Big Ideas, Enterprise IT, Real Talk

Unknown Unknowns

Most people prepare for the known knowns— that is, the problems  we already know about and the known unknownssuch as what projects your customers will dream up next, or what your competitors might bid against you. The most haunting problems are the unknown unknowns—the things that blindside us, the problems that are coming our way that we don’t even know about yet.

How do you prepare for the unknown unknowns? Let’s take an example everyone knows about: data scientists blew the predictions about last fall’s election, while one astute observer (former president Clinton) got it right. What did he know that that all the data scientists didn’t know? What combination of knowledge and experience led him to sound the alarm while others thought the election was in the bag? And most importantly, what could we learn from it in our own businesses and lives?

As a writer who helps people explain complex products and services, I think it’s important to remember that no matter how good it is, any analysis is just a simplified way to think about a thingnot the thing itself. Sometimes people are so in love with their own explanation that they forget that!

Former president Clinton, an old-school politico, didn’t use an analysis to predict events. If I had to guess, he reached his conclusions via full immersion in his ecosystem, taking in information from multiple sources on a constant basis. He had a baseline, sure, but he kept tuned in as costly and irreversible events unfolded. To this day, I’m always amazed how many IT projects go off the rails because the people in charge don’t want to spend the time to involve their employees and customers and tap into the storehouse of knowledge right at their fingertips.

Usually, it turns out that so-called unknowns were known to plenty of folksjust not the ones who “mattered” when the decisions were being made. Think “connect the dots.” If you don’t map all the dots, you probably won’t get the right picture, no matter how clever you are or what you decide you see in the dots that you do have.

Today’s thought:

Photo Mar 17, 9 11 10 AM

Today’s links:

How We’ve Destroyed User Stories
Why So Many “Fake” Data Scientists? 

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