Your data science team is brilliant. So why are their models useless in the real world?

I’ve seen this movie a dozen times, from massive consumer goods companies to agile tech startups. A team of brilliant PhDs spends six months building a breathtakingly complex AI model. It works—in their pristine, isolated environment. But when it's time to go live, the model fails. It produces nonsense, and nobody can explain why.

This is the most common and costly form of "Doing Data Wrong" I see today: Tech-for-Tech's Sake. It’s the obsession with building a skyscraper (the fancy AI model) without first laying the foundation (the data infrastructure). You’re building on a swamp.

The result is always the same: a “garbage in, garbage out” catastrophe. Money is wasted, deadlines are missed, and worst of all, business leaders lose faith in the data team’s ability to deliver tangible value. The problem isn’t the model; it’s the data you’re feeding it.

The unglamorous, indispensable hero of this story is Data Engineering.

It’s the plumbing. It’s the electrical grid. It’s the foundational work that makes everything else possible. At places like Ambev, Itaú, and Alura, and especially when building my own company, I learned that you can’t generate a single dollar of ROI from data without mastering this. It means getting serious about:

• Robust Data Pipelines: Automating the flow of clean, reliable data.

• Data Governance: Establishing clear ownership and rules. Who can touch what? How is it validated?

• A Single Source of Truth: Ending the endless debates in meetings about whose numbers are “right.”

This isn't just a tech problem; it's a leadership failure. Here’s how we fix it.

The Manager's Playbook: What to Demand Don't just sign checks for "AI projects." Start funding and rewarding "data infrastructure" and "data quality" initiatives. Ask your tech leads pointed questions:

• "Show me the data lineage for this model. Where does every field come from?"

• "What is our automated process for catching data quality issues before they poison our analytics?"

• "Who is accountable for the quality of our core data assets?" Reward the teams that build the solid foundation, not just the ones building the flashy penthouse.

The Tech Pro's Playbook: How to Deliver Real Value

Stop chasing the shiny new model and solve the foundational problem. Building a trusted, reliable data pipeline that serves the entire organization is a monumental achievement—far more valuable than another prototype that never ships. Make your North Star "delivering trusted data at scale." Master data engineering and governance, and you will become one of the most valuable people in any company.

My passion, my purpose, is to bridge this chasm between technical possibility and business reality. The knowledge I’ve gathered isn’t mine to keep. It’s ours to use, to build better, and to stop building beautiful models on swamps of bad data.

Let's get our hands dirty and build something that lasts.

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