This document discusses a method called "data fingerprinting" to represent data as signatures that capture the underlying structure and semantics. It presents two case studies applying this method to question complexity analysis and image recognition with limited data. The method uses autoencoders trained on clustered data to extract and encode structural patterns, allowing data-hungry machine learning algorithms to be used for "small-data" applications. Evaluation results demonstrate it can accurately classify new data types not seen during training.