The Analytics pane – Exposing Insights from Data

The Analytics pane

Power BI Desktop includes the ability to add lines to a visual using the Analytics pane. Lines are useful to make it easier to see things such as trends, average, min, max, and other functions used in statistical analysis.

The type of lines that can be added depends on the visual used. Constant, trend, min, max, average, median, and percentile lines can be added to area, clustered bar, clustered column, line, and scatter chart visuals. X- and y-axis constant and symmetry shading can be added to scatter charts.

To add these lines to a visual, select the visual and then click the magnifying glass icon (Figure 12.10) to activate the Analytics pane portion of the Visualizations pane.

Figure 12.10 – Magnifying glass icon to activate the Analytics pane

In the Analytics pane, you can select which line you would like to add and select the options associated with each, such as the color, transparency, style, and configuration for combining the data series or highlighted values when the line gets rendered by Power BI.

These analytics lines allow you to provide greater emphasis on things such as trends, min, or max while using visuals. For example, you might start with a visual as in Figure 12.11 showing the count of sales by date:

Figure 12.11 – Clustered column chart showing the count of sales for each day in the year 2000

To better emphasize how the count of sales has increased in the second half of the calendar year, we might add a trend line to this visual and we’ll see the overall upward trend of the count of sales from January 1, 2000, through December 31, 2000, as shown in Figure 12.12:

Figure 12.12 – Clustered column chart with a trend line showing the count of sales for each day in the year 2000

The great thing is that these lines are dynamic based on the data presented in the visual. So, if we have a slicer that allows us to focus in on the first half of the year, we’ll see that the trend in the first part of the year (January 1 to June 30) was going down slightly, as shown in Figure 12.13:

Figure 12.13 – Clustered column chart with a trend line showing the count of sales for each day from January 1 to June 30

We can see that creating analytic lines on visuals adds additional user-friendliness that makes getting value from visuals even easier for data consumers.