Information graphics and data visualizations are two of the latest online trends. There are sites dedicated to it, volumes of books detailing the process of it, and visualization tools for just about everything, everywhere you look: Social media, Your resume, Your money.
There are a ton of tools out there, but what does it take to pull off a successful visualization? And what exactly is the difference between information graphics and data visualizations? That’s right, they’re not the same thing!
Information Graphics vs. Data Visualizations
An information graphic generally deals with answering and analyzing knowledge, whereas a data visualization is typically a more complex examination of a set of statistics. Information graphics are, more often than not, created ‘by hand’ by an individual or a design team (using programs such as Adobe Illustrator or Photoshop). Data visualizations are typically created in an automatic fashion using software to generate the image (SAS or Tableau). An extreme example to contrast the two would be visualizing Girl Scout cookie sales with cookies (infographic) paired against business analytics (data visualization).
In either case, there is a question or set of questions that need to be answered. As simple as it sounds, it is very easy to get caught up in the analysis and stray from the initial question or question set. This can lead to scope creep in a project (learn more about scope creep in Professor Kevin Crowston’s course, IST 654 Information Systems Analysis).
The Seven Steps of Visualizing Data
Once you’ve got the differences straight, it is time to dig in. There are a multitude of different ways you can go, but one of the best structures I have come across is by Ben Fry. Fry is a data visualization genius, co-developer of Processing and the author of Visualizing Data. Though his book can get extremely technical and is geared toward someone with significant programming experience (as well as statistical analysis skills), his Seven Stages of Visualizing Data are relevant to anyone interested in displaying data.
To summarize, his seven steps are:
- Acquire – Obtain the data, whether from a file on a disk or a source over a network.
- Parse – Provide some structure for the data’s meaning, and order it into categories.
- Filter – Remove all but the data of interest.
- Mine – Apply methods from statistics or data mining as a way to discern patterns or place the data in mathematical context.
- Represent – Choose a basic visual model, such as a bar graph, list, or tree.
- Refine – Improve the basic representation to make it clearer and more visually engaging.
- Interact – Add methods for manipulating the data or controlling what features are visible.
If you can identify (and stick with) your question and work through Fry’s stages, your result will be a cohesive and impressive product. Keep in mind that there is nothing easy about the development process, so don’t get frustrated. I have worked long and hard on some visualizations I thought were beautiful, only to have the first person I showed it to be totally confused by what I developed.
Information graphics and data visualizations are topics that can be written about ad nauseam. I’ve touched on a small slice of what can be examined, especially given the explosion of data that we’re trying to manage. There can be global cross-functional teams working on a data project, or an individual toying around with a way to get a visual grip on his or her finances.
One great resource that is both rich and understandable for those with beginner and intermediate level experience is the Flowing Data site/blog. You can also check out its companion, The Flowing Data Book, Visualize This: The Flowing Data Guide to Design, Visualization, and Statistics.
Have you used infographics or data visualizations personally or professionally? Is there something you think I missed? Feel free to leave a note in the comments below, email me, or find me on Twitter.