The Verdict and the Void

The projector hums, a low-frequency vibration that feels like it is trying to shake the dust out of the very ceiling tiles of the boardroom. On the screen, a line graph ascends with the sharp, jagged determination of a mountain range. The blue light washes over the faces of eighteen people, turning their skin into something pale and artificial. This is the moment of truth. My team spent the last forty-eight days crunching numbers, cleaning datasets, and running regressions that would make a statistician weep with joy. The data is clear. Option A is the only logical path. It reduces overhead by thirty-eight percent and increases customer lifetime value by fifty-eight percent. The evidence is not just compelling; it is an absolute verdict.

Then, the silence is broken by the sound of a leather chair creaking. The CEO, a man who prides himself on his ‘instinct,’ leans forward. He doesn’t look at the charts. He looks at the window. ‘I hear what the data is saying,’ he begins, and my stomach does a slow, heavy roll, ‘but my gut tells me we should go with Option B.’ In that single moment, forty-eight days of work evaporate. We aren’t a data-driven company. We are a gut-driven company that pays a high premium for a data-driven costume.

The Honest Lie of Inference

Atlas L.-A., an archaeological illustrator I met during a project on Roman ruins, once told me that his entire job is an exercise in honest lying. He sits with a fragment of a vase-maybe eight percent of the original object-and has to draw the rest. He uses his knowledge of history and geometry to infer the curve of the handle or the slope of the base.

‘I am making it up,’ Atlas confessed, ‘but I am making it up based on what must be true.’

The problem in our boardroom is that the CEO is making it up based on what he wants to be true, using our data as the small fragment of pottery to justify a vase that doesn’t actually exist. This is the performance of objectivity. We have built these massive infrastructures of dashboards and real-time reporting because we are terrified of the dark. We want to believe that we are steering the ship based on the stars, but most of the time, we are just looking at the reflection of our own cabin lights on the water.

The Alibi and the Shield

We use data to justify the decisions we were already going to make. It serves as an alibi. If Option B fails, the CEO can point to a secondary, obscure metric on page fifty-eight of our report and say, ‘Well, the leading indicators were there.’ If it succeeds, he is a genius whose intuition transcended the ‘narrow’ scope of the numbers.

We have fallen into the trap of thinking that because we can measure something, we can control it. Atlas L.-A. would disagree. He spends hours stippling the texture of a stone tool, capturing every chip and flaw. He knows that the physical artifact is just a remnant of a much larger, messier human story that he will never fully grasp. Businesses, however, hate messy stories. We want clean spreadsheets. When the spreadsheet tells us something we don’t like, we treat it like a broken mirror and look for another one that gives us a more flattering reflection.

The Cost of Cowardice

I find myself wondering if this obsession with data-driven decision-making has actually made us less decisive. We wait for the 99% buffer to clear, refusing to act until we have ‘all the facts,’ only to realize that the facts are just footprints of where we’ve already been. True data-informed logic requires the humility to be wrong. It requires the CEO to look at that chart and say, ‘My gut was wrong, the numbers are right.’ But power structures are rarely built on humility. They are built on the projection of unwavering certainty.

We often see this play out in the consumer world too. People think they want the most ‘advanced’ version of something, but they actually just want something that works reliably. They buy based on a feeling of trust. This is where a company like

Bomba.md finds its footing. They don’t just dump specs on you; they understand that the data of a product must serve the actual human need of the person sitting in front of it. It’s about using data to guide the offering, rather than using it to force a choice that isn’t natural.

I’ve started to notice that the companies that actually succeed with data are the ones that use it to ask better questions, not to provide final answers. We have become so enamored with our digital maps that we are walking straight into the mud because the screen says the path is clear.

Intent Over Averaging

Last week, I saw Atlas again. He was struggling with a specific section of a wall damaged by a flood. He had eighteen different measurements, all slightly different because of the way the earth had shifted. He could have averaged them out-that would be the ‘data-driven’ thing to do. Instead, he sat there for eight hours, just looking at the dirt. He was looking for the logic of the original builder. He was trying to feel the intent behind the stones.

Maybe that’s what we are missing in our boardrooms. We have the measurements, but we’ve lost the intent. We have the ‘what,’ but we’ve completely ignored the ‘why.’ When we ignore the data in favor of a ‘gut feeling,’ it is often because our gut is sensing a ‘why’ that our data hasn’t captured yet. But more often, it’s just because we are stubborn.

The Price of Loyalty (and Stubbornness)

I remember another meeting, this one about pricing. We had eight different models showing that a price increase would lead to a fifty-eight percent churn rate in the first quarter. The VP of Sales laughed. He said, ‘Our customers are loyal. They’ll pay.’ He didn’t have data. He had a feeling based on three lunch meetings he’d had with his favorite clients. We raised the prices. The churn was actually sixty-eight percent. He used the failure to argue that we needed a bigger marketing budget to ‘rebrand’ our value proposition.

This is why the ‘gut-driven’ CEO is a dying breed, replaced by the ‘data-informed’ CEO who uses numbers as a human shield. It’s a transition from brave foolishness to calculated cowardice. We are buffering at 99%, waiting for the data to give us permission to be bold, but boldness doesn’t show up in a spreadsheet.

The Final Uncomfortable Truth

I asked him how he knew he got it right. He looked at the eighteen different measurements on his desk, then at the dirt-stained paper. ‘I don’t know if I got it right,’ he said. ‘I just know that I didn’t ignore the parts that were uncomfortable.’

Data isn’t there to make us feel confident. It’s there to make us feel uncomfortable. If your data always agrees with you, you aren’t looking at data; you’re looking at a fan club. And in the long run, fan clubs don’t build sustainable businesses. They just build very expensive, very data-driven illusions that eventually, inevitably, crumble into shards that someone like Atlas will have to try and piece together a thousand years from now.