When the KC-135R transitioned from steam gauges to a glass cockpit, something unexpected happened on both ends of the experience spectrum.
The old timers, pilots who'd spent careers trusting six analog instruments, looked at the new displays and blocked most of it out. They flew the airplane the old way: pitch, power, trim, crosscheck. But they were leaving situational awareness on the table.
The new guys did the opposite. They loved the data. Every readout, every trend line, every system parameter. They wanted it all. And their ability to actually fly the airplane suffered for it.
The lesson wasn't that data is good or bad. Neither extreme produces a great pilot. What you need is someone with enough situational awareness to understand what's happening inside the aircraft, and the experience to know which variables matter right now and what to do about them.
That's exactly where most businesses are today.
A few years ago, the conversation was simple: use data. Most owners were flying on gut feel with nothing on the panel. Today, the problem has flipped. Especially in high-tempo BizAv operations like scheduling, dispatch, and charter management, there's data everywhere. Multiple platforms. Endless reports. And when the Visionary asks, "Are we actually winning?" nobody answers cleanly.
That's not an information problem. That's a measurement problem.
In Data (EOS Traction Library), Mark O'Donnell, Mike Stanley, and Angela Kalemis draw a line that changes the conversation: numbers aren't about numbers. They're about tracking behaviors and habits in a measurable way so you can get what you want out of your business.
The solution is a Scorecard: five to fifteen weekly numbers that give you genuine situational awareness without the cockpit noise. For a dispatch operation, those might be the quotes-to-contracts ratio, on-time departure rate, cost-per-leg variance, and repeat booking frequency. Owned. Reviewed weekly. Acted upon.
Work backward from the outcomes that matter: new customers, existing orders, client satisfaction, profitability, and cash. Find the leading indicators that predict them. That's your glass panel configured right.
Now layer in AI, and the stakes get higher, not lower. Automation is only as smart as what you feed it. A dispatch operation running AI-assisted scheduling on dirty data, wrong inputs, inconsistent definitions, numbers nobody owns, doesn't get efficient. It gets efficiently wrong, at scale, faster than any human could manage. If you want AI to drive better decisions, you need clean data upstream. That means a culture where measurement is disciplined, ownership is clear, and the numbers actually reflect reality. Garbage in, catastrophic out. The aircraft doesn't care how sophisticated your autopilot is if the altimeter is lying.
The goal is a team with enough data literacy to read the instruments and the judgment to know which ones to trust when it counts.
Data equals freedom and confidence, but only when it's the right data, kept clean, in the right hands.
Don't block out the panel. Don't chase every readout. And never hand the controls to an autopilot you haven't calibrated.
Fly the airplane.
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