How the sausage is made (and other fun stories)

How did you learn how to learn?

Thinking a lot about data storytelling, hiring, and early-stage infrastructure? We are too. Grab some time here and let’s chat.

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The role of just about any knowledge worker is to make sense of countless inputs in order to produce an output that is greater than the sum of its parts. Whew, say that ten times fast.

Inputs = data sources, customer feedback, team feedback, experiment results, hypotheses, industry trends, macro trends, etc.

Output = a succinct story that illustrates a clear move-forward plan

We’ve talked a lot about the output (stories!) and have, recently, spent some time talking about all the inputs (data!). Let’s talk about that whole “making sense” part.

Once upon a time, we sat in a classroom and were presented with a (very) structured way to learn: curated reading, discussion, assignments, problem sets, etc.. This curriculum was designed for you by a professor and oftentimes leaves you in debt.

At work, you are expected to design your own curriculum. This is a skill that, in my opinion, is the ulimate superpower. How quickly can you navigate inputs, analyze their importance or significance, and begin to connect the dots? The people best at this consciously consider the role of their own biases. They recreate the classroom discussion by surrounding themselves with a team that asks questions and pokes holes. They test their knowledge by practicing and teasing drafts of their story to a small group.

The person who says he knows what he thinks but cannot express it usually does not know what he thinks.

- Mortimer Adler

Want to become a better storyteller? It’s not just about praciting in front of a mirror. It’s about challenging your learning habits and process.

Happy weekend,

Raman at Rhetoric

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📚 What’s made me a better storyteller this week

While we’re talking about learning: “Scientists have shown for the first time that briefly tuning into a person’s individual brainwave cycle before they perform a learning task dramatically boosts the speed at which cognitive skills improve.” There are fascinating real-life applications (like how we talk to children), too.

Why has Apple maintained a functional org structure for over two decades? Turns out, it’s good for innovation: “Long before it can get market feedback and solid market forecasts, the company must make bets about which technologies and designs are likely to succeed in smartphones, computers, and so on. Relying on technical experts rather than general managers increases the odds that those bets will pay off.”

This one was a fun ride: if you’ve asked ChatGPT a very simple question that it’s gotten wrong, it might be because AI is modeled off of the human brain, which is incredibly flawed. If you read this story in reverse, you can use AI to learn about the human brain, too.