Decarbonizing the world's fleet is one of the most important challenges in the near term future. However, the 'how' is a big question mark to most. This paper focusses on solving that problem through data. It centers on how data is generated and collected on board and the analysis in transforming those raw inputs into coherent operational profile reporting with the aid of machine learning. In turn, this allows us to create an accurate model for the required (future) energy needs of the operation. This yields a fact-based decisionmaking tool for shipowners to select the right decarbonization strategy (electric? hybrid? methanol?) and correct vessel type, as well as furthering operational excellence by means of (real-time) data insights.