Data visualisation is the process of representing data graphically in order to identify trends and patterns that would otherwise be unclear or difficult to discern. Data visualisation serves two purposes: to bring clarity during analysis and to communicate.
The choice of what type of graph or visualisation to use depends greatly on the nature of the variables you have, such as relational, comparative, time-based, etc. Here we have adopted and modified the categorization system used by ManyEyes (archived link, IBM closed this service in 2015).
That said, sometimes graphing data with an inappropriate visualisation can lead to insights during analysis that would have remained hidden. Experimentation with visualisations during analysis is okay, but when communicating a visualisation, use the graph types listed under the proper options below. Incorrect visualisation leads to confusion, errors, and abandonment among viewers.
The options listed here can support both purposes of analysis and communication. You may want to graph data during analysis to see, for example, spikes in website traffic related to your social media campaigns. Visualisation, in this instance, eases data analysis. When communicating that data, however, the visualisation may need to be simplified and key areas may need emphasis in order to call the attention of readers and stakeholders. See the discussion under Report and Support Use for more information about how you may want to repackage a data visualisation for communication purposes.
Each main option below contains several visualisation possibilities. Click on each to see examples and read advice on using and choosing that visualisation option.
This graphic by Andrew Abela from Extreme Presentations provides a good representation of different types charts that can be used to visualise data. Click on the graphic to download a pdf version.
(c) 2006 A. Abela, used with permission. www.ExtremePresentation.com.
See relationships among data points
Displaying the relationship between two quantitative variables plotted along two axes. A series of dots represent the position of observations from the data set.
Summarising a multidimensional data set in a grid.
Depicting how people or other elements are related to one another.
Compare a set of values
Illustrating the main features of the distribution of a data set in a clear way.
Presenting a frequency distribution of quantitative data in a graphical way.
Providing a way to communicate complicated data sets quickly and easily.
Using a target line to show progress to date, often with levels of performance graphed in the background.
Deviation bar graph
Aligning two bar graphs along their spine to compare the shape of their data sets.
Plotting two or more dots on a single line for each category being compared.
Positioning several small graphs with the same scale in a row for easy comparison.
Changes over time
Displaying information as a series of data points connected by straight line segments, on two axes.
Comparing change between two points in time with a line.
Split Axis Bar graph
Regraphing comparison between two points in time by simply graphing the change that has occurred in that time frame.
Visualising how a group of quantities changes over time. Items are "stacked" in this type of graph allowing the user to add up the underlying data points.
See the parts of a whole
Arranging a matrix of icons (usually 100 or 1000 icons) typically as a frequency-based representation, simultaneously displaying both the number of expected events and the number of expected non-events.
Illustrating proportion through a circular chart divided into sectors (like slices of a pie).
Making use of qualitative information in the form of important distinctions or differences that people see in the world around them. They help overcome some of the problems that may be encountered when dealing with qualitative information.
Analyse a text
Connecting key words in a text using lines to show linkages.
Displaying the words in a qualitative dataset, organised by frequency of use.
See the world
Adding geographic information about digital content, within “metadata” tags - including latitude and longitude coordinates, place names and/or other positional data.
Using maps that allow zooming in and out, panning around, identifying specific features, querying underlying data such as by topic or a specific indicator (e.g., socioeconomic status), generating reports and other means of using or visualising select information in the map.