Analyzing at the Speed of Color

One of our goals when designing visualizations should be to help accelerate the analysis process for the analyst. There are many ways to do this, one of which includes being conscious about color resonance…or how colors might resonate with our audience.

  

So how can color choice speed up analysis? Let’s look at a few examples.

In this first example, rank Sprite, Coke and Sunkist in order by sales:

Obviously it can be done, but how efficiently? I think most would agree that this next graph, in which we’ve changed only the color, requires much less time and effort to analyze:

This is because the colors in the second chart in most cases will resonate with the audience. “Thanks” to to heavy marketing of these brands, we associate red with Coke, Green with Sprite and Orange with Sunkist. (Granted, labeling the bars would have helped quite a bit as well)

We could provide seemingly endless examples, but I’ll leave you with this last one that came up when I was at Cisco. In the 2 charts below, I challenge you to determine which is a larger, the ‘% of blue badge’ or ‘% of red badge’ employees?

 

      

The second one should be much easier to analyze. Now this may seem silly and petty, but keep in mind that dashboards often have multiple charts so the time lost can easily multiply.

(although many, myself included, might argue it’s still a pie so it still “sucks”…at least it’s binary)

Color can be a great mark type for categorizing your data. Just keep in mind that all color should be used with purpose. So when you have a chance to use colors that will resonate with your audience, consider doing so.

Cheers! 

Kevin A. Taylor

Dueling Views on Dual Axis Charts

Maybe it’s the recent birth of my twin girls that’s got me thinking in 2’s. Whatever it is, I can’t seem to get my head off the effectiveness of dual-axis charts.

For a long time, I used dual axes and thought nothing of it. After all, they enable us to show 2 measures on the same graph and we’re seemingly always looking for more real estate to work with.

However, I’ve found that there are fewer situations that truly benefit from leveraging this technique than I might have originally believed. Now I’m not gonna  go to an extreme here and say “Never Use Dual Axes”, I’ll leave the absolutism to the hard asses in our field.

While a case can be made for (although I’m not completely sold) dual axes when you have want to compare 2 measures of different units (i.e. Sales $ and Units Sold), I would suggest that we savoid using 2 scales for measures of the same unit (i.e. Sales $ and Shipping Cost $).

I’ll use a simple plot chart to illustrate the potential shortcoming here. Let’s say we want to show Sales $ and Shipping Cost $ for each of our Customer Segments. It might look something like this:

The issue with the chart above is the Sales $ are exponentially larger than the Shipping Costs $ leaving the latter in a state where the differences can’t be visualized. A common tendency here would be to create 2 axes, one for Sales and a 2^nd for Shipping Costs as shown below:

So we’ve resolved our problem of not being able to see the pattern for Shipping Cost $. However, in doing so we’ve created an illusion as our minds will instinctively attempt to compare the magnitude of difference.

The best solution here might be to use a separate chart for each measure…i.e. Small Multiples. Or, if your data is granular enough, a scatter plot might be more revealing depending on the question you seek to answer. Just a couple of alternatives, happy to hear yours

Cheers,

  

Kevin Taylor

10 Easy Ways to Improve Your Tableau Workbook Performance

I don’t typically write “tool-specific” blog posts. However, tomorrow I’m presenting at a local Tableau User Group Meeting so I figured I’d kill 2 birds with 1 stone. If you are not a Tableau user, hopefully you can find some value in the concepts.

Part of my presentation pertains to optimizing your Tableau workbooks. The following 10 suggestions are some of my favorites for reducing workbook size and enhancing the overall performance. Most of these take only a few seconds to complete and will pay huge dividends.

Every little bit counts!

Please feel free to add your own suggestions in the comments! I know there are many more Jedi tricks. (& if you know of a way to do both #4 AND #5 on the same workbook…do tell!)

1. Use Extracts when Possible
• Tableau’s Columnar Data Engine is the way to go if you don’t require real-time data
2. Optimize Extracts
• Adds your calculated fields to your extract instead of calculating on your desktop
3. Filter Data at Data Source Level
• If you have 10 years of data and you’re only reporting the last 5, filter out what you don’t need before you extract.
4. Aggregate for Visible Dimensions (& “Roll Dates To”)
• Aggregates your data based on your visualizations (i.e. you have data to Engineer level but we only need to report at Director Level)
5. When using dates, specify how far down date hierarchy to view.
• No need to aggregate to the minute or second if you need to display at a weekly view
6. Incremental Extracts
• Appends only new data rather than regenerating all data
7. Hide All Unused Fields
• Best used when ready for production
8. Use Actions instead of quick filters
• Actions Filter the Visualization rather querying your data source
9. If you must use filters, use context filters
• Context Filters create a temp table with the results of your filter so that all subsequent filters query only the reduced result set
10. String Calculations perform worse than Numeric Calculations.
• Parameters are a great place to leverage this advice
11. Do not attempt to Boil the Ocean
• Use workbook case specific datasets rather than 1-size-fits-all solutions, this is not like building a Universe in BO if you’ve worked with that tool.

Cheers,

Kevin Taylor

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 

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

Can Data Visualization Save Lives?

If you’re reading this blog, you’ve probably already heard plenty about the ever expanding presence of data visualization and infographics. Not just on dashboards and in board rooms, but out in the real world, on billboards, on fast food cups, virtually everywhere.

Beyond helping to bring some clarity around KPIs and Corporate Metrics, data, and specifically data visualization, is being leveraged in every facet of life. Things like “data for good” where visualization is being used to help resolve world hunger.

But what about in our everyday lives…at home? Can visualizing data make a difference?

I’m sure we could come up with an extensive list of examples, but one I want to talk about today is weather forecasting, especially as it relates to storm tracking.

It seems like just yesterday that when it came to reporting tornadoes, the news was always reactionary. You only knew a tornado was in your area if one had been reported from the ground. The obvious downside to this was there was no warning system. People were often blindsided. No longer is this the case.

Take a look at the following radar map:

This one shows a fairly strong storm system with a pattern in the bottom left (“The Hook”) that is often an indicator that a funnel cloud has formed,  or could be forming. Although these pictures are older, the same pattern was shown recently as storms passed through North Carolina. When this pattern is spotted, our local weatherman, Greg Fischel, will drill down into the following radar visualization that shows wind direction.

Here, the Red shows wind moving away from the radar satellite and the green shows wind moving towards the satellite. As the two wrap around each other, it’s a tell-tale sign that a funnel cloud has formed. Greg will typically zoom in on the edges to get the wind speeds and then provide some annotation (arrows) as he has done here to help the casual viewer understand exactly what is going on.

Having this data allows the local news to broadcast warnings to individual subdivisions, providing predictions of when a tornado is likely to hit your precise location with accuracy to the minute.

So if anyone asks you if data visualization has ever saved lives, the answer should be YES.

This is just one example of the power of data visualization. I’d love to hear other examples that you have come across.

Cheers,

Kevin Taylor

"Age Stickers" - Data Visualization Should be Unbiased

What I’m about to write about should be considered by confession of geekness. I’m perfectly fine with that. The sad part is my wife now knows just how big of a geek I am too.

In the “real world” it’s often easy to find examples of poor data visualization design. It’s a bit more rare to find a really good example.

Sitting on the couch one weekend, watching TV with my wife, I saw a Prudential Retirement commercial. (yes a commercial, they still exist in the DVR / NetFlix era). Essentially, people are asked to place a sticker on a chart to mark the age of the oldest person they’ve ever known. By the end of the commercial you are made to believe that people are living longer so you should invest more in your retirement.

Here’s a link to the 1-min commercial:  http://www.ispot.tv/ad/7IhP/prudential-age-stickers

I literally got up and shouted “I LOVE IT” and my wife looked at me like I had 6 heads. Oddly, I was going to write about how good of an example it was until I sat down to write. When I did, I started to break down the visual and the story it was telling and realized how biased it actually is.

What if the study had asked the same people if they knew someone that has passed away between the ages of 50 and 65 and plotted those stickers? Would you still want to increase your retirement investment if it were likely you wouldn’t reach retirement age?

The fact is, you can make a chart tell just about any story you want by using partial data. In order to represent the data accurately, you should include all the data.

In this case, a more responsible way to visualize this data might have been to show average (or median) age of death(based on all death data) over time. If the line is trending up, you have your unbiased visual to market. If it’s trending down, don’t twist the data to tell otherwise. Although this is marketing and honesty doesn't necessarily sell investment packages.

Cheers,

Kevin Taylor