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Foundations First

Making Data Work
Outcomes Over Technology

Three departments, three definitions of revenue, same airline. The problem isn't the dashboards — it's that nobody has the same definition of what they're measuring. Fix data trust before visualization.

At some point at every airline...

CEO: "Revenue reporting is a disaster."

Data team: "We can fix that. Real-time feeds, natural language querying, elastic compute!"

Everyone excited about executive sponsorship and finally solving this: "Let's go!"

But here's what's actually broken:

Finance is double-counting revenue because they can't see exchanges.

Revenue Management is using only the "final version" of data, blind to the full life of a booking, including cancelations and no-shows.

Commercial Analytics is busy project managing everything from bug fixes to ingesting data from some new system, delivering Tableau reports with good numbers but never with the detail that answers the next business question without a change request to IT.

Three departments. Three results. Same airline.

The problem isn't the dashboards — it's that nobody has the same, precise, point-in-time definition of revenue. No amount of visualization will fix data you can't trust.

This problem has been solved. I've seen it, and the teams that did it started with the boring stuff first.

Versioning logic. Shared definitions of what constituted revenue, OD and sales channel. Fit-for-purpose views across subject areas.

Armed with unified, historical data, suddenly teams could respond to the questions the CEO really wanted answered.

Seasonality fixed — worth millions in RASM. Premium seat prices optimized — take rates up double digits. Finance forecasts trusted — synergy goals met.

The fancy dashboards? They got those, too. Built on data everyone trusted.

This problem isn't unique, and it's not your team's fault — it won't be solved by new tech.

It's a system design problem. We see the symptom — bad reports — and fix that instead of the real flaw — unreliable data. Our instinct is to chase what's visible over what's valuable, what's flashy over what's fundamental.

Making data work for commercial teams means building trust in your data before building beautiful visualizations and deploying AI.

Push back. Focus on the foundation. Get the data right, everything else follows.

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These perspectives are a sample. Each week, I put out a new brief on what I'm seeing inside airline data teams — the patterns, the pitfalls and what actually separates investments that deliver from ones that don't.