Why are data science projects in an industrial context still rare? Simon made the experience that, especially in complex industry facilities, understanding the data and identifying a concrete use case requires a lot of domain expertise. Building up this domain expertise might be the hardest part for a data scientist. But without clear data understanding there won’t be a precise problem definition and sooner or later the project will fail. In this talk, Simon will explain the key success factors of a concrete data science project with TAL-Group, which is the operator of the transalpine oil pipeline transporting about 45 millions of tons of crude oil every year across the alps. Based on the experiences made in that project he will focus on two questions: 1. How did they overcome this “hardest part” from raw sensor data and to a concrete data science problem definition? 2. How did machine learning algorithms help in order to identify root causes for efficiency losses while pumping raw oil?