To Graph Or Not To Graph

Recently, I had an interesting conversation with an associate regarding graphs. My colleague had worked with someone who held the opinion that graphs were worthless, since anything you might decide based on a graph should really be decided based on a statistic. My initial response was to reject this idea. I have used graphs a number of times in my work, and believed them to be useful, although I readily admit that in many cases, a simple numeric measure or test could have been substituted, and may have added precision to the analysis. Data visualization and related technologies are all the rage at the moment, but I wonder (despite having a nerd's appetite for computer eye-candy) whether data mining should perhaps be moving away from these human-centric tools.

Thoughts?

Data Visualization: the good, the bad, and the complex

I have found that data visualization for the purposes of explaining results is often done poorly. I am not a fan of the pie chart, for example, and am nearly always against the use of 3-D charts when shown on paper or a computer screen (where it appears as a 2-D entity anyway). With that said, that doesn't mean that charts and graphs need to be boring. If you would like to see some interesting examples of obtuse charts and figures, go Stephen Few's web site to look at the examples--they are very interesting.

I like in particular this one, which also contains a good example of humility on the part of the chart designer, along with their improvement on the original.

However, even well-designed charts are not always winners if they don't communicate the ideas effectively to the intended audience. One of my favorite charts in my work was for a health club is on my web site, and is reproduced here:



The question here was this: based on survey given to members of the clubs, which characteristics expressed in the survey were most related to the members with the highest value? I have always liked it because it has a combination of simplicity (it is easy to see the balls and understand that higher is better for each of them, showing which characteristics for the club are better than the peer average), yet it is rich with information. There are at least four dimensions of information (arguably six). The figure of merit for judging 'good' is a combination of questions on the club survey related to overall satisfaction, likelihood to recommend the club to a friend, and the individual's interest in renewing members--this was called the 'Index of Excellence'

• seven most significant survey questions are plotted in order right to left (rightmost is the most important). Signficance was determine by a combination of factor analysis and linear regression models
  
• the relative performance of each club compared to the others in its peer group is shown by the y-axis, with the average of clubs.
  
• the relative difference between results from the year 2003 and 2002 are shown in two ways: first with the color of the ball (green for better, yellow for about the same, and red for worse), and also by comparing the big ball to the dot in the same relative position (up and down) in the importance axis.
  
• finally, the size of the ball indicated the relative importance of the survey question for that club--bigger meant more important.

Each bullet was a dimension represented in the plot, but note that bullets 2 and 3 were relative values and really represent two dimensions. Regardless of how many dimensions you would count, the chart I think is visually appealing and information rich. One could simplify it by removing the small dots, but that's about all I would do to it. My web site also has this picture there, but it was recolored to fit the color scheme of the web site, and I think it loses some of its visual intuitive feel as a result.

However, much to my dismay, the end customer found it too complex, and we (Seer Analytics, LLC and I) created another rule-based solution that turned out to be more appealing.

Opinions on the graphic are appeciated as well--maybe Seer and I just missed something here :) But at this point it is all academic anyway since the time for modifying this solution has long passed.