You probably know this, but maybe you don't know you know.

The takeaway first:

One of the foundations of what explanation means for us is that it doesn't depend on when something happens: if every causal factor is the same, then the same thing will happen regardless of time.

The other way around:

If time is one of your explanatory variables, then you don't really know why something is happening, and you'll have a harder time predicting and influencing a process.

Or to coin a rule of thumb:

If your plot has time on the x axis, you need more plots.

Knowing YoY revenue is stalling is necessary but useless on its own; you need to understand which of the different parameters that determined last year's revenue growth has changed, and why, and so backwards the causal chain until you can find something you can influence. Cutting-edge strategy isn't just the practice of iterative causal modeling under terrible conditions of complexity, lack of information, and a limited experimental budget, but a lot of it is.

I sketched a couple of detailed toy examples, plus an anonymized case study from my work with a previous client, but on second thought toy examples are often more useful to induce the feeling of understanding than to transfer knowledge. Working out the idea as it applies to your own practice — When was the last time you were aware of a strategic decision based on a time plot? What were the time-independent assumptions in the decision? What time-free analysis (and ultimately plot) would have helped validate the hypothesis? — even for ten minutes, is going to be more useful to you than any number of plots I could show you.