Synthetic data, generated through advanced generative artificial intelligence (GenAI), is emerging as a crucial solution to address the impending shortfall of data needed to train advanced AI algorithms. By providing a scalable and efficient alternative, synthetic data is not only ensuring robust AI development but also driving innovation and operational efficiency across industries, all while maintaining compliance with stringent privacy regulations, according to GlobalData, a leading data and analytics company.
GlobalData’s latest report, “Synthetic Data is the Often-Overlooked Application of Generative AI About to Take the World by Storm,” reveals that GenAI streamlines the data generation process and can lead to higher quality outcomes. Additionally, it can scale to create large volumes of data records efficiently and for a range of applications.
Rena Bhattacharyya, Chief Analyst and Practice Lead for Enterprise Technology and Services at GlobalData, comments: “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.”
GlobalData’s Innovation Explorer database reveals that the use cases for synthetic data are expansive and cover a broad range of industries. Synthetic data can be used in just about any scenario where large volumes of data are required.
Bhattacharyya continues: “Synthetic data is often used to test software in pre-production environments. However, the applications for synthetic data extend far beyond software testing. Synthetic data can 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 (to name just a few potential applications.”
In healthcare, it is being leveraged to address privacy concerns and accelerate research. Manufacturers are using GenAI to train models for optical inspections, enhancing quality control. The automotive sector employs synthetic images for advanced in-cabin monitoring, while insurance firms utilize it for more accurate claims processing. Financial institutions are adopting synthetic data to prevent fraud, and the technology sector is testing it to improve machine learning models, showcasing its broad applicability and transformative potential.
Additionally, synthetic data can help companies adhere to regulations regarding data privacy and sovereignty.
Bhattacharyya concludes: “By using synthetic data, organizations do not need to collect and store sensitive information governed by privacy regulations. This is critical to financial or healthcare organizations that collect and hope to leverage customer and patient information.”
