This guide provides information about best practices for creating a data visualization, including design tips, design tools, and recommended books.
It is created and monitored by the DHSS Library.
“Information graphics are visual representations of information, data or knowledge often used to support information, strengthen it and present it within a sensitive context….They are specific, context-sensitive and often times hand-crafted. Data visualizations are visual displays of measured quantities by means of the combined use of a coordination system, points, lines, shapes, digits, letters quantified by visual attributes. They are general, context-free and often times created automatically. Both can be static, animated or interactive…So, I think the difference is more about the objective. Information graphics; are used to tell a story or answer a question. Data visualizations are used to let the user find his own story or answer.”
-Benjamin Wiederkehr of Datavisualization.ch retrieved from https://readwrite.com/2011/01/07/difference-between-datavisualization-infographics/
Image retrieved from LinkedIn Slideshare, 2018, Infographics vs. Data Visualization: The Critical Difference
(Credit: Brent Dykes, Forbes "Data Storytelling: The Essential Data Science Skill Everyone Needs")
"Data storytelling is a structured approach for communicating data insights, and it involves combination of three key elements: data, visuals, and narrative. When you combine the right visuals and narrative with the right data, you have a data story that can influence and drive change. "
1. Know your data & purpose
2. Pick a chart type
3. Choose the software or tool
4. Refine your visualization