Data scarcity refers to the lack of sufficient or high-quality data necessary to train machine learning models effectively or to improve the accuracy of predictive analytics. When there is inadequate data, AI and machine learning systems struggle to recognize patterns, leading to suboptimal model performance and reduced reliability in predictions.
This issue can arise in domains where data is difficult to collect, expensive to obtain, or not readily available. Data scarcity can hinder the development and deployment of AI solutions, particularly in specialized fields or emerging industries. To address this, organizations may resort to techniques like data augmentation, synthetic data generation, or transfer learning to compensate for limited data.