Overview of data visualization types

Score visualizations

This type of data visualization shows a single value or score as a number or percentage. Scores are often used to show how a particular value or metric compares to a target or benchmark. They can be useful for tracking progress or identifying areas for improvement, for example showing a company's or division's overall performance.


Visualization	Description
Single score visualization	Single-score visualizations display a single aggregate value that is important to your business. Single score visualization example
Dial visualization	Dial visualizations show where a single value lies across a range from minimum to maximum expected values. Visually, a "needle" points to the value, and the dial is colored in for values up to the needle. Dial visualization example
Gauge visualization	Like dials, gauges show where a single value lies across a range from minimum to maximum expected values. In addition to dial functionality, you can set colored data ranges to help users understand what the value represents. Gauge visualization example

Time series visualizations

Time Series visualizations show data over time. All time series visualization types share configuration options. They differ in use case, depending on whether you want to emphasize data trends or the differences between individual data points. For more information about these use cases, see Create time series visualizations.

Note:

In addition to data from within your instances and imported data sources, you can also use MetricBase data in time series visualizations. For more information, see MetricBase application.

Table 1. Time series visualization types
Visualization	Description and use case
Visualizing trends in a data source
Line	Shows how one or more values change over time by connecting a series of data points with straight lines. Use a line visualization to emphasize the trend in the data. Consider line visualizations to be the default choice for showing a time series. If you’re unsure of which visualization to use, use a line.
Spline	Shows how one or more values change over time by connecting a series of data points with a fitted curve. The curve emphasizes the trend over individual data points. Spline charts let you take a limited set of known data points and approximate intervening values.
Scatter	Shows unconnected points for values in the Y-axis against time in the X-axis. Usually the trend line is also shown. Use with a spread of data that can’t be usefully connected with a line.
Comparing scores in a data source
Column	Shows changes in data over time by showing values as proportional vertical columns. Use either to visualize changes in one data source or to compare data sources. To compare data sources with a column visualization, either add data sources to the visualization, or place several column visualizations next to each other in a dashboard.
Step	Emphasizes changes in a data source between discreet points in time. Use to show small incremental changes, especially when a line visualization smudges the data.
Comparing scores or trends between data sources
Area	Resembles a line visualization, but the area between the axis and line is emphasized with colors. Use with multiple data sources to highlight the relative contribution that each data source makes to the whole.

Bar visualizations

Bar visualizations enable you to compare scores across data dimensions. Horizontal and vertical bar visualization types are available. They share all configuration options. In general, use horizontal bars for nominal or categorical data. Use vertical bars for ordinal or sequential data. Use different colors or patterns to distinguish different groups or categories. For more information, see Create a horizontal or vertical bar data visualization.


Visualization	Description
Horizontal bar visualization	Bar visualizations show categories labeled on one axis and values on the other. Use vertical bars to compare ordinal data, especially when there aren’t too many categories, such as sales numbers grouped into buckets. Use horizontal bar charts with nominal data, such as incident severity or assignment group. Horizontal bar visualization example Vertical bar visualization example Pareto bar visualizations help you identify the most important dimension in a large set of dimensions. Columns show data in descending order. A line shows cumulative percentage. Pareto charts contain both bar and line graphs. The bars display the data in descending order from left to right, and the line graph shows the cumulative totals from each category in the same order. The left Y axis is the record count, and the right Y axis is the cumulative percentage of the total number of records evaluated.
Vertical bar visualization
Pareto bar visualization

Pie and Donut visualizations

Pie and donut visualizations show the relationship between parts and the whole of a data set. The segments of these charts should total to 100%. For more information, see Create a pie or donut visualization in the Visualization Designer.


Visualization	Description
Pie visualizations	Pie charts are best when comparing 5–7 segments that total 100%, when no two segments have a value within 10% of each other. Donut charts are best for comparing no more than five segments that total 100%, when no two segments have a value within 10% of each other. The center of the donut can be used to show additional information. Semi-donut charts are best for comparing no more than four segments that total 100%, when no two segments have a value within 10% of each other. Pie visualization example Donut visualization example
Donut visualizations
Semi-donut visualizations

Multidimensional charts

Multidimensional visualizations enable you to show multiple variables in a single chart, and it can be useful for showing the relationships between different variables. They are useful when you have a lot of data, and you want to find patterns or trends that might not be immediately obvious. They're also good to use when you want to show the relationship between three or more variables.


Visualization	Description
Pivot table visualization	Pivot tables allow for several kinds of aggregation between its fields. You can also filter the data. The columns represent one field or breakdown, while a hierarchy of rows represents multiple other fields or breakdowns. Pivot visualization example
Heatmap visualization	Heatmaps show the relationship between two table fields or indicator breakdowns. The changes in color as you move along the axes reveal patterns in the value of one or both fields/breakdowns. Heatmap visualization example
Bubble chart visualization	Bubble charts are circles of different sizes along an x-y axis. The x and y axes represent different numeric fields, such as values or amounts. Use the relative size and position of the circles to compare fields and see their relationships. You can also group the data by a third field, which can be qualitative. The third field is differentiated by color. Use bubble charts to answer binary questions, such as whether two fields have a relationship, and to highlight patterns. Bubble visualization example

Other visualizations

Data visualizations can also show calendars, simple lists, indicator scorecards, and location.


Visualization	Description
Calendar report visualization	Displays data-driven events in a calendar format.
Indicator scorecard	The Indicator scorecard component enables you to visualize and compare data between multiple Performance Analytics indicators. Create an Indicator Scorecard
Simple list	Shows a list of table records. Has a reduced set of features compared to a List component, allowing the simple list to fit in small spaces. Simple list visualization example
Box plot	Use a box plot to show the median and lower and upper quartiles of numeric data along with outliers. You can also compare the distribution of different groups of this data. Available in the latest Data visualizations application from the ServiceNow® Store.
Geomap	Displays data by country, state, or city. Users can use table data that contains location information to visualize in the chart.