Search “Power BI dashboard” and you get a thousand screenshots and almost no measures. Screenshots are easy to admire and impossible to rebuild. This page does the opposite: three Power BI dashboard examples you can reconstruct, with the DAX that powers them and the layout logic behind each.
One thing to settle first, because it trips up nearly everyone.
Power BI dashboard vs report: the distinction people miss
In Power BI, “dashboard” has a specific meaning that differs from a report, and mixing them up leads to a lot of confused Googling.
- A report is multi-page, highly interactive, and binds to a single semantic model (dataset). Slicers, drill-through, and cross-highlighting live here. This is where you build.
- A dashboard is a single canvas in the Power BI service, assembled by pinning tiles from one or more reports. It can combine multiple datasets, and clicking a tile jumps you back to its source report.
One catch worth knowing before you spend an afternoon on it: some report formatting does not carry over when you pin a visual to a dashboard, including background color, exact width and height, and tooltips. Design the visual to stand on its own, because the tile will not inherit everything you tuned. Most “Power BI dashboard examples” you see online are actually report pages, which is fine, that is where the design work happens. The examples below are built as report pages you can then pin.
Power BI dashboard example 1: the executive KPI strip
The most-used and most-abused Power BI layout. A row of KPI cards across the top, a trend underneath, a breakdown at the bottom. It works because it follows the F-pattern: the eye lands top-left and moves right.
Top row: four card visuals. Use the 2025 card visual, which embeds a sparkline and a reference label so a single tile shows the number, its trend, and its target without three separate visuals. Revenue, gross margin, active customers, and net revenue retention is a defensible starter set for a SaaS exec.
The number in each card is a DAX measure, not a column. Measures calculate at query time and respect whatever filters are active, so the same card obeys the date slicer without extra work.
| Measure | DAX |
|---|---|
| Revenue | Revenue = SUM(Sales[Amount]) |
| Revenue YTD | Revenue YTD = TOTALYTD([Revenue], 'Date'[Date]) |
| Revenue last month | Revenue LM = CALCULATE([Revenue], DATEADD('Date'[Date], -1, MONTH)) |
| Revenue MoM % | Revenue MoM % = DIVIDE([Revenue] - [Revenue LM], [Revenue LM]) |
A few things doing real work here. TOTALYTD is shorthand: under the hood it wraps CALCULATE and DATESYTD, stripping the existing date filter and replacing it with year-start through the current period. DATEADD shifts the whole date context back one month for a clean prior-period compare. And DIVIDE is deliberate, not /, because DIVIDE returns a blank instead of an error when last month is zero, which happens the moment someone filters to a brand-new product.
Any of these time-intelligence functions needs a proper date table: a dedicated table with a continuous run of dates covering every year in the model, marked as the date table. Skip it and TOTALYTD quietly returns nonsense, which is worse than an error because nobody notices.
Color the cards with conditional formatting: green when the measure beats target, red when it misses. Drive it off a variance measure so the rule is one place, not four.
Power BI dashboard example 2: the sales pipeline
The sales dashboard carries the highest commercial value of any Power BI layout, and the cheapest to get wrong, because most versions show activity (calls made, emails sent) instead of the metrics that predict revenue.
Four numbers belong on the top row: pipeline coverage, win rate, average deal size, and sales cycle length. Together they feed the one metric a sales leader actually forecasts on, pipeline velocity.
| KPI | DAX | Reads as |
|---|---|---|
| Win rate | Win Rate = DIVIDE([Opps Won], [Opps Closed]) | Share of closed deals that were won |
| Average deal size | Avg Deal Size = DIVIDE([Revenue Won], [Opps Won]) | Revenue per won deal |
| Pipeline coverage | Pipeline Coverage = DIVIDE([Open Pipeline], [Quota]) | Open pipeline as a multiple of quota |
| Pipeline velocity | Pipeline Velocity = DIVIDE([Open Opps] * [Win Rate] * [Avg Deal Size], [Cycle Days]) | Revenue throughput per day |
Read those together and they diagnose, not just report. Pipeline coverage under 3x going into a quarter means the team does not have enough at-bats to hit quota, and no amount of hustle late in the quarter fixes a coverage problem you should have caught in week two. Velocity rising while win rate is flat means bigger deals or faster cycles are carrying you, worth knowing before it reverses.
Under the cards, put a funnel chart for stage-to-stage drop-off, because a funnel is the one visual that shows exactly where deals die. Beside it, a sorted bar of pipeline by rep or by segment. Skip the gauge visual that Power BI keeps tempting you with; a gauge shows one value against a range and eats a quarter of the screen doing what a card does in a corner.
Power BI dashboard example 3: the operations view
Operational dashboards get read every day, sometimes every hour, so they optimize for a different thing than the exec strip: fast scanning and quick drill-down, not polish.
The layout that holds up: a thin KPI strip on top (open tickets, SLA breach count, average handle time), a line chart of volume by hour or day in the middle, and the star visual of an ops dashboard, a decomposition tree, at the bottom.
The decomposition tree earns its place. It lets someone drill into a metric across dimensions in any order (region, then product, then agent, or the reverse) without you building a chart for every path. When SLA breaches spike, an ops lead can crack the number open live and find the one queue causing it, which is exactly the root-cause question these dashboards exist to answer.
Add a slicer for date and team, and a bookmark or two to save common views (for example, “yesterday, escalations only”) so nobody rebuilds the same filter every morning. Bookmarks capture the full state of filters and slicers and can sit behind a button, which turns a dashboard into something closer to an app.
The visuals worth using, and two to skip
Power BI ships with more visuals than any real dashboard should use. A short opinionated list of what earns its place:
- Card visual for headline numbers. The 2025 version with a built-in sparkline replaced three older tricks.
- KPI visual when you want value, target, and trend fused into one, though a well-set card often does the same job cleaner.
- Line chart for anything over time. Non-negotiable.
- Sorted bar chart for rankings and category comparisons.
- Decomposition tree for interactive root-cause work.
- Funnel for stage drop-off in sales and onboarding.
Two to skip on an executive or ops screen. The gauge looks like it belongs on a dashboard and mostly wastes space; a card with conditional formatting says the same thing in a fraction of the area. And a pie chart past a few slices is a comparison you are making harder on purpose. Both survive in Power BI because they demo well, not because they inform well.
Model and refresh: the boring part that decides everything
Two backend habits separate a Power BI dashboard that stays fast from one that crawls after the dataset grows.
- Build a star schema. One fact table (sales, tickets) surrounded by dimension tables (date, product, customer), related by keys. Power BI is engineered for this shape, and a flat single-table import that felt fine at 10,000 rows will grind at 10 million.
- Clean in Power Query, calculate in DAX. Shape and filter source data in Power Query before it loads; use DAX measures for anything that has to respond to filters, like YoY or variance to target.
- Set refresh to the decision. Ops volumes hourly, sales pipeline daily, executive revenue monthly. Then show the last-refresh time on the page, because the first stale number an exec catches is the last time they open it.
Rebuild any one of these three examples and you have a Power BI dashboard that answers a question in five seconds instead of one that gets a polite nod in the review and never gets opened again.
Keep reading
- Dashboard Design: 9 Rules for Dashboards People Actually Open
- Sales Dashboard Examples and KPIs
- KPI Dashboard: How to Build One
- Dashboard Widgets: Which Tiles to Use
- How to Choose the Right Chart Type
- Excel Dashboard: A Practical Build