Uncertainty is ubiquitous. Depending on what information we have, we get different types of uncertainty. For each type of uncertainty, techniques have been developed for efficient representation and processing of this uncertainty. However, the plethora of different uncertainty techniques is often confusing for practitioners. The situation is especially difficult in frequent situations when we need to gauge the uncertainty of the result of complex multi-stage data processing, and different data inputs are known with different types of uncertainty. To avoid this problem, it is necessary to develop and implement a general approach to representing and processing different types of uncertainty. In this paper, we argue that the most appropriate foundation for this general approach is interval uncertainty.