In recent years, researchers have demonstrated that digital footprints from mobile phones can be exploited to generate data that are useful for transport planning, disaster response, and other development activities—thanks mainly to the high penetration rate of mobile phones even in low-income regions. Most recently, in the effort to mitigate the spread of COVID-19, these data can be used and explored to track mobility patterns and monitor the results of lockdown measures. However, as rightly noted by other scholars, most of the work has been limited to proofs of concept or academic work: it is hard to point to any real-world use cases. In contrast, this paper uses mobile data to obtain insight on urban mobility patterns, such as number of trips, average trip length, and relation between poverty, mobility, and areas of Freetown, the capital of Sierra Leone. These data were used in preparation of an urban mobility lending operation. Additionally, the paper describes good practices in the following areas: accessing mobile data from telecom operators, frameworks for generating origin and destination matrices, and validation of results.