Dashboards are the easy part. The value is in the steps before it — understanding why the business is asking the question in the first place, and the steps after it — turning a finding into something someone can act on. Here's the method behind every case study in this lab.
Before opening any dataset, I map out who is affected by the problem, what process generates the data, and what decision would actually change if the analysis went one way or another. Skipping this step is the fastest way to produce a technically correct analysis that nobody uses.
Structure, volume, missingness, and distribution — before any modeling. This is where I find out whether the data can actually answer the business question, or whether the real finding is about the data itself.
Correlation and segmentation, used to explain rather than just describe. I look for where a metric should vary — by priority, by team, by category — and diagnose what it means when it doesn't.
A KPI only earns its place if it would change what someone does next. I design a small set of metrics tied directly to the root causes found in step 3, not a generic dashboard checklist.
Recommendations are specific enough to assign to someone: a process change, a system rule, a policy. Vague advice like "improve communication" doesn't appear here.
A number, with the assumptions behind it stated plainly. I'd rather show a transparent estimate with clear assumptions than a polished figure with hidden ones.