You Don't Have a Data Problem. You Have a Decision Problem.

Because data doesn't make decisions. People do. And most people don't know what decisions they're trying to make.

Companies love collecting data. They build dashboards, hire analysts, and talk about being "data-driven." Then they make terrible decisions anyway.

I watched a retail chain spend millions on analytics. They tracked everything: foot traffic, conversion rates, basket sizes, weather patterns. Their dashboards looked like mission control. They still opened stores in the wrong locations and stocked the wrong products.

Why? Because data doesn't make decisions. People do. And most people don't know what decisions they're trying to make.

Here's what happens. Someone important says "we need better data." Teams scramble to collect it. Databases fill up. Reports multiply. Everyone feels productive. But when decision time comes, the same political dynamics play out. The loudest voice wins. The data becomes decoration.

The problem starts with a basic confusion. Companies think data gives answers. It doesn't. Data gives evidence. The answers come from knowing what questions to ask.

Take pricing decisions. You can analyse competitor prices, customer surveys, and elasticity curves all day. But if you haven't decided whether you're competing on price or value, the data won't help. You'll cherry-pick numbers that support whatever you wanted to do anyway.

I saw this at a software company. They had beautiful customer segmentation data showing that enterprise clients drove 80% of profits. So they decided to focus upmarket. Except nobody told the CEO, who kept pushing for consumer features because his kids used the product. Eighteen months of confusion followed.

Good decisions need three things before data even matters.

First, clarity on what you're optimising for. Growth or profit? Market share or margins? Customer satisfaction or operational efficiency? You can't maximise everything. Pick one.

Second, agreement on who decides. Is it the product manager? The regional director? The CEO? When everyone thinks they're the decider, data becomes ammunition in turf wars.

Third, commitment to act on evidence. This is the hardest part. It means admitting when you're wrong. It means changing course when pet projects fail. It means disappointing powerful people who don't like what the numbers say.

A logistics company got this right. They wanted to optimise delivery routes. Before collecting any data, they agreed: fuel costs mattered most, the operations team would decide, and everyone would accept their conclusions. Only then did they start measuring. The resulting changes saved millions.

Compare that to most "data initiatives." They start with technology. They end with PowerPoints. Nothing changes because nobody agreed what should change or who would change it.

The fix is straightforward. Before your next data project, write down the decision you're trying to make. Be specific. "Should we expand to France?" beats "understand international opportunities." Then identify who makes that decision and what would convince them to act.

If you can't do this, stop collecting data. You're wasting time and money. Fix your decision process first.

Smart companies don't have more data than others. They have clearer intentions. They know what they're deciding, who's deciding, and what evidence would change their minds.

Everything else is expensive theatre.