Synthetic Data Generation Market By Component (Solution, Services), By Deployment Mode (On-Premise, Cloud), By Data Type (Tabular Data, Text Data, Image and Video Data, Others), By Application (AI Training and Development, Test Data Management, Data Sharing and Retention, Data Analytics, Others), By Industry Vertical (BFSI, Healthcare and Life Sciences, Transportation and Logistics, Government and Defense, IT and Telecommunication, Manufacturing, Media and Entertainment, Others): Global Opportunity Analysis and Industry Forecast, 2021-2031
Synthetic data is evolving as an advantageous solution for model development. The idea is to allow machine learning methodologies to learn the statistical information from a real data set and simulate it on a new simulated data set, without copying or transforming the original data. Synthetic data is artificially created and keeps the original data properties, safeguarding its business value while maintaining compliance. It is important to preserve the data quality and structure, ensuring high-quality data for purposes such as training machine learning models. Moreover, using synthetic data, organizations can achieve data set balance, address issues such as bias, and certify fair within the data sets used to develop data science initiatives.
On the contrary, recently, there has been a growing interest in generative models for applications such as creating new types of art or simulating video sequences. However, developments in tabular data seem to be less ambitious, despite being the most frequent type of data available in the world. Synthetic tabular data is disrupting industries like autonomous vehicles, healthcare, and financial services. The healthcare business embraces this novel idea, not only for addressing patients’ privacy concerns, but also for simulating synthetic genomic datasets or patient medical records in research projects. Such advantages provide lucrative opportunities for the market growth during the forecast period. Furthermore, many organizations such as government & defense, BFSI, robotics and other sectors are adopting synthetic data generation due to surge in demand of digitization. This factor creates lucrative growth opportunities in the market.
Surge in digitalization transformation across enterprises and rise in adoption of advanced technology such as AI and ML drive the growth of the market. However, lack of skilled workforce hamper the growth of the market. Furthermore, increase in demand for connected devices and IoT and other technologies is expected to provide the lucrative growth opportunities for the market in the upcoming years.
The synthetic data generation market is segmented on the basis of component, deployment mode, data type, application, industry vertical, and region. By component, it is bifurcated into solutions and services. By deployment mode, it is divided into on-premises and cloud. On the basis of data type, it is categorized into tabular data, text data, image and video data and others. By application, the market is segmented into AI training and development, test data management, data sharing and retention, data analytics and others. By industry vertical, the market is categorized into BFSI, IT and telecommunication, government and defense, healthcare and life sciences, manufacturing, transportation and logistics, media & entertainment, manufacturing and others. Region wise, it is analyzed across North America, Europe, Asia-Pacific and LAMEA.
The market players operating in the synthetic data generation market include Amazon.com, Inc., CVEDIA Inc., Datagen, Gretel Labs, IBM Corporation, Meta, Microsoft Corporation, Mostly AI, NVIDIA Corporation and Synthesis AI. These major players have adopted various key development strategies such as business expansion, new product launches, and partnerships, which help to drive the growth of the synthetic data generation market globally.
KEY BENEFITS FOR STAKEHOLDERS
The study provides an in-depth analysis of the global synthetic data generation market forecast along with the current and future trends to explain the imminent investment pockets.
Information about key drivers, restraints, and opportunities and their impact analysis on global synthetic data generation market trends is provided in the report.
The Porter’s five forces analysis illustrates the potency of the buyers and suppliers operating in the industry.
The quantitative analysis of the global synthetic data generation market from 2022 to 2031 is provided to determine the market potential.
Key Market Segments
By Component
Solution
Services
By Deployment Mode
On-Premise
Cloud
By Data Type
Tabular Data
Text Data
Image and Video Data
Others
By Application
AI Training and Development
Test Data Management
Data Sharing and Retention
Data Analytics
Others
By Industry Vertical
BFSI
Healthcare and Life Sciences
Transportation and Logistics
Government and Defense
IT and Telecommunication
Manufacturing
Media and Entertainment
Others
By Region
North America
U.S.
Canada
Europe
UK
Germany
France
Italy
Spain
Rest of Europe
Asia-Pacific
China
Japan
India
Australia
South Korea
Rest of Asia-Pacific
LAMEA
Latin America
Middle East
Africa
Key Market Players
Gretel Labs
Microsoft Corporation
Meta
IBM Corporation
NVIDIA Corporation
Synthesis AI
CVEDIA Inc.
Datagen
Mostly AI
Amazon.com, Inc.
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