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What do football passes and financial transactions have in common? Both are observable events in some real-world walk process that is happening over some network that is, however, not directly observable. In both cases, the basis for record-keeping is that these events move something tangible from one node to another. Here we explore process-driven approaches towards analyzing such data, with the goal of answering domain-specific research questions. First, we consider transaction data from a digital community currency recorded over 16 months. Because these are records of a real-world walk process, we know that the time-aggregated network is a flow network. Flow-based network analysis techniques let us concisely describe where and among whom this community currency was circulating. Second, we use a technique called trajectory extraction to transform football match event data into passing sequence data. This allows us to replicate classic results from sports science about possessions and uncover intriguing dynamics of play in five first-tier domestic leagues in Europe during the 2017-18 club season. Taken together, these two applied examples demonstrate the interpretability of process-driven approaches as opposed to, e.g., temporal network analysis, when the data are records of a real-world walk processes.