How professionals think in their work
This is the first part in a four-part series on how a Product Manager thinks about using machine learning in products. The key, you don’t need to learn how to do the hard parts. You don’t need all those frameworks or to follow someone else’s processes.
You need to understand why they do what they do. Once you learn how people think and what they value in their work — a head of product, marketing, growth, sales, business strategy, user experience, investments, and on and on. You learn
- We all think we do different things, but in reality, we mostly do the same. We just call them different names and apply them to a different part. (see ”Writers did “agile” before agile, or the books “A Philosophy of Software Design” for engineers and “Don’t Make Me Think” for user experience designers)
- Once you understand the foundation of how they think, what they value, and the questions they think about, you learn how helpful they are if you apply the parts you’re not doing yet to your work too.
- Use techniques of writers to help yourself understand it so good that you can teach it to others. Avoid all jargon unless the audience expects it. Switch out technical, industry lingo, complex words for simple ones. Cut all the words they agree with, that won’t change what they already know, and then cut more. Don’t give them facts and info; tell it in a way that if they close their eyes, they could picture it like watching a movie. Make it so simple an 8th grader can understand it.
My goal is to write at an 8th grade level but with ideas that are super sophisticated. It doesn’t mean the way I’m writing is dumb. It means the way I’m writing is super simple and straight forward. An eighth grader can easily read one of my books. They may not understand every idea, but writing is not going to defeat them. Writing should be simple enough to that it doesn’t defeat the reader. — Malcolm Gladwell
- In the end, it comes down to doing what leaders do. Not having or giving the answers. But instead to knowing the right questions to ask at the right time.
Part 2 in series: How a Data Scientist thinks