GlobalData’s latest report, “Synthetic Data is the Often-Overlooked Application of Generative AI About to Take the World by Storm,” reveals that GenAI-driven synthetic data can address data shortfall when training new AI algorithms.
“Although the world is generating and collecting increasing amounts of information, academics and investors such as venture capitalists warn that within years there will not be enough data to meet the growing demand for data to train new machine learning algorithms,” Rena Bhattacharyya, chief analyst and practice lead for Enterprise Technology and Services at GlobalData, said.
Synthetic data
Bhattacharyya said that synthetic data has a lot of use cases beyond testing software in pre-production environments.
I can also be used to evaluate risk, prevent fraud, gauge the impact of business strategies, aid in drug discovery, validate financial models, provide predictive maintenance and quality control, and forecast demand.
Industry use cases
Among other use cases of synthetic data is addressing privacy concerns and accelerating research in healthcare, and enhancing quality control in manufacturing
The automotive industry also uses synthetic images for advanced in-cabin monitoring, while the fiancé sector adopt syntetic data forfraud prevention.
Insurance firms utilise it for more accurate claims processing, and the technology sector is testing it to improve machine learning models, showcasing its broad applicability and transformative potential.
Synthetic data can also help companies with compliance with data privacy and sovereignty regulations.
“By using synthetic data, organisations do not need to collect and store sensitive information governed by privacy regulations. This is critical to financial or healthcare organisations that collect and hope to leverage customer and patient information,” Bhattacharyya said.
