How does one select the "right" or "best" way to visually represent data? Of course, the short answer is it depends. (In fact, that theoretical manner might not even exist.)
Beyond that, it's an interesting question and, as I argue in The Visual Organization, a harder one to answer these days for two reasons. First, there's just so much more data flying around today. Second and in a related vein, there are more ways to represent this information.
Against that backdrop, wouldn't it be useful to consult a dataviz guide? A visualization of the different types of visualizations available to us (read: a metavisualization)?
It turns out that such a thing does, in fact, exist.The Data Visualization Catalogue is currently an ongoing project developed by Severino Ribecca. From the site:
Originally, this project was a way for me to develop my own knowledge of data visualization and create a reference tool for me to use in the future for my own work. However, I thought it would also be useful tool to not only other designers, but also anyone in a field that requires the use of data visualization regularly (economists, scientists, statisticians etc).
Or, visually speaking:
This is just a sample of the types of data visualizations that anyone with some data and basic computer fluency can create. The site contains many more – with more coming soon (as evinced by the gray colors of bubble charts, arc diagrams, etc.) Think of these types of tools as starting guides for finding an ideal way to convey the meaning of data and, just as important these days, to encourage data exploration and discovery.
Simon says
Projects like the dataviz catalogue underscore the fact that we have entered a more democratic and transparent information era. Open data, mobility, big data, cloud computing, APIs, SDKs and other trends are all converging.
Brass tacks: It's high time for everyone in organizations to think about the data that they visualize – and how they visualize it. Tried-and-true methods may have worked ten or even five years ago. Foolish is the organization that is stuck in the "set it and forget it" mindset.
Feedback
What say you?
6 Comments
In my experience, the answer to what is "best" requires that you know your audience. In our increasingly diverse culture, there are age groups that still relate to data by looking at it in spreadsheets, while other age groups would never consider that format to be valid. So, the key in my mind is flexibility, with a constant eye toward how your intended audience digests information.
There certainly are more types of data--and ways to represent those types--than ever, Steve. Thanks for the comment.
From the perspective of a non-statisician, but an information professional who relies on visualizations to tell the "thousand word story", I think this catalog is much-needed and I'm grateful that you pointed this one out. The more we can see examples of how these visualizations work and the tools that created them, the better they will fit into the boardroom conversation.
Thanks, Mike. There's a figure in my new book on which dataviz is best, but that's a starting point to a conversation.
Thanks for this pointer, and for the larger discussion. I agree with Mike Huberty -- graphics need to be part of the daily discourse, and the better the graphics are, the more valuable the discussion. I'm a visualization consultant, working with clients to help them improve their data graphics for communication. This catalogue will be a great resource for me, and for them. Will keep an eye on Severino Ribecca's site for sure.
Hi,
I am working for several years on this topic (I am even paid by the French government to do so... What a pleasure !) : Which visualization for which analysis ? For sure there are other criteria such as:
- data (univariate, multivariate, geo...)
- users (beginner, expert, stressed expert...)
- usage (data exploration or visual explanation)
- context (device, location, in crowd...)
Tableau has built a small tool ("Show me") to help users to pick the right data visualization. But it's very limited, especially to the few graphs that this software can generate. There are other algorithms such as the one made by SAS in JMP (I like its density. It's more robust, but only focused on statistics) or there are totally new systems invented by researchers... such as the one built by the University of Illinois at Chicago in 2010.