There are two main pieces to our perception of the world - the bare facts and the larger picture you get from combining the facts. The larger picture is more universal than the bare facts, so it is more useful than a use case.

Plus, and this is often ignored today, big pictures are not entirely mutually exclusive. To use a comp sci analogy let's look at search algorithms:

Use cases are more similar to deterministic partial solution searches (backtracking depth first search, breadth first search) for which the more interesting problems are intractable. Additionally, such search algorithms are very self contained, and it is usually quite difficult to combine them, making these algorithms a one time tool.

On the other hand, big pictures are more similar to stochastic solution spaces searches, which won't get you the best solution, but they'll get you a pretty good one much, much more quickly. And since solution space searches all interface with the same space, they can be mixed and matched depending on what is most useful for the problem domain. For example, with genetic algorithms, once you can make your problem domain fit a standard GA representation, the whole of GA theory can be applied.