Jun 22, 2022 · Gartner estimates that by 2030, synthetic data will completely overshadow real data in AI models. Gartner analysts will discuss use cases and the prospect of synthetic data at upcoming Gartner Data & Analytics Summits, taking …

May 7, 2025 · By 2026, Gartner predicts 75% of data used in AI projects will be synthetically generated, and by 2030, synthetic data will completely overshadow real data in AI model training.

By some estimations, SD “will completely overshadow real data in artificial intelligence (AI) models” by 2030 (Linden, 2022). SD promises to be a viable alternative when data collection and data sharing may not be feasible or cost-effective.

Dec 19, 2024 · Gartner anticipates the use of synthetic data will overshadow the use of real data in AI models by 2030. This trend makes it increasingly likely you will encounter synthetic data, if you haven’t already.

Jun 12, 2022 · According to a widely referenced Gartner study, 60% of all data used in the development of AI will be synthetic rather than real by 2024.

Mar 14, 2024 · In fact, by 2030 synthetic data will completely overshadow real data in AI models (Source: Gartner). Undoubtedly, synthetic data hold immense possibilities and benefits. Let’s dive into the synthetic data benefits in the section below. Among the top benefits of Synthetic Data, data privacy and data security claim the top berth.

Apr 30, 2025 · Synthetic data offers solutions to data scarcity and privacy issues but must be used judiciously to avoid compromising AI model integrity. Model collapse is a critical risk associated with over-reliance on synthetic data, leading to degraded AI performance.

Apr 22, 2025 · Gartner estimates that by 2030, synthetic data will completely overshadow real data in AI models. This could be considered a threat to “traditional” suppliers of real data.

Aug 20, 2024 · Synthetic data, artificially generated information designed to mimic real-world scenarios, is rapidly gaining traction in AI development. It promises to overcome data bottlenecks, address privacy concerns, and reduce costs. However, as the field evolves, questions about its limitations and real-world impact are coming to the fore.

Synthetic AI data generation can help reduce AI bias just by generating artificial data or modifying the original data before it enters the AI / ML algorithms. This ensures a more inclusive and fair AI application.