Dashboard Mode - Why optimization ensures scalability
Business Question
How does optimizing data models and calculations help a Power BI dashboard handle more data smoothly as the business grows?
How does optimizing data models and calculations help a Power BI dashboard handle more data smoothly as the business grows?
| OrderID | Region | Product | SalesAmount | OrderDate |
|---|---|---|---|---|
| 1001 | North | Widget | 150 | 2024-01-15 |
| 1002 | South | Gadget | 200 | 2024-01-20 |
| 1003 | East | Widget | 300 | 2024-02-05 |
| 1004 | West | Gizmo | 250 | 2024-02-10 |
| 1005 | North | Gadget | 180 | 2024-03-01 |
| 1006 | South | Gizmo | 220 | 2024-03-15 |
Total Sales = SUM(Sales[SalesAmount])SUM(Sales[SalesAmount]) grouped by Sales[Region]SUM(Sales[SalesAmount]) by Sales[OrderDate]Sales Last Month = CALCULATE(SUM(Sales[SalesAmount]), PREVIOUSMONTH(Sales[OrderDate]))+----------------------+----------------------+ | Total Sales | Sales by Region | | (KPI Card) | (Bar Chart) | +----------------------+----------------------+ | Sales Over Time | Sales Details | | (Line Chart) | (Table) | +----------------------+----------------------+ | Sales Last Month (KPI Card) | +-------------------------------------------------------+
Filters on Region and OrderDate connect all visuals. Selecting a region updates the bar chart, line chart, KPI cards, and table to show only that region's data. Date slicers let users focus on specific time periods, updating sales trends and recent sales calculations instantly.
If you add a filter for Region = North, which components update?