Signal vs. Noise is a helpful metaphor for work and life in general when dealing with information. But before we get into the metaphor, it would help to define the critical terminology here:
- Signal: meaningful information that you're trying to detect.
- Noise: random, unwanted variation or fluctuation that interferes with the signal
Simply put, noise is what you need to ignore, and the signal is what you need to hear.
Example of Signal v Noise
Suppose you are the CEO of a company; In which case, there will be a lot of information coming your way—everything from the latest negative review of your product to your most cherished employee flirting with the competition.
If you were to ask the person/ department that delivered the news, you'll end up with the impression that every piece of information you get should be deemed necessary, but you know they are not.
Also, if we were to apply the Pareto principle, 80% of the information would not give us the results proportional to the effort needed to act upon it.
Some of the information you receive are signals worth acting upon, while others are random variations that interfere with the signals, a.k.a noise.
Your latest negative review, while relevant, might not necessarily impact your business as much as your top employee getting courted by your competition
Hence, it becomes paramount that you distinguish between what is essential and what is just noise.
Why do we consume noise?
We are wired to consume a lot of noise when we sense that we may discover an extra ounce of signal.
So our instinct is at war with our capacity for making sense.
There is also this interesting theory put forward by Taleb, where he argues that by sampling an information source very frequently, we will likely end up seeing more noise than signal.
"The more frequently you look at data, the more noise you are disproportionally likely to get (rather than the valuable part called the signal); hence the higher the noise to signal ratio." - Mr. Nassim Taleb
Beyond the psychological and cognitive strains produced by what we call information overload, there is a point in intellectual inquiry where adding more information decreases understanding rather than increasing it.
Handling Signal vs. Noise
So how do we handle the problem of Signal vs. Noise? The answer is to only look at substantial changes in data or conditions and not the small ones.
Going back to our earlier example, a bad review from a user is just a data point if you zoom out and look at your product development process from a long-term perspective.
If you were to take action on each negative review, you'd likely need a much bigger development team than your current one. Hence, this bad review is likely nothing but just noise.
On the other hand, your most prized developer leaving for your competition will set your development efforts back by a few weeks at least, so this is a signal you need to act upon.
And if you are someone with a decent level of awareness, signals have a way of reaching you.
Long story short, the signal vs. noise problem is caused by our assumption that if X is good, then 2X must be better, which is not always the case.
And the way to resolve this is to look at significant changes in data or conditions and not at minor variations or anomalies.
Thanks for reading. We hope this post was helpful; we will be back with a new one soon.