Data analysis is the process of inspecting, cleaning, and interpreting raw data to uncover patterns, trends, and insights that inform decisions. In UX, it usually means turning quantitative data like analytics, survey results, or usability metrics into findings you can act on.
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It can be as simple as spotting a drop-off point in a funnel or as involved as running statistical tests across large datasets. Either way, the goal is the same: replace guesswork with evidence.

Why does Data analysis matter?

Data analysis matters because it grounds design decisions in evidence rather than opinion. It helps you prioritize what to fix, validate whether a change actually worked, and spot problems you wouldn't notice just by looking at a design.

You'll use it after usability tests, when reviewing product analytics, or when digging into survey responses. For example, analyzing session recordings alongside heatmap data might reveal that users consistently miss a call-to-action button, giving you a clear, evidence-backed reason to redesign it.

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