Global Synthetic Data Software Market Size to Grow At 35.8% CAGR From 2024 to 2030

Global Synthetic Data Software Market Size (2024 - 2030)

As per our research Report, the Global Synthetic Data Software Market is forecasted to be growing at CAGR of 35.8% from 2024 to 2030.

Synthetic data is artificial data generated from original data and a model trained to replicate the traits and structure of the original data. A measure of the utility of the method and the model is the degree to which synthetic data is an accurate replica for the original data. Several techniques, such as deep learning algorithms or decision trees, can be utilized to carry out the generation process, also referred to as synthesis. Based on the original data type, synthetic data can be segmented into the following, the first type employs actual datasets, the second type employs knowledge gathered by analysts, and the third type is a mixture of these two types. In the field of image recognition, Generative Adversarial Networks (GANs), which were recently launched, are frequently utilized. Usually, they are made up of two neural networks training one another iteratively. The discriminator network attempts to distinguish synthetic images produced by the generator from actual ones by comparing them to one another. To ensure that the generated synthetic data does not contain original personal data, a privacy assurance assessment should be carried out, which assesses the degree to which data subjects can be identified in the synthetic data and the amount of new data that would be revealed if successful identification is accomplished.

Synthetic data is gaining popularity in the area of machine learning. It assists in training machine learning algorithms that requires a lot of labelled training data, which can be expensive or have restrictions on the usage of data. Furthermore, synthetic data can be used by manufacturers for software testing and quality control. Synthetic data can assist businesses and researchers in making the data repositories necessary to train and even pre-train machine learning models.

Synthetic data of good quality shows original data with high accuracy. Resultantly, sensitive performance data can be substituted for synthetic data in non-production settings, including AI training, analytics, and software testing or development. To make sure customer privacy while making data-driven decisions, businesses employ synthetic data versions of patient experiences, customer databases, medical information, and transaction data. Banking, healthcare, insurance, and telecommunications are some of the industries where synthetic data is employed as an industry-agnostic solution. Therefore, this element propels the demand for synthetic data.

Cutting-edge technologies, such as artificial intelligence (AI), machine learning (ML), and nanotechnologies, are being invested by businesses to improve operational effectiveness and create more revenue opportunities. Synthetic data will be a significant technology in handling data management challenges across domains of predictive analytics, privacy, security, and general data centricity. The advanced, synthetic datasets are scalable, privacy and security compliant and consist all of the original importance without the weight of sensitive information. 

The outbreak of the COVID-19 pandemic substantially affected the global synthetic data software market. The imposition of strict lockdowns, travelling restrictions, and social distancing measures across various nations hindered many companies' manufacturing capacities and caused a shortage of skilled personnel. The pandemic resulted disruptions in supply chains and distribution of goods and services, which decreased and delayed the output of synthetic datasets for training AI and ML models. These factors adversely impacted the growth of the global synthetic data software market. However, due to the travelling limitations and lockdowns in almost every country, several organizations shifted to remote work and online operations, which grew the demand for synthetic data software to comply with data privacy and security. Moreover, the escalation in demand for synthetic data software was witnessed in the healthcare industry as synthetic datasets are proven to be valuable in the testing and development of AI and ML algorithms in medical devices that are utilized to diagnose diseases and monitor and improve health conditions. These factors positively resulted in the market's growth. Therefore, the global synthetic data software market observed both challenges and opportunities during the difficult time of the COVID-19 pandemic.

Key Market Insights: 

  • The cloud-based segment held the highest market share in the year 2022. The growth can be credited to the advantages that cloud-based deployment offers over on-premise regarding scalability, budget-friendly, accessibility, security, and integration with other cloud-based services.

  • In 2022, the IT and telecommunications segment held the largest market share. The growth can be attributed to the rising requirement for synthetic data software due to the extensive amount of data generated on a day-to-day basis. Moreover, to cope with the evolving customer demands, various IT companies are adopting synthetic data software to assist in accelerating the testing and development of new products and services, which further influences the segment's growth.

  • In 2022, the region of North America held the highest share of the Global Synthetic Data Software Market. The growth can be attributed to the surging usage of synthetic data software in multiple industries to enhance business operations and customer experience. The high value investments in research and development of synthetic data technologies by the United States government also pushes the region's growth. Additionally, the region is home to several key market players, including Gretel, Synthesis AI, IBM Corporation, NVIDIA Corporation, and GenRocket.

  • However, Asia Pacific is expected to exhibit the highest growth due to the growing adoption of cloud-based services and the increase in penetration of cutting-edge technologies, including AI and ML.

Global Synthetic Data Software Market Segmentation:

By Deployment Mode

  • On-Premise

  • Cloud-Based

By Industry Vertical

  • BFSI

  • Transportation and Logistics

  • IT and Telecommunications

  • Government

  • Retail and E-commerce

  • Manufacturing

  • Healthcare and Life Sciences

  • Others

By Region

  • North America

  • Europe

  • Asia-Pacific

  • Middle East & Africa

  • South America

Request sample for this report:







Analyst Support

Every order comes with Analyst Support.


We offer customization to cater your needs to fullest.

Verified Analysis

We value integrity, quality and authenticity the most.