There are many ways to visualize your PowerMetric data in Klipfolio. This article defines each visualization type and suggests which ones to use for the PowerMetric you want to display.
- Bar Chart
- Line Chart
- Pie Chart
- Area Chart
- Radar Chart
- Waterfall Chart
- Heat Map
- Score Card
The Bar Chart displays a comparison of values as bars along an axis. Bar charts are an effective way to compare the quantities of different categories. You can choose which segment your x-axis represents using the drop-down at Over on the right. If the data set tracks values over a set period of time, use a line chart or an area chart instead. The example below shows sales revenue by country.
You can apply segments to create stacked bar charts. When viewing a stacked bar chart, the top of the bar indicates the total value, while each coloured section represents a subtotal for each segment. The example below shows total monthly expenses segmented by the total revenue generated by different products.
Note: While bar charts can be used to show parts of a whole value, depending on your data, it may be easier to understand this information in a pie chart.
The Line Chart , like the area chart, displays a comparison of values over time by plotting points joined by line segments. It’s great for comparing PowerMetrics as well as showing trends over time.
The Pie Chart displays categorical data divided into sections, so you can see each section’s value in comparison to the whole. You can mouse over segments to see the specific values for each one. Pie charts are effective at quickly conveying information but if your data includes a lot of segments, this impact can be weakened and your message blurred. In such cases, opt for a different visualization type. Note that pie charts do not display comparisons to a previous period.
Area Charts display changes in values over time, plotted as line segments. The area below the line segments is shaded, drawing greater attention to significant changes in value.
Note that when you are viewing segmented data, the areas are stacked. This means you must mouse over an area to see its assigned value. The top line of the stack indicates the cumulative value of all segments.
Area charts are an effective way to quickly detect trends in data over time, but can become confusing if too many segments are included. If this is the case, we recommend switching your visualization type to a line chart.
Treemaps are used to display hierarchical data, meaning that each category can be subdivided by different segments to make up a whole value. The value each rectangle represents is reflected in its area size. For example, in a PowerMetric for total sales for three different sales agents, the agent with the least sales will have the smallest rectangle. Note that because this PowerMetric is only comparing one level of data, all the rectangles display as the same colour.
When visualizing multiple segments of data (you can view up to three levels of hierarchical data on a treemap) different categories are differentiated by colour. Subcategories are represented as smaller rectangles (also scaled based on value) nested within the rectangle for their category. In the example below, the rectangle for Carmine Carman is blue and contains rectangles for eggs, bread, butter, and milk. You can tell at a glance that she made the smallest percentage of her total sales on milk because it is the smallest of the blue rectangles.
A Radar Chart (also known as a “spider chart” or “web chart”) enables users to display data with multiple variables on a chart of three or more axes. It functions as a wrap-around line chart, making it easy to identify outliers and correlations in data. When comparing multiple segments, you can quickly see which values have the greatest degree of difference and which values overlap by looking at how their shapes intersect. Consider using a radar chart as a substitute for a regular line chart when you have limited space on your dashboard. Note that if you have a large number of segments to compare, the visualization may become cluttered and difficult to interpret. If this is the case, consider using a line chart instead.
Waterfall Charts illustrate how an initial value increases and decreases by a series of intermediate values (displayed as floating columns), showing value progression over time. The final column represents the total balance. The example below displays monthly revenue (represented as floating green columns) leading to the total revenue balance in the final column.
A Heat Map (also known as a matrix) is used for comparing information in the same category. This visualization colour-codes value ranges so that users can quickly distinguish differences in values. The heat map legend shows which colours correspond to which values. Mousing over a cell displays its exact numeric value. The example below refers to sales figures by representative. You can quickly determine the top sales performers based on the number of cells and the depth of colour saturation.
Score Cards depict a single numerical value that’s used to track the status of a PowerMetric. This is a good visualization choice when you need to communicate a sum value without going into greater complexity.
The Table visualization displays your data in a “pivot table.” Pivot tables enable you to view your values as plain text, eliminating any ambiguity. These tables are useful for seeing the individual values of a metric, broken down by more than one dimension. You can assign up to two dimensions as nested levels in rows and, optionally, select one dimension for columns.
Note: To optimize clarity in your visualization, we recommend building your PowerMetric using fewer than 20 members. If your PowerMetric includes > 20 members, all members above the first 20 will be grouped together under “Other” in your visualization.
In the example below, the table displays sales revenue. The columns organize the data by month while the rows segment the values by sales agent and territory.
The table visualization is also useful for identifying blank entries. In the table above, you can see that there is a blank entry for Kelvin Kash’s total revenue for Canada in March. This would not come across in another visualization, such as a line chart, in which a total value for sales in March would display without indicating the missing entry.
If you choose not to assign a dimension to your columns, your table will contain one column of data and use your metric name as the column header. In the example below, the column is named Revenue after the name of the metric. If you change the metric name, the column header will update to match it.
Note that the table visualization has of limit of 2000 value cells.