u/NickatAtaviz

I've spent a lot of my career around data and decision-making, and I've always found this question fascinating. You can have good data, solid analysis, and smart people in the room, and the decision can still go somewhere completely different.

The more I've thought about it, the more I've become interested in the human side of decision-making: trust, incentives, identity, relationships, communication, and what happens when evidence challenges what someone already believes.

That question became the focus of my new book, Decision Intelligence: Why Evidence Fails and How Leaders Win the Room.

And here's the best part: AI isn't mentioned once in the entire book. That's intentional. The technology for producing evidence is changing quickly. The human beings deciding what to do with it haven't changed nearly as much.

The book is available on Amazon.

Would love to hear your thoughts!

I've noticed that we tend to remember leadership decisions based on their outcomes. If something worked, we remember why it was a good idea. If it failed, we remember what we should have seen coming.

But that's not necessarily what we actually thought at the time.

What evidence did we have? What alternatives did we consider? How confident were we? What assumptions were we making? What did we think was most likely to go wrong?

That's why I included a Decision Log in my new book, Decision Intelligence: Why Evidence Fails and How Leaders Win the Room. The idea is to capture your reasoning before you know the outcome, then come back later and compare what you believed with what actually happened.

The goal isn't to beat yourself up over decisions that didn't work. It's to separate a bad decision from a bad outcome, and learn from both.

I turned the template into a free PDF that can be used for personal or professional decisions:

https://www.thenicholasjohnson.com/downloads/decision-intelligence-decision-log.pdf

I'd love to hear from other leaders: Do you have a way of looking back at important decisions you've made? What have you actually learned from doing it?

I've spent a lot of my career around data and decision-making, and I've always found this question fascinating. You can have good data, solid analysis, and smart people in the room, and the decision can still go somewhere completely different.

The more I've thought about it, the more I've become interested in the human side of decision-making: trust, incentives, identity, relationships, communication, and what happens when evidence challenges what someone already believes.

That question became the focus of my new book, Decision Intelligence: Why Evidence Fails and How Leaders Win the Room.

And here's the best part: AI isn't mentioned once in the entire book. That's intentional. The technology for producing evidence is changing quickly. The human beings deciding what to do with it haven't changed nearly as much.

The book is available on Amazon.

Would love to hear your thoughts!

I've spent a lot of my career around data and decision-making, and I've always found this question fascinating. You can have good data, solid analysis, and smart people in the room, and the decision can still go somewhere completely different.

The more I've thought about it, the more I've become interested in the human side of decision-making: trust, incentives, identity, relationships, communication, and what happens when evidence challenges what someone already believes.

That question became the focus of my new book, Decision Intelligence: Why Evidence Fails and How Leaders Win the Room.

And here's the best part: AI isn't mentioned once in the entire book. That's intentional. The technology for producing evidence is changing quickly. The human beings deciding what to do with it haven't changed nearly as much.

The book is available on Amazon.

Would love to hear your thoughts!

I've spent a lot of my career around analytics, and one thing I've come to believe is that being good with the tools just gets you in the door.

You can build a great dashboard, write good SQL, or produce a really solid analysis and still have almost no impact if it doesn't change a decision.

The more interesting question becomes: What is someone actually going to do differently because of this analysis?

That gets into things that aren't usually taught alongside the technical skills: understanding the decision, knowing who you're trying to influence, anticipating what will make them skeptical, and communicating the evidence in a way that actually survives the conversation.

I ended up writing my new book, Decision Intelligence: Why Evidence Fails and How Leaders Win the Room, largely because I kept seeing this gap between being analytically right and being organizationally effective.

For you more experienced analysts, when did you first realize that being technically good wasn't enough to make an impact? What did you actually do to bridge that gap?