The data machines do read

Julián had three stores. One in Cartagena, one in Lorca, one in Águilas. Industrial hardware: screws, nuts, power tools, supplies for workshops and construction sites. Thirty-two employees in total, twenty-six years in business, one Excel spreadsheet per store, and an intuition that had served him reasonably well until now.

Julián knew that Cartagena sold more. He knew it the way you know these things: because he had seen it, because the numbers more or less added up, because the Cartagena manager told him every month that things were going well and the Lorca manager complained more. For ten years, Julián had reinforced Cartagena's staff on Wednesdays and Saturdays, which were the busiest days.

The Lorca manager, Tomás, also asked for reinforcements. Julián told him they had to make do with the current team. Not out of bad faith. Simply because if Cartagena sold more, Cartagena had priority.

Tomás didn't argue. He nodded, went to the counter, and served as best he could the eleven o'clock queue that, according to him, was a little longer every year.

The numbers that were already there

One day someone proposed to Julián that they cross-reference the data. Not an audit, not a digital transformation project. Simply taking the sales from all three stores—which were in the spreadsheets, messy but complete—and looking at them differently.

Julián agreed reluctantly. He already knew what each store sold. But he agreed because the person proposing it didn't promise anything spectacular, and that struck him as honest.

The data said things Julián didn't expect.

Cartagena sold more overall. That was true. But it didn't sell more every day. It sold more on Saturday mornings. Much more. On Saturday morning, Cartagena billed nearly double what it did on weekdays. On Wednesdays, which Julián had reinforced, it sold practically the same as on a Tuesday.

Why on Saturdays? Because on Saturdays there was a street market in the nearby square. Private customers came. The ones fixing a bathroom tap, assembling a shelf, needing a drill and picking up screws along the way. People who weren't Julián's regular customers but who, being in the area, walked in.

And Lorca. Lorca sold more on weekdays than Cartagena sold on weekdays. Especially on Tuesdays and Wednesdays. The reason: the Lorca industrial estate was more active mid-week, when workshops placed orders for the rest of the week.

Tomás was right. The eleven o'clock queue existed. And it was growing.

Ten years of decisions with an incomplete map

Julián stared at the chart for a while. It wasn't a complicated chart. Two colored lines, one per store, days of the week on the horizontal axis. Anyone could read it. Anyone would have drawn the same conclusions.

But nobody had done it.

Julián had been putting more staff in Cartagena on Wednesdays for ten years. Wednesdays, which were a normal day. And on Saturdays, when the market brought people in and the store was overwhelmed, the team was at the same level as on a Tuesday. Meanwhile, Lorca was short-staffed on exactly the days it sold the most.

Not because Julián was a bad manager. Because the data he needed was spread across three different Excel files, with different formats, and nobody had put them together. And without putting them together, reality was invisible.

Julián's intuition wasn't wrong. It was incomplete. It told him what he saw. It didn't tell him what the data saw.

A dashboard that speaks clearly

A dashboard is not a pretty spreadsheet. It is a translator. It takes the data your company already generates—sales, schedules, inventory, staff—and converts it into something you can look at and understand in thirty seconds.

Julián now has one. It's not sophisticated. It has no artificial intelligence or algorithmic predictions. It has sales by store, by day, by time slot. It has real-time stock. It has an indicator that turns red when a store is selling above its staffing capacity. Everything fits on one screen.

He looks at it in the mornings, with his coffee. It takes two minutes. Before, he spent half an hour reviewing each manager's spreadsheets and still lacked information.

The first month, he redistributed staff. More people in Cartagena on Saturdays. More people in Lorca on Tuesdays and Wednesdays. The result wasn't spectacular. He didn't double sales or triple profits. What happened was quieter and more real: Lorca's customers stopped waiting in line at eleven. Cartagena's customers found someone to serve them on Saturdays without rushing. Tomás stopped complaining. Which, in a company of thirty-two people, is a more reliable indicator than any chart.

What intuition doesn't see

Julián still trusts his intuition. He has been at this for twenty-six years and isn't going to change now. But he has learned something that isn't easy to admit for someone who has spent half his life making decisions from the gut: intuition is good for the questions you know exist. For the ones you don't know exist, you need data.

Julián didn't know that the Cartagena street market affected him. It hadn't occurred to him that that correlation existed. A spreadsheet wouldn't have shown it to him, because a spreadsheet doesn't cross-reference data on its own. Someone had to look at it differently.

Now he looks at it every morning. With his coffee. Two minutes. And every so often something appears that he didn't expect. Which is, in the end, what looking is for.