Visual Analytics – A Gentle Introduction

May 23, 2016   |   Jason Lee

At This year’s CHI conference, Jean Scholtz, Russ Burtner, and Kris Cook ran a course on Visual Analytics.  As a User Experience designer or developer, you might have engaged in some Visual Analytics design in the past without even realizing it.  So what is Visual Analytics?  It’s the “science of analytical reasoning facilitated by interactive visual interfaces” (Thomas and Cook, eds., 2006).[1]  With the persistent problem of information overload that plagues our modern day experiences, Visual Analytics aims to help people make sense of data by combining machine learning, visualization, and human-computer interaction. If you’ve ever used any kind of health tracker, you may have used some type of dashboard with graphs and charts showing your health progress.  These types of apps and dashboards leverage visual analytics.  Imagine how much more difficult it would be to track your progress if all you had to work with was a basic spreadsheet full of numbers.

 

Visual Analytics deals with what are known as ‘wicked problems’[2].  These are problems that among other things have no optimal solution, have answers that cannot be characterized as true or false, and have no discrete set of possible solutions to pick from.  An example of a wicked problem is ‘How do we deal with global warming without greatly disrupting current economic and social structures?’  One can see of different charts and graphs of different sets of data could be used to try to analyze this problem.  Compare that with a simple problem such as ‘What is the temperature?’ which can be answered with a single number. Visual Analytics

 

Visual Analytics has a number of important requirements that need to be considered, some of which you may not typically consider in traditional web applications or mobile apps:

 

  • Data: Data is all important and is the foundation on which Visual Analytics are built. How much, what type and how clean is your data?
  • Tasks: What is the overall task that your users are trying to accomplish and what functionality will help they achieve it?
  • Visualizations: What visualizations are needed for each task and how can they be designed to best support sensemaking—the ability discover facts, relationships, and abnormalities in the data?
  • Analytics support: What will users do themselves, what guidance can you provide and what parts of the task can be automated?
  • Hardware: What displays can be supported? What resolution are your displays?  How many displays should be used?

 

This post just scratches the surface of Visual Analytics.  The field itself is still relatively new and still in active development.  If you are interesting in testing your Visual Analytics skills, check out the Visual Analytics Science and Technology (VAST) Challenge. It’s an annual contest with the goal of advancing the field of visual analytics through competition.

If you want to learn more about how we at Blue Water can help your organization make sense if its data through Visual Analytics, contact us here.

 

[1] Thomas, J. J., & Cook, K.A. (2006). A visual analytics agenda. Computer Graphics and Applications, IEEE, 26(1), 10-13.

[2] Rittel, Horst W.J. and Webber, Melvin M. “Dilemmas in a General Theory of Planning”. Policy Sciences 4 (1973), 155-169.