December 22, 2014

Red & Green Color Scales? Maybe for Christmas

  


Tis the season!

I’m as guilty as the next guy. When I first started out building data visualizations and dashboards I was quick to select a Red-Green color scale to differentiate between good and bad. In my defense, in the United States green is often associated with good results, red with bad results. Additionally, it didn’t help that many of the available tools defaulted to the red-green scale as the vendors weren’t focused on sound data visualization practices.

So what’s the drawback of using the red-green color scale?

First, color meaning varies greatly between cultures. i.e. Red typically  symbolizes “bad” in western cultures…in China red symbolizes good luck, happiness and long life…all positives. As for green, western cultures often associate green with “good”, whereas North African and South Pacific cultures associate green with corruption and danger respectively.

For more information on color meaning by culture, try a site like ColorMatters. There’s a lot of resources that touch on this topic. We encourage you to leave comments on the post if you have other examples to share.

So, beyond the varying meanings from culture to culture, why else would the red-green color scale be less than optimal?

If you’ve studied visual perception and design, you’ve probably come across the subject of color blindness. Two common forms of color blindness, Deuteranope (aka  Green blindness) and Protanope (aka Red blindness), will essentially render the red-green color scale useless for those affected by either.  Below are examples of what these users would see:

Normal Vision:



Deuteranope (Green blindness):



Protanope (Red blindness):


Our suggestion: use an orange-blue scale. While the colors will still appear different to the color blind, the hues will still provide enough differentiation for analysis. It may take some explanation up front, but consistency on this will lead to quicker processing.
 


Cheers,



Kevin A. Taylor 

December 5, 2014

Data Sushi: Shimmy Shimmy Ya



In the infamous words of the late O.D.B., (I will resort only to the abbreviation since this is an “all-ages” blog) in his collaboration with Mariah Carey…

“Ooh Baby I Like It Raw”


Hold on…We’re talking about data.

Raw Data vs Cooked Data

All too often we get requests to visualize data that has exactly 1 layer of granularity…meaning we can’t drill into or interact with the data. We’re left with only a static visualization. That might have been acceptable before, when we were limited to print media and power point slide decks (even PowerPoint can include interactivity these days).

But today, in the digital age, we have the ability to explore and interact with data on the fly…changing the conversation both in media as well as the corporate meeting room. But to do this, we MUST provide the raw data! It’s one thing to generate new questions; it’s another to be able to answer them.

Last February I was blessed with the opportunity to attend Tapestry 2014 in Annapolis, MD. One of the presentations I enjoyed the most was “Jock Dreams of Data Sushi,” delivered by visualization expert Jock Mackinlay.

 Jock Dreams of Data Sushi



Data Sushi, as Jock describes it, is “a visualization that is beautiful on the outside, and contains RAW data on the inside”.

Below are the reasons Jock gives for why we should demand raw data when possible followed by the excuses we hear for not doing so:

Reasons:
  1. Increases Dwell Time
  2. Validate the Author / Designer / Developer
  3. Encourages Conversation
Excuses:
  1. People Have Cooked Data
  2. Too Valuable to Share the Data
  3. Hard to Share Raw Data
I encourage to you to watch Jock’s 15 minute presentation as well as to promote raw data behind your visualizations.

Cheers,


Kevin A. Taylor