Measuring embedding drift in unstructured data is a complex task due to its inherent characteristics, making traditional measures from structured data unsuitable. Approaches are needed to comprehend the alterations in relationships within unstructured data itself. Detecting unstructured drift aims to determine if two datasets are dissimilar and provide methods to understand the reasons behind their differences. Teams often encounter various image data issues, including quality problems and unexpected objects not part of the original training set. Text drift poses significant challenges for natural language processing models due to changes in terminology, context, or meaning over time, low-resource languages, and cultural speech gaps. These issues can lead to reduced model performance when encountering new, unseen data.