Early in my career, I believed the advice everyone gives women in data: learn one more tool, one more language, one more platform. So I did. SQL, analytics, data pipelines, cloud, healthcare data, enterprise platforms. The work kept getting harder. The recognition didn’t scale with it.

This session is a reality check not anti-learning, not anti-tech, but honest. Many women in data are already technically capable. The real blockers are decision rights, narrative control, and who gets trusted when ambiguity shows up. Yet the default advice remains: “upskill more.”

I’ll walk through real delivery scenarios from large data programs where women carried the technical and operational load but were still positioned as “support,” while less technical voices shaped the story and decisions. We’ll unpack why skill-stacking alone doesn’t fix visibility gaps, how over-competence can actually trap women in execution roles, and when “learning more” is avoidance by leaders and by us.

This isn’t a motivational talk. It’s a practical reframing. We’ll discuss where additional skills do matter, where they don’t, and what actually shifts influence in data organizations: framing problems, owning risk conversations, and deciding when to stop proving and start positioning.

Key Takeaways:

When more skills help and when they quietly hurt

How to recognize when you’re over-investing in execution

Practical ways to move from “reliable expert” to decision influencer

Soundbite

“If learning more tech was the answer, most women in data would already be running the room.”