Planning a data visualization should always begin with a series of questions designed to better understand your audience, what you are trying to communicate, and how you intend to do so with visualization.

Who is your target audience?

It is important to understand what your audience knowledge and expertise level is. A data visualization for a publication in your field will be different than one for a general lay audience.

What are you trying to show?

What message are you trying to communicate with your data? What variables in your data are you trying to show? What relationship between those variables is important? This might be a correlation between variables, rankings of variables, distributions of variables, etc. The Understanding Your Data section of this guide can help you better understand aspects of your data and how those can best represented visually.

How do you intend to show it?

What is the appropriate visualization type for your data and what you are trying to communicate? The Choosing an Appropriate Visualization Type section of this guide can help point you towards the right visualization for your data.

What can we visualize?
 

When we visualize data we must convert data values into visual elements that comprise a graphic. To choose the appropriate visualization for your data it is important to first understand what kind of dataset you are working with. Common dataset types include:

Dataset Types	What Can Be Visualized		Examples
Table	Items		- Tabular data (.tsv, .csv), - Spreadsheet data (.xls, .xlsx)
	Attributes
Networks	Items (Nodes or Vertices)		- Phylogenetic trees, - social network analysis, - FlowingData Network Visualizations
	Links (Links or Edges)
	Attributes
Fields	Grids		- Simulations from fluid or solid mechanics, - astronomy, weather and climate data - data from geology and geophysics, - biomedical image data, - molecular data
	Positions
	Attributes
Spatial (Geometry)	Position		- Geographic information systems (GIS), - spatial point processes

Table content and images adapted from: Munzner, Tamara. Visualization analysis & design. Boca Raton, FL: CRC Press/Taylor & Francis Group, 2015. Print.
Scientific Visualization examples adapted from: Ozsu, M. Tamer, and Ling Liu. Encyclopedia of Database Systems. Springer, 2009.

How do I visualize my data?
 

Choice of visualization technique, framework, and design depend on the type of data being visualized. Select the guide specific to the dataset you are trying to visualize. If you would like to learn about data visualization in general see Designing Effective Data Visualizations.