Dashboard Layout Re-Thought

Having studied data visualization and dashboard design for many years, I had been programmed to regard screen space in a very particular way.

You may have seen something similar to this picture before:

According to this, the upper left part of your screen (or dashboard) should always contain your most important data, or what you hope to emphasize.

Others describe the layout in terms of following the shape of the letter “Z”. The upper left is the most important, followed by the upper right, then the lower left and then the lower right accordingly.

With this in mind, I have always pushed my filters out to the right of my visualizations. This made sense thinking strictly about the approaches above.

Then someone challenged me on this, saying “Go out on the web and find me a site that has the filtering or navigation on the right side of the screen.”

Although there are plenty of exceptions, the majority of websites have these controls on the left side of the screen. Almost as if to say, “Here, Right Here, This is where you tell me what you want to see!!!”

Try it for yourself. Facebook.com, CNN.com, Gmail.com and even most internal corporate sites have the controls placed strategically on the left side of the screen, consuming at least a portion of the “Emphasized” quadrant.

So while approaches like the ones I had read over and over again are still very valuable in terms of where to place each visualization, User Experience should never be overlooked. For this reason, I have started designing most of my dashboards with the filters on the left.

Cheers, 

Kevin Taylor

Never Say Never! Except When...

Sorry “Beiber Nation! This is not a blog about a pop song. Your Welcome to the rest of the world.

A mentor of mine once warned me against using absolute terms like never and always.  In the world of visualization there are a lot of rules and principles, but rarely are there laws that are written in stone, never to be broken…Although some of the purists might wish to argue this point.

While there are some rules that really should be avoided in almost any situation, there are still typically exceptions. The main 2 arguments I tend to hear are :

1. The customer asked for it.
2. My boss asked for it.

Both of these are very valid arguments in the real world. For the former, I’d suggest designing as they have requested but also show them a better alternative. For the latter, good luck!

With that said, I want to raise one design rule/principle/guideline that should never, ever, ever, ever be broken…ever. Do NOT sort your data alphabetically when your intention is to show a trend over time.

The issue here may be obvious, but let’s look at how this can blind one’s analysis:

Looking at the chart below it’s quite difficult to determine what direction the data is trending…at best, it will take some time.

Not so hard in the chart below is it?

So if your boss demands the 1st chart, you might consider a different role.

I saw this recently in a dashboard and I do not believe it was by design…but it was there. So the lesson here is to check your work and when you’re done checking, check it again and they have someone else check it. Otherwise your credibility could be challenged.

In one of my 1^st blogs titled “Do You See What I See” , I suggest that as designers we should have others validate our visualizations. This situation might have been avoided had the designer heeded this advice.

I’d like to know if you all have any “Never-Break” rules? Please leave a comment.

Cheers,

Kevin Taylor

If Data is the New Bacon, How Can We Prepare?

It’s no secret that more and more and more data is becoming available. When I got started working with data in 1999, Terabyte was very rarely heard word. Today we’re speaking an entirely different language: Exabytes and Zettabytes and, before too long, Yottabytes (May the force be with you).

What there’s not necessarily a lot of…people with the skills and “know-how” manage the new landscape or to transform the data from data to information to insight.

"There is a shortage of big data experts," said Michael Rappa, director of advanced analytics and distinguished professor at North Carolina State University. "I don't see the gap narrowing. Universities aren't producing enough. We have 80 grads per year" with master's degrees in analytics. "We could be producing 800 per year and still not meet demand. With each class, the demand goes up."

The Advanced Analytics program referred to above is gaining a lot of attention from the corporate world and thus, admissions has become extremely competitive. However, there are alternatives.

One such alternative is the 1^st Associates Degree Program in Business Analytics. Offered at Wake Technical Community College, the program is now in its 2nd class which includes over 70 students, most of which already possess Bachelor and Master degrees in various fields.

In addition to the degree program, the school also offers 2 different  certifications (Business  Intelligence Certificate & Business Analyst Certificate). To meet the demands of the modern world, classes are offered both in-person as well as online.

If you or one of your colleagues is interested in the program, please don’t hesitate to ask questions and be sure to check out the program website below:

 http://www.waketech.edu/programs-courses/credit/business-analytics/program-information

Cheers, 

Kevin A. Taylor

Pies for Binary Comparisons? Not so fast Skippy

If you study data visualization enough, you’re probably aware there is a large constituent of folks that believe pie charts suck. However, most have at least some level of acceptance for their use. ***although as I found out on Twitter last week, plenty of hardliners do exist***

Some experts say a pie chart should never exceed 5 slices, some say 3 and others say no more than 2. Personally, I’d say no more than 2…with a possible exception for 3 slices if you must.

Yet even if you only have only 2 slices, pie charts can still have their shortcomings.

While speaking with an industry expert some time ago, I shared a dashboard I had built. I had mistakenly thought hat using a series of pie charts would be a good choice to show the ratio between Red Badge and Blue Badge workers across 4 different regions. Needless to say, my colleague blasted this.

Here’s the reasoning. And it’s not that the pie charts don’t show the ratio well for each region…it’s the extra bit of insight that the pie charts can’t deliver…at least not very well.

Here’s what I originally had (I have omitted all labeling and axes as they’re not pertinent to this discussion & Red = Red Badge and Blue = Blue Badge in all visualizations):

While it’s easy to see the split of Red to Blue for any given region, it’s impossible to compare the regions. OK, may you be able look back and forth to see the slice..but which has more?

We could use size to show that right?

Sort of. But it’s been proven that we suck at comparing 2-D surfaces & Angles. What we do excel at is comparing length.

Using a bar graph, it’s easy to see  not only the ratio of red to blue, but also the total number for each region and how they compare to one another. And it takes up considerably less space! Very glad that someone pointed this out to me and hope you’ll find this valuable too!

I wrote about this one as I know that stacked bars have their shortcomings as well and I'm really hoping you will be vocal about what works even better!

Cheers, 

Kevin Taylor

Sometimes You Just Gotta Give 'Em What They Want

I’m probably guilty of this as much, if not more than anyone else:

Not letting go of the Principles of Data Visualization.

I say this because I have studied these principles inside and out. I have a tendency to read the words of Stephen Few, Edward Tufte and several other experts as though they are written in stone.

This is something I have to consciously recognize and then quickly get over. Even Stephen Few will tell you, the best dashboard is the one that is used.

The fact of the matter is, if we are building a dashboard, we are probably building it for someone else. That someone probably has less knowledge of data viz design and often has no desire to understand data viz design. But in the end, we have to deliver something that our client will adopt, use, understand and ultimately take action on. If we don’t accomplish these things, our dashboard will not be a success, no matter how good of job we did applying the principles!

So, if a client or manager or director wants to see all good/bad data scenarios to be marked with green/yellow/red, then that’s what we deliver. Do we know that statistically, approximately 10% of the population may not be able to decipher between green and red? Yes! Then why do we do it? Because it’s not a show-stopper and if it helps to increase adoption, that’s a win.

With this said, you still don’t want to create “3-D, Spinning, Flaming Pie Charts”. There are certain things like chart selection that you will want to save your battles for.

And when you do get asked to build that “3-D, Spinning, Flaming Pie Chart”, if your tool will actually build it, go for it. But be sure to provide a better solution right beside it. Explain the difference and hope that your client SEES the better way.

I chose to write about this in response to a healthy debate I had based on the following tweet I posted:

While I stand by my comment and believe that there are much better ways to display data than through a donut chart...I am developing a dashboard now for an executive that will include a series of donut charts with a % Value in the center.

#BendButDon’tBreak

Cheers! 

Kevin Taylor

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