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What a Book About Megaprojects Taught Me About Growing as a Data Analyst

  • Writer: Victor Peña
    Victor Peña
  • Jun 17
  • 2 min read

A few months ago, Leon Palafox recommended a book to me: How Big Things Get Done by Bent Flyvbjerg and Dan Gardner. At first, I wasn’t sure it would apply to my work. I’m a data analyst, not someone building airports or Olympic stadiums. But I gave it a shot—and I’m glad I did.


This book changed how I think about projects. Not just big ones, but everyday ones. The kind that start with a Slack message like, “Hey, can you pull this data?” or “We need a quick dashboard.” Before reading it, I used to jump straight into action. Open the tools. Start building. Try to deliver fast.


But that approach didn’t always work. I’ve had projects fall apart halfway through. I’ve delivered things that didn’t solve the real problem. I’ve spent hours fixing things that could’ve been avoided with a little more planning. And that’s exactly what this book helped me see.


One idea hit me hard: the best way to avoid failure is to plan. Not just a little. A lot. The most successful projects in the world—the ones that finish on time and actually work—spend most of their time planning before anything gets built. They “think slow, act fast.”


That made me pause. Because I’ve done the opposite. I’ve rushed to create solutions without fully understanding the problem. I’ve skipped the questions that matter: What’s the goal? Who’s this for? What decision will this support?


Since reading the book, I’ve changed how I work. Now, when a new request comes in, I stop. I ask questions. I write things down. I make sure I understand the “why” before I touch the “how.” It feels slower at first, but it saves time in the long run. And the work is better. More focused. More useful.


This book isn’t about data. But it’s one of the most useful books I’ve read as a data analyst. It reminded me that speed isn’t the goal—value is. And value comes from clarity, not chaos.


Reading this book improved my ability to start with data and end with value.

 
 
 

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