Category Archives: Visualization


Have you heard about “Makeover Monday”?

Every Sunday Andy Cotgreave and Andy Kriebel present a data set and a visualization that has been published somewhere in the world. The data sets range from Peaches to Police Violence and anything and everything in between. The point of it all is to present the data in a new way; keep what works and change what doesn’t. The project has run since January 2016, and to date over 400 Tableau developers/fans/enthusiasts have submitted their takes on the data.

I only recently started participating despite watching it with great envy all year. Why did I start? A couple of reasons.

  1. The challenge – There are rules to the @MakeoverMonday project. The hardest one from me is the time box. Limiting myself to one hour is difficult because I like to explore data, and figure out what’s going to work best. This process can take a while depending on the data set, and considering I have zero familiarity with the data sets each week made it tougher.
  2. The learning – I’m trying to try new techniques and break out of “the easy and obvious” when and where I can with these challenges; it will only make me better
  3. Beauty in Simplicity – With only an hour to use, I can’t spend a lot of time on complexity, color palettes and the like. I have to do what works and disregard what doesn’t; Make it impactful and make it tell the story.

I’m posting my #MakeoverMonday offerings over at if you are interested. I’ve done the last two weeks, and I went back and found a couple of data sets to “catch up” on for practice. By posting them I’m hoping to see the evolution of my efforts, and if I learn anything cool along the way, I’ll be sure and add it on the blog post.

You can follow the #MakoverMonday action on Twitter or at the main website.





Any Colo(u)r You Like…

After Matt Francis’ “50 Shades of Data” at #Data15 I really got to thinking about color, it’s usage, and the planning of pallets. So I thought that I would share some of the tools that I use for testing, planning and plotting color for use in Tableau.

palettonOne of my absolute favorite tools for selecting color pallets is Paletton. Paletton allows the user to start with a known base HEX value, or pick using the color wheel. With a click of an icon you can switch from Monochromatic, to Adjacent Colors, to a Triad, to a Tetrad. You also have the option to “Freestyle”.Once you have your base pallet, you can use their presets to run from light pastels to deep colors, as well as grey-tint pallets. You can then apply your pallet to a mocked up website, artwork, or animated images to see how they look “live”. You also have the option to run visual simulations for 13 vision deficiencies (color blindness). Finally, you can export your color pallet and insert it in to a custom color pallet within Tableau with very little effort.

ColorBrewerAnother great tool, especially for maps pallets if ColorBrewer 2.0. ColorBrewer 2.0 applies pallets to a map from their pre-defined pallets. The have predefined pallets for single and multi-hue sequential use, divergent use and qualitative use. You can use the tool to take in to account color blindness, print friendly and photocopier safe color combinations. You can include and exclude roads (and adjust the colors of the roads), turn on and off the city marks and well as the boarders. This allows you to get a strong sense of how your map will look once the pallet is used within Tableau

Now once you have your colors selected, there is a super easy way to build you custom XML file. If you save the images, or take a screenshot, you can use it in Interwork’s Color Tool (one of their awesome Power Tools). Registration is free, and the tool is very easy to use. Below you can see I took a screen grab from Paletton and uploaded it to the Color Tool, and it instantly generates an XML file. The tool does take some liberties in adding additional pallets, but they are easily edited or removed.


I hope you find there resources useful in your color planning. Do you use other tools? Please share in the comments!

“Color Rush” and the NFL


If you watched the Bills/Jets game on November 12th, you probably had a myriad of thoughts when first you turned on the game. In my house it was something to the effect of “even the NFL couldn’t wait to decorate for Christmas.”

My split-second later thought was “Wow, being colorblind would make this game really hard to watch.” 

Turns out, I was right.


I’ve recently started a personal research project on color usage, design, and messages (which I will eventually share here). This example illustrates in a real-world setting the importance of color, and it’s usage. I’m sure Nike thought that those uniforms looked cool and the entire internet/social media universe would be talking about them all night and the next day… and they are, but the topic is different than expected.

Ever since I had a color-blind (albeit unknown to me) CFO ask me for a scorecard and I delivered a standard R/Y/G scorecard I have been hypersensitive to color usage for data visualization. Who would have thought it would have leaked in to a nationally televised NFL game?

The Importance of Good Visualizations

As children we go to the eye doctor regularly to make sure our vision is okay and that we can see clearly. As I got older my vision changed and I needed to get glasses to improve my vision. My prescription continues to change as I age as I’m sure many of yours does the same. People take their vision seriously, because if you are unable to see clearly you run the risk of bumping into things, or maybe something worse like getting in a car accident because something you would have normally been able to see if your vision was clear.

These same principles, although not always followed, hold true in data visualization. Unfortunately there is no Business Intelligence Lasik that can be performed, so you have to work at perfecting your “data vision”. If you need a good jumping off point for perfecting your data vision let me recommend Stephen Few and his books as they are a valuable resource in learning how to visualize data correctly and effectively.  In my eyes, Stephen is an evangelist for the simplification data presentation in order to magnify its importance and multiply its effectiveness.

When I say “dashboard” what do you immediately think of? If you said your car, give yourself a gold star. In your car the dashboard tells you a very quick story of what is going on in your car while you drive.

 How fast are you going?

How much fuel do you have?

How hot is the engine?

Any other issues you should be aware of?

If your seat belt light is on, you know to click in your seat belt. If your oil pressure light is on, you know you have a lubrication issue. If your open door indicator is on, you know not only to shut the door, but sometimes you know exactly which door needs to be shut.

Your corporate dashboards should work the same way. In a minute or less the business user should be able to see their key performance indicators and see if there are any issues that need attention. Whether the monthly expenses are out of line with the budget or the manufacturing of widgets is off pace, they should be able to view and react to correct the problem. If the users are staring at the screen for 5 minutes trying to decipher what the dashboard is trying to tell them, then you have failed as a developer of that dashboard. Can you imagine having to take your eyes off the road for 5 minutes to figure out what was going on with your car? How about even a minute? Scary.

So how do you reach visualization nirvana? There are two questions I ask myself when creating visualizations and dashboards.

1.) Are my visualizations appropriate for the data that is being presented?

It feels like you can present data in thousands of different ways, but there are 4 presentation categories in which everything will fall. The designer must understand what each category entails and select an appropriate visualization technique.

This first category is a simple comparison. There are two types of comparisons that can be performed, one that shows data over time and another that shows data among common items. If you are showing data over time, are your time periods linear, like a year to date sales report, or are the cyclical, like a rolling 24 months of data? If you are comparing common items, how many are you comparing? These details will change what type of visualization you can use to show your data effectively.

The next category in regard to ease of visualization would be the relationship. The relationship is generally displayed as a “scatter” chart if you are showing the relationship of 2 variables in regards to each other. If you have three variables, it then becomes a “bubble” chart with the third variable showing the relative size of the bubble.

The third category is distribution, which shows 1 to 3 variables with the ability to show a single variable over a handful of data points or many data points. Single variable distributions will look something like a bell curve for those of you who remember statistics. Visualizations with 2 or 3 variables can be effectively shown in a scatter or bubble chart.

The last, and probably the most complex category, would be composition. Two sub-categories for composition are static compositions and compositions that change over time. If you are thinking about composition, you are looking at “stacked” visualizations like stacked bar/column charts or stacked area charts. The important things to consider here are which types of differences matter; relative or both absolute and relative. If only relative changes matter, you will show the stack as 100% and the data breaking up that 100%.  If absolute and relative changes matter, then a “traditional” stacked chart can be used.

2.)    Is my dashboard simple enough to be processed instantaneously by the user?

How quickly can I tell a story to the user, and is that story actionable… or can they drill into the data to see and address the anomalies or trouble areas? Even though I’ve chosen the right way to display the data, am I doing anything with the scale or colors to make it misleading? Am I using color deliberately to highlight things that need attention? Am I showing too much data or too many charts on the screen causing “Analysis Paralysis”? Have I steered clear of using 3D charts of any kind?

Simplicity is the key; you have to keep it simple so that the message is clear. If your dashboards are cluttered with gizmos, gadgets, 3D charts and flashing lights you are only going to distract the user from getting what they need, which is the information itself. If you present your visualization to a daily user of the presented data and they need an explanation of what the charts are trying to convey, then you have failed.

It’s easy to build a chart but it’s an art to present data in a usable format. If you aren’t thinking about the data you are presenting, then you are just slapping the data around.

So why do BI Software vendors have all these charts, gauges and widgets if they don’t display the data correctly?

Easy, sex sells and those really slick looking visualizations are sexy to the C-Level executives and to managers who are trying to please those executives. What they fail to realize is that while those visualizations are sexy, they are also useless in 99% of the cases in which you need to visualize data. The developer then has to use these sexy visualizations, because that is what was sold to the management team and the cycle continues. The problem is only getting worse as each software vendor tries to out do the next, which has never been more evident then with the introduction of “exploding 3D pie charts” that showed up on the latest SAP BusinessObjects release. I wish that BI software vendors would adopt the “Visualizations are a tool, it’s the data that is powerful” motto, and we could move on to more useful visualizations.


 3D Exploding Pie Chart? Why?

It might seem like I’m piling on in singling out this example, but it’s by far one of the more ridiculous presentations of data that I have seen in recent memory. Tools will come and go and software vendors will continue to add the latest and greatest ways they think data should be displayed. However, the responsibility of effective data presentation rests squarely on the shoulders of the developers who are given the tools so we must resist the urge to use the latest gadgets and gizmos to present data just because they are new and the executives thought they were cool in the sales pitch. We must be vigilant in our efforts to explain and defend our choices for data visualization because ultimately it will be the developer, not the software company, that will be seen as a failure if the CEO’s “vision” is skewed because the data glasses he or she were given were the wrong prescription.