Synthetic Data Generation Market Trends

Growth Drivers

• Growing need for data security: Synthetic data has proven to be an efficacious tool in unleashing the possibilities of data without compromising privacy. Market players in various sectors such as health, finance, insurance, etc. are opting for synthetic data to maximize the utility of data while also shielding consumer privacy. Additionally, synthetic data plays a prominent role in addressing crucial issues such as fraud detection, risk modeling, etc. The alarming rate of cases of data breaches is compelling market players to adopt mitigation methods. According to a report published by Harvard Business Review in February 2024, there was a 20% surge in data breach cases from 2022 to 2023 globally. The rising need for security and privacy of data, the market is projected to witness significant growth.

• Increased use of Large Language Models (LLM):  Use cases of large language models are in content generation, translation and localization, chatbots, personal assistance, etc. According to data published by the World Economic Forum in October 2023, social networking sites such as WhatsApp, Instagram, and Facebook will interact with almost 30 AI chatbots by parent company Meta to revolutionize their social media users' experience. Various end users use these language models for code generation, fraud detection, image annotation, text production, and conversational AI. Synthetic data makes these chatbots accurate and useful for the consumer.

• Use of AI and ML technologies to synthesize complex databases during the pandemic: The advent of the COVID-19 pandemic reflects the characteristics of the patients on a wide scale and recreates the impact of the pandemic over time and across densely tested geographic areas. There is a surge in the number of epidemiologists all across the world. For instance, a report published by the U.S. Bureau of Labor Statistics in May 2023 stated that the number of epidemiologists employed is 10,230. They use synthetic data on a large scale to deduce the repercussions of the pandemic.

Challenges

• Occurrence of 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.

• Associated ethical considerations: The utilization of synthetic data increases the ethical considerations associated with data privacy and assent in the generated data. Various frameworks for governing data usage and protection may put limitations on the usage of synthetic data and hinder scalability and adoption. The potential for bias and privacy concerns are projected to hinder the market growth.