As a data analysis expert, sometimes I wonder—are we overcomplicating things?

I have been working in data analysis for a few years now, and while I truly love what I do—digging into messy datasets, uncovering patterns, building dashboards, running regressions—lately I have been thinking about how often we (as analysts or teams) overcomplicate things.

I have seen teams spend weeks building complex machine learning models, when a simple bar chart would have told the story better and been easier to explain to stakeholders. I have sat through meetings where everyone nodded at a p-value, but nobody could answer the business question clearly.

Do not get me wrong, I appreciate the technical depth we bring to the table, but I feel like sometimes we lose sight of the "why" behind the data. We can predict churn with 92% accuracy, but what are we doing with that insight?

I guess I am just curious—how do other data folks here keep their work grounded and actionable? How do you avoid falling into the trap of building for the sake of building?

Would love to hear your experiences. Whether you are a veteran or just starting out.