Cycling teams are often multimillion companies that should be run as efficiently as possible, given the high monetary stakes. In practice, many decisions remain based on gut feeling, despite the growing list of answers and aids business analytics can offer. However, current developments in the field have a very narrowed scope due to the absence of an industry standard of data collection and representation. Accordingly, I propose the Universal Cycling Language (UniCycL), which should act as an industry standard for future, broader developments in the field. The UniCycL framework retrieves online unstructured textual updates on races and structures these updates using natural language processing techniques into one comprehensive database on in-race actions. Using this unified framework, future developments are suggested compromising the three main pilars of talent management: talent identification, talent development, and talent selection.