Multi-touch journeys, cross-device behavior, last-click attribution defaults, and privacy restrictions all make attribution messy. Much messier than most dashboards suggest. The challenge is that stakeholders usually want a clean answer. But the data rarely behaves that way. When reports don’t match expectations, credibility can wear off, and it is not because the analysis is wrong, but because uncertainty isn’t communicated. In practice, the solution is fairly simple: Be explicit about what the data shows, what it estimates, and what it simply can’t tell us. That kind of transparency doesn’t weaken your reporting. If anything, it tends to build trust over time. Uncertainty in analytics usually comes from the way the tools themselves operate. Once you understand where the limitations are, it becomes much easier to talk about them without sounding defensive. Most of the time, uncertainty shows up in four predictable places, and none of them are really anyone’s fault. Take Google Analytics 4, for example.…