For better or worse, a couple months in, this Substack is mostly a newsletter of weekly analogies.1 SQL style guides are the First Epistle to Timothy. Business users are science journalists and Natalie Portman. Analytics templates are golf clubs. The modern data stack is the beer industry. Analysts are architects, and writers, and confused college students (and, given all the mixed metaphors, confused writers). People who close airplane windows are sea monsters.2
Some people may say this is too much—”say something real; be less oblique; not everything is like a Billie Eilish song.” To which I say, you can’t have too much of a good thing (and is everything not like a Billie Eliish song?). Moreover, though I may be a worse offender than most, it’s always struck me how much other data analysts do the same. If our job is to help people figure stuff, why are we always communicating so indirectly?
Though analogies may seem like misdirection, I believe they’re actually clarifying—even when they don’t seem to make sense. That’s because, unlike SAT test questions that directly compare X to Y, real analogies have a prerequisite step: Turn complex X into simple X. This process, more than the comparison itself, is what makes analogies so useful.
To create an analogy, you have to pull out the essential bones of an idea, and map them onto the bones of another idea. This requires you to make clear what you think the structural skeleton of your point is, and what you think is extraneous. In doing so, you have to create a simple model for your idea. And when we use simple models, we can’t hide behind complexity or unnecessary details, either intentionally or inadvertently, to obscure our argument's logical core.
In this way, even mismatched analogies are instructive. We just have to pay more attention to the translation from complex X to simple X than the comparison of X to Y.3
It’s the same principle that explains why it’s better to explain things in simple words than business jargon, even thought the latter might feel more precise.4 Simple words, like simple models, leave little room for misinterpretation. Phrases like "solutioning" and "value-add" are an argumentative sleight of hand: They create artificial complexity, and let us appear to make a point without ever having to actually say what that point is. They aren't meant to communicate but to distort, like a series of fun house mirrors that warp and deform whatever they're reflecting—which, as often as not, is nothing at all. When we try to explain something without translating a complex X into a simple one, we can use sophistication as a shield in the same way.
There's another analogy for analogies, which may explain why I’m drawn to them: Analogies share a lot of similarities with data analysis.
In many analytical questions, the hardest and most important thing to figure out is what actually matters. What are the critical elements to this question, and what are red herrings? What's the signal, and what's the noise? It’s not possible to consider every nuance that explains why customer retention rates are rising, or why baseball’s hitters seem to be getting worse, why the economic recovery has been so unsteady. The only way to solve these riddles is to reduce them to smaller questions that are representative of larger problems. To think this way—to create limited models to uncover larger truths—is to think like both an analyst and an analogist.5
There are several other parallels with the analytical work. First, data analysts spend a lot of time searching for patterns. Finding the dozens of trends in a dataset is easy. The real work is figuring out which ones to pair up and how to make sense of them—building, in other words, “quantitative analogies.”
Second, thinking in analogies makes us better storytellers. Data is often boring and confusing. Analogies help us build more memorable stories by adding color to the mundane.
Analogies also have one final benefit that’s even more significant: They force us to seek common ground with our audience. To translate something unfamiliar into familiar terms, we first have to figure out what’s familiar to those we’re talking to, and explain our ideas on their terms—which is a point I’ve made before, using, ironically, a baseball analogy that only makes sense to baseball fans.
So maybe, I suppose, you can have too much a good thing. As for me, well—I ain’t nothing but a lost cause.
One bit of evidence I’ve taken analogies too far: Several people assumed this entire piece was itself an analogy for something bigger. Sadly, no. Also, to those of you who do close windows—I’m actually writing this from an airplane, window wide open beside me, screen easy to see, and the spectacular expanse of the Appalachian foothills a glance away.
Note that this isn't the same thing as using analogies for decision making. In those cases, the tightness of the analogy—i.e., how closely X resembles Y—matters much more than it does for explaining concepts or telling stories.
If you take away any learnings from this post, it’s that you should operationalize all the action items in this article immediately.
Analogist (n): An analogy enthusiast.