When Having All the Answers Means You Might be Asking the Wrong Questions

January 21, 2026

Hi Reader

( 4-minute read at most...and then you can get back to Instagram!)

I was sat in a meeting recently. At the head of the room a sporting director was presenting recruitment analysis. Twenty or so slides worth of data. Heat maps, passing networks, progressive carries per 90 minutes etc.

It looked scientific, sounded impressive and kept my attention.

Then someone asked the obvious question: "So who do we sign?"

Silence and then…. "Well, the data suggests several candidates..."

Right. Several. Which woke me up from my spell to an understanding that it was another way of saying the data didn't actually answer anything.

You see the same pattern at every level. Organisations and clubs building these massive data operations. Brining in analysts. Investing in ‘solutions’ that give you a dashboard for everything. (This is even creeping into grassroots sport with worrying knock on for player & coach retention)

But then they make decisions basically the same way they always did; but with more PowerPoint slides to justify them afterward.

I’m not anti-data, to be clear. I actually think that data's really useful. Sometimes essential.

But something's happened where it's become this thing you're supposed to worship. Like if you're not "data-driven" you're basically reading the tea leaves and hoping for the best.

Except I'm watching supposedly data-driven organisations make appalling decisions while clutching reports like the Dead Sea scrolls.

The problem isn't the data. It's what we're doing with it. Or more accurately, what we're pretending to do with it while actually doing something else entirely.

Ian Graham, the guy who helped Liverpool win everything with data, says something that should make every executive uncomfortable: people look at insights, say "that's interesting," then carry on exactly as before.

The data didn't change anything. It just gave them something to reference in the meeting.

Or here's the worse version: they cherry-pick metrics that support what they already wanted to do. Ignore everything that questions it. Then claim the decision was data-driven.

That's decorating your biases instead of using data.

Marketing and behavioural science guy Rory Sutherland has this line: "More data leads to better decisions. Except when it doesn't."

That second bit's what nobody wants to examine.

Because sometimes we deploy rigorous analysis not because it makes them easier to defend…it helps you win an argument rather than arriving at the best solution.

Think about it. Make a call based on judgment and it fails? You're exposed. Your competence gets questioned.

Make a call "based on the data" and it fails? Well, the model had limitations. Variables changed. Nobody could have predicted market conditions.

You see how that works? Data gives you somewhere to hide.

Sutherland again: what often matters most isn't a successful outcome, but the ability to defend your decision regardless of outcome.

That's why data teams exploded. Nothing to do with better decisions but decisions made more defensible.

You see it in outside of sport too. Take hotels for example. They’ll give you chapter and verse and dozens of metrics on occupancy rates by room type, season, booking channel etc

But crickets when you ask why repeat guests stop coming back.

All the data and fu£k all insight.

Measuring everything doesn't mean understanding anything.

"The greatest obstacle to discovery is not ignorance, it is the illusion of knowledge."

You think you know because you can see numbers. However knowing numbers, knowing what they mean and pattern recognition is a different bag altogether.

Here's what I've noticed about leaders who actually use data well. They, generally, ask one question first:

"What decision would this change?"

If the answer's none and/or nobody's actions shifted based on them, they don't collect it or stop.

It’s different level thinking i.e. a shift from "what can we measure?" to "what do we need to decide and what would actually change how we decide?"

Many clubs, sports organisations and businesses have this problem: they've bought/built quite sophisticated data infrastructure but the people making decisions don't know how to use it.

Like handing learner drivers Ferraris they can't manoeuvre.

Everything looks impressive. The dashboards are beautiful. The models are sophisticated.

But when decision time comes, leaders revert to what feels safe. They just make calls the way they always have and then reference the data afterward to justify it.

The Ferrari sits unused while everyone drives the Corolla.

After your next data presentation this might be worth trying.

Ask yourself: "What am I going to do differently because of this?"

If you can't answer specifically, it's noise.

And then the follow-up: "If the data showed the opposite, would I change my mind?"

If not? You're using confirmation bias with graphs not leveraging data.

What's happening is we're replacing judgment with the appearance of rigour, and that's dangerous. Good decisions require data and judgment. The data shows patterns. Judgment interprets what those patterns mean in context that can't be measured.

But now we've created cultures where saying "based on my judgment" sounds weak and "based on the data" sounds strong.

So leaders pretend their judgment is data. Or, worse, they abandon judgment entirely and let models decide things models can't actually decide.

Again, I am NOT suggesting we ignore data.

I am suggesting, strongly, that we stop worshipping it.

Data's useful when it answers questions worth asking. When it changes decisions that need changing. When it reveals patterns you couldn't see otherwise.

But data that doesn't shift action is just expensive decoration, isn’t it?

And leaders who can't make calls without perfect information aren't confusing rigour and paralysis.

Tools work best when you know what you're building. Most leaders are holding hammers looking for nails when what they need is judgment about whether anything should be built at all.

The natural follow then isn’t asking "what does the data say?" but asking "what decision am I making and what would genuinely change my mind?"

It's a better question and a way harder question.

P.S. If you're drowning in data but starved for insight, or suspect your organization performs data usage rather than actually using it—worth examining with someone outside your situation. Sometimes what you need isn't more analysis. It's clarity about what you're actually trying to decide and what would change how you decide it.