The Best Decision Was Removing The Data

I accidentally made my best friend move to Columbus, OH.

Not directly.

She had narrowed her list to Philadelphia, Denver, and Portland. She had good reasons for all three, but I wasn’t convinced she was evaluating the places themselves. I thought she was weighing the idea of these places too heavily.

I don’t mind being wrong. I mind making big decisions for reasons that weren’t fully examined.

So I built a spreadsheet.

Because of course.

I researched ~50 cities that seemed to meet her criteria (with a little help from my robot): cost of living, job market, weather, politics, airport access, healthcare, recreation, population. You get the idea.

Then I applied formulas, scores, weights, and standard decision-making strategies.

And then I deleted the most important information on the spreadsheet.

The city names.

Philadelphia, Denver, and Portland all carry their own lore. They sound like places someone would want to move. But I wanted to know what happened if we stripped away the branding and looked only at the criteria that mattered.

Every location became a row of data.

No names. No states. No reputations.

Just the kind of life each place could realistically offer.

She scored each one without knowing what she was looking at.

And once the labels disappeared, so did the frontrunners.

Columbus won.

Not Philadelphia. Not Denver. Not Portland.

Columbus.

When I revealed the names afterward, she laughed.

Then she moved there.

To be clear, I had no idea Columbus would win. That was kind of the point. The spreadsheet didn’t choose Columbus. It showed her which city best fit the life she wanted to build.

Columbus isn’t objectively better. No city is. But clear criteria can lead us to a better answer once we tune out the static.

It reminded me how often we make important decisions with information already filtered through assumptions, branding, familiarity, and emotion.

Most people use spreadsheets to justify what they already wanted.

Sometimes we need to challenge what we think we want.

The best decision isn’t always about collecting more data. Sometimes it’s about removing the data that shouldn’t be influencing us in the first place.

I wasn’t anti-Denver or pro-Columbus. I was against making a major life decision without reducing one of the biggest risks: regret.

I’ve started applying the same thinking to my own relocation planning. It has been fascinating to see which assumptions survive once the labels disappear.

I’ve also realized I’ve been using similar approaches for over a decade, from major personal decisions to software vendor selection. The quality of a decision depends as much on the process as the options. Good decisions don’t happen by accident; they happen by design.

Sometimes we need more data.

Sometimes we need less noise.

Sometimes changing the process changes the answer.

Next up, we’ll be applying this framework to dating.