1. Six top-level stats jump out at you: customers, orders, revenue, growth %, current week revenue, previous week revenue. All of these stats are adorned with a few substats (smaller text), almost always a % up/down from last period
2. A few large panels with breakdowns: revenue over time, revenue vs projections, revenue by referral source, revenue by location
3. The top right has your filter buttons, and generally it includes every breakdown dimension on the page. For example, "let's look at this dashboard by just the Google referral source" or "let's look at our stats from the U.S. geography only" or "let's filter this for last 2 years only"
4. Drill-down is "top selling products." This isn't truly a drill-down, as it is still an aggregation, so you really want to drill-down to the record-level. If you filter the dashboard for "U.S. sales by the Google referral source for the last 2 years only", people invariably want to see what the actual row-by-row sales were, and that is the drill-down. They can easily export this and reconcile to source systems. As an example, for some of the work I do, sales reps don't just want aggregations about their sales leads, they want the actual names of actual sales leads (as rows) so they can contact them.
So again, four major parts to a dashboard, which really drive from two simpler (likely familiar to most data analysts): metrics and dimensions.
