Why do conclusions diverge so fast?
I’ve seen two analysts work with the same dataset and timeframe, both technically correct - and arrive at opposite recommendations. Each backed by numbers.
Where does the split come from?
Hidden assumptions:
• metric choice
• filters
• baselines
• interpretation logic
And you know what? That's okay!
These decisions are rarely visible, but they shape the outcome more than SQL ever does.
What’s the uncomfortable part?
Analytics isn’t just calculation. It’s a series of choices about what matters. When assumptions stay hidden, data becomes a storytelling tool.
What I always require
Not just numbers - but assumptions. What this report answers. What it explicitly doesn’t. And what alternative interpretations exist. Without that, “data-driven” is just a label.
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