I recently had the pleasure to attend a couple of Andy Kirk’s talks (one being at the Data Visualization In New York Meetup) on the topic of, well, Data Visualization. I’ve known about the field for some time but I hadn’t had much of an introduction up until this point. After spending a day in data vis. information sessions and presentations, I’m interested in further exploring the field. This post will serve as my initial high-level understanding of data vis. but I plan to dive deeper in future posts. Also, I am not a designer or developer so my writing will come more from a theory-type angle.
Data Visualization is a large field, combining both science and design and is the representation and presentation of data that exploits our visual perception in order to communicate information both clearly and effectively. It can encompasses categories such as: Data Visualizations (charts, graphs, maps), Visual Analytics (analysis, exploratory, computational), Scientific Visualization (biological, medical, 3D…), Infographics (charts, illustrations, hand crafted, diagrams, tables, video), Information Design (communication, forms, wayfinding, instructions), Physical/Ambient (beyond visual - sculptures, tactile…) and Data Art (data driven expression, exhibition)…
Some basic principles to consider when thinking about data visualization:
- As a designer, think about what it is that needs to be communicated.
- Consider the audience- Readers come with their own contexts, biases and assumptions.
- Consider the data itself and how it informs the truth.
The immediate importance of data visualization:
- Communicates complex information fast.
- The visual element of the data representation aims to engage the viewer quickly and present information that can change behavior.
Check out this quick PBS Off Book series introduction to Data Visualization:
Some of my interests I plan to explore in future posts:
- What makes a good data visualization?
- How to create data visualizations for clients (knowing audiences, constraints, resources…).
- Understanding data vis. from a design perspective- What is the question and the goal? Why were certain design decisions made?
- Emotional aspects of a good data visualization.
- Data visualization as art.