At Trax, we digitize retail data from images. We process it using our image recognition engines and output it in tabular form. While this data is very rich and unique, it also requires quite a bit of preprocessing to make it useful for data science and advanced analytics. In this session I will present how complicated this preprocessing stage really is and how much algorithmic work is being put into managing and validating the data even before using it. I will demonstrate how we leverage anomaly detection models, and data cleaning techniques to create clean, workable retail data. Lastly, I will show how this data is joined with Point of Sales data to drive real business value for our customers. Roy Resh, Senior Data Scientist with 5+ years of experience @ Trax. Currently focusing on developing prescriptive models that deliver insights for CPG customers. Prior to that worked as a Team leader and Algorithm developer at Trax in the computer vision group, implementing anomaly detection and data enrichment engines. Has a B.Sc in Physics and Chemistry and an M.Sc in Physics specializing in quantum information from the Hebrew University of Jerusalem. Father to 3 kids, likes doing crossfit, hiking and music.