Signal / Noise, Forest / Tree - What type of data problem are you solving?

Recently I've been trying to accelerate cross-functional understanding and interpretation of large data problems by framing them in the form of an analogy; "Is this a Signal & Noise Problem or a Forest & Trees Problem?"

Signal & Noise

The term "signal" refers to underlying patterns or bits of information that are truly valuable and are likely to ultimately lead to the solution. "Noise," on the other hand, is more random in appearance and/or irrelevant to the problem at hand. Noise masks the true signals, making it more difficult to get to an effective solution.

In this type of problem, the main challenge is to identify the true signals amidst the (contextually) irrelevant noise. Solving these problems typically involves techniques such as filtering, statistical analysis, or even ML algorithms.

Personally, I strive to arrive at as binary of a definition as possible for what is signal and what is noise. This may mean some "partial-signal" edge cases are left to the noise, but I believe the clarity it brings to the process is well worth the trade-off.

Forest & Tree

The term "forest" represents the overall context of problem space/domain/context /etc. It is the bigger picture that we sometimes lose sight of, or perhaps may have never zoomed out enough to see at all. "Trees" represent the individual components or atomic units within that whole. They are the details we sometimes lose ourselves in.

In this type of problem, the main challenge is to maintain a balanced perspective by considering both the macro (forest) and micro (trees) perspectives. This can be done by zooming in and out, analyzing the system at various levels of granularity, and ensuring that individual elements are understood in the context of the broader system.

Often in these problems, we find ourselves asking, "Is this a weird tree or did the forest move?"

What else?

Do you use analogies to frame your data problems? If so, what are your go-tos?