This article outlines how to effectively measure and communicate the value of user experience (UX) within an organization. It emphasizes the importance of both qualitative and quantitative data, suggests various metrics to track, and offers strategies for reporting success to stakeholders.

The mostlyai-qa library provides tools for assessing the fidelity and novelty of synthetic samples compared to original datasets, allowing users to compute various accuracy and similarity metrics while generating easy-to-share HTML reports. With just a few lines of Python code, users can visualize statistics and perform detailed analyses on both single-table and sequential data. Installation is straightforward via pip, making it accessible for developers and researchers working with synthetic tabular data.