Synthetic Data Generation Market Size & Share, by Modelling (Direct, Agent); Offering (Fully, Partially, Hybrid); Data Type (Tabular, Text, Image & Video); Application (AI Training & Development, Test Data Management, Data Sharing & Retention, Data Analytics); Vertical (BFSI, Healthcare & Life Sciences, Transportation & Logistics, Government & Defense, IT & Telecommunication, Manufacturing, Media & Entertainment) - Global Supply & Demand Analysis, Growth Forecasts, Statistics Report 2025-2037

Growth Drivers

• Growing Need for Security and Privacy of Data- The need for synthetic data a realistic duplicate of the real data collection with comparable statistical characteristics is driven by the growing privacy hazards associated with gathering real-world statistics. This synthetic data has various benefits in terms of privacy, scalability, and variety and can be utilized in place of genuine data.
  For example, in April 2023, Betterdata, a Singapore-based startup, announced that it would secure confidential data and improve machine learning models by using synthetic data that resembles real-world datasets in terms of structure and characteristics without revealing any personal or sensitive information about an individual.
• Increased Use of Large Language Models (LLM)- With the aid of enormous datasets, language models are used in the production of several websites and other applications. Large Language Models (LLM) are learning algorithms that assist in the translation, generation, and prediction of text and other types of information. A language model called the Generative Pre-trained Transformer (GPT) uses the GPT-1, GPT-2, and GPT-3 models to generate text data. With 175 million machine learning parameters, GPT-3 is the most sophisticated model and has produced a sizable dataset of conversational data.
  The ongoing creation of websites and other database solutions takes use of the need for language models in a number of sectors, including computing, retail, healthcare, and other industries. Various end users use these language models for code generation, fraud detection, image annotation, text production, and conversational AI.
• Growth of the Market Was Accelerated by Increasing Use of AI and ML Technologies to Synthesize Complex Database During Pandemic- The increasing adoption of artificial intelligence (AI) and machine learning (ML) technology in several industries, such as banking and financial services, healthcare, media & entertainment, automotive, and others, aids in protecting private data from online dangers. The use of synthetic data promotes internal data sharing inside the company, which greatly aids in the safe storage of extremely complex structural data by adhering to security guidelines. Therefore, during the COVID-19 crisis, the use of synthetic data preserved data privacy and mimicked the statistical characteristics of the operational data without endangering the privacy of an individual or an organization.

Challenges

• Inaccurate and unrealistic data impedes market expansion- Users can test and share virtual replicas of datasets created using synthetic data production. Furthermore, it is challenging for this method to capture the fine details of specialist models and real-world photographs. Maintaining the synthetic dataset over time is difficult since it relies on real-world data and varies as a result of inventions and advancements. Organizations should therefore routinely verify the accuracy and dependability of the synthetic data.
  This aspect substantially impedes the growth of the synthetic data generation market by degrading the quality and realism of the synthetic data.
• Lack of maturity in the market is anticipated to impede market growth.
• The use of phony data poses privacy risks that could impede market expansion.