry demands.

Key Industry Development

  • Healthcare, banking, retail, automotive, and cybersecurity are just a few of the areas that have seen a surge in their use of synthetic data generation. The market has grown dramatically as businesses realize the value of synthetic data in addressing privacy issues and improving data analytics.
  • Partnerships and collaborations between data providers, AI firms, and industry-specific organizations have increased in order to speed innovation and address the varying needs of many industries. These partnerships seek to give specialized synthetic data solutions by fusing domain knowledge, domain experience, and knowledge of data generating methodologies.
  • Deep learning methods have advanced and improved the production of realistic synthetic data, including generative adversarial networks (GANs) and variational autoencoders (VAEs). To improve the quality and diversity of synthetic datasets, researchers and practitioners have been actively investigating new architectures and techniques.
  • There have been efforts made towards standardization in order to create benchmarks and guarantee the calibre of synthetic data generating techniques. To encourage transparency and comparability among various techniques, organizations and academic communities have been focusing on establishing evaluation metrics, creating benchmark datasets, and exchanging best practices.
  • The moral ramifications of creating synthetic data have drawn more attention. Concerns about biases, fairness, and potential exploitation of synthetic data must be addressed as the usage of synthetic data increases. To ensure responsible use and guard against unforeseen consequences, legal frameworks and regulations are being created.

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