Simone Somazzi developed a built-in function for the apache/systemds repository that detects and classifies missing data types in datasets as MCAR, MAR, or MNAR. Leveraging skills in data analysis, data manipulation, and statistical modeling, Simone’s approach focused on enhancing data quality assessment by enabling targeted data cleaning strategies based on the nature of missingness. The implementation, written in DML, provided review-ready code with a clear commit history, directly addressing SYSTEMDS-3151 and closing a tracked issue. This work improved analytics readiness for users of apache/systemds by making it easier to identify and address missing data patterns in large datasets.