In recent years, the availability of GPS-equipped mobile devices and other inexpensive location-tracking technologies have enabled the ubiquitous capturing of the location of moving objects. As a result, trajectory data are abundantly available and there is an increasing trend in analyzing them in the context of mobility data science. However, the abundant availability of trajectory data makes them compelling for other tasks too. In this paper, we propose the use of these data to tackle the data scarcity problem in data analysis by appropriately transforming them to extract relevant knowledge. The challenge lies not just in leveraging these abundant trajectory data, but in accurately deriving information from them that closely approximates the target variable of interest. Such knowledge can be used to generate or supplement the scarcely available datasets in a data analytics problem, thereby enhancing model learning. We showcase the feasibility of our approach in the domain of fishing where there is an abundance of trajectory data but a scarcity of detailed catch information. By using environmental data as explanatory variables, we build and compare models to predict fishing productivity using the actual catches from fishing reports and/or the inferred knowledge from the vessel’s trajectories. The results show that, mainly due to trajectory data being larger in volume than fishing data, models trained with the former obtain a precision 7.9% higher, despite the simplicity of the applied transformations.