AmirhosseinHonardoust/Measuring-The-Soul-of-Data
A narrative and technical exploration of data authenticity through the four pillars of synthetic data realism, Fidelity, Coverage, Privacy, and Utility. This thought-leadership piece combines storytelling, mathematics, and code to explain how these metrics define the ethical and functional “soul” of data in AI systems.
This project helps data professionals understand and measure the quality of synthetic data. It takes your generated synthetic datasets and compares them against real data, providing clear metrics for how realistic, complete, private, and functionally useful your synthetic data truly is. Data scientists, machine learning engineers, and data privacy officers would use this to ensure their synthetic data is fit for purpose.
Use this if you are generating synthetic data and need to rigorously evaluate its quality across key dimensions like realism, completeness, privacy protection, and usability for AI models.
Not ideal if you are looking for a tool to generate synthetic data itself, as this project focuses solely on evaluating existing synthetic datasets.
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Last pushed
Nov 14, 2025
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