Mar 112015
 

Overview

Note: I based my Tableau Conference 2015 presentation on this blog post. You can download a PDF of the presentation and the Tableau packaged workbook.  Click here to see a video of the presentation.

Earlier this year one of my clients was updating a collection of survey data dashboards and they wanted to revisit the way they presented demographic data.  They thought that the collection of bar charts comprising the demographics dashboard was boring and wanted to replace them with something that was a bit more visually arresting.  In particular they wanted to take something that looked like this this…

Figure 1 -- a "boring" collection of bar charts.

Figure 1 — a “boring” collection of bar charts.

… and replace it with something that looks like this:

Figure 2 -- A "flashy" demographics dashboard

Figure 2 — A “flashy” demographics dashboard

When asked why they wanted something “flashier” they indicated a desire to draw the viewer into the dashboard and they thought a dashboard with more than just bar charts would do the trick.

I wondered “why stop there?”  Why not add pictures of kittens and puppies?

Figure 2a -- the Too Cute dashboard.

Figure 2a — the Too Cute dashboard.

The real issue here is that the underlying data just isn’t interesting and adding sexy visual elements will do nothing to make the data more interesting.  There’s only one way I know to make this kind of data “interesting”.

Make it personal.

Tapestry and Chad Skelton

I recently attended the 2015 Tapestry Conference where Chad Skelton of the Vancouver Sun presented a great session making the case that people are ravenous for data about themselves.

I was particularly taken with an interactive dashboard Chad created that allows Canadians to see how much older / younger they were than other Canadians.

I decided I would look at United States census data and build a similar dashboard.

US Census Data without Personalization

Here’s a histogram showing the relationship between age and US population.

Figure 3 -- A histogram showing the relationship between age and US population.

Figure 3 — A histogram showing the relationship between age and US population.

I have to admit this doesn’t do much for me although I do find the long downward slope from around the age of 50 somewhat interesting (but I am a bit of a data geek).

Contrast this general purpose graphic with the personalized dashboard shown below.

Did you try it?  Are you over 38 years old?  If “yes,” were you a bit depressed?

I certainly was.

While I don’t mean to depress anyone I do want to underscore how much more interesting the data is when the data is about YOU.

Make the Demographics Dashboard Interesting – Make it Personal

With the goal of personalization in mind let’s see how we can make the dashboard in Figure 1 more interesting.

Let’s start by gathering some information about the person viewing the dashboard; that is, let’s present some parameters from which the viewer can apply personalized settings:

Figure 4 -- Get your user to tell you something about himself / herself.

Figure 4 — Get your user to tell you something about himself / herself.

Now we can take these parameter settings and highlight them in the dashboard.

Figure 5 -- A "personalized" demographic dashboard.

Figure 5 — A “personalized” demographic dashboard.

We can then go one step further and invite the viewer to select the colored bars to see exactly how many people that took the survey have the same demographic background as the person interacting with the dashboard.

6_boring

Figure 6 — There are 65 people who fall into the same demographic pool as the person viewing the dashboard.

Conclusion

I’ve become a big advocate for adding personalization to dashboards and a number of my clients have started to adopt the approach.  I’ve seen some very good results at Bersin by Deloitte where Bersin is leveraging their proprietary survey data by allowing individual organizations to benchmark their numbers against similar organizations.

Note: A few months ago Joe Mako sent me a link to a Stephen Few blog post.  In researching this topic I revisited the post and see that Chad Skelton was in fact featured in Few’s essay . It seems that Skelton did not just “happen” upon the idea of personalization but was grappling, like so many of us, with ways to entice people to engage with visualizations.

For the record, I think personalized bar charts beat packed bubbles any day of the week.

Jan 202015
 

 “With great power comes great responsibility”

— Voltaire

— Benjamin Parker (Uncle Ben from Spiderman)

Overview

Recently both Ryan Sleeper and Andy Kriebel blogged about donut charts in Tableau.

Figure 1 -- Donut chart courtesy of Andy Kriebel

Figure 1 — Donut chart courtesy of Andy Kriebel

While both of them cautioned about where, when, and how best to use them, I fear many people will ignore the warnings and dig into this sugary, analytically-impoverished chart type and start creating stuff like this.

Figure 2 -- Really bad donut chart.  In fact, it’s just a pie chart with a whole in the middle.

Figure 2 — Really bad donut chart. In fact, it’s just a pie chart with a hole in the middle.

Yuk.

And what fuels my fear?  Ryan and Andy do great work, and they write great blogs.  They rightfully have a lot of influence in the Tableau community.

But with great power — and influence —  comes great responsibility and I suspect that some people will see Ryan and Andy’s work, ignore their recommendations, and apply the following bit of “logic”:

Ryan Sleeper is a Tableau Iron Viz champion and really cool — and he makes donut charts.

Andy Kriebel is a Tableau Zen master and really cool — and he, too, makes donut charts.

I want to make cool vizzes and be really cool; therefore, I should make donut charts.

[Insert face palm here]

Interviewer: So, what do you have against donut charts?  Don’t you think they look cool?

Me: My problem is that donut charts don’t tell you very much.

Interviewer: Yes, but they look cool!

Me [yelling]: You know what else looks look cool?  Pictures from the Hubble telescope.  Vintage electric basses.  Three-dimensional pie charts! Should I festoon my dashboards with these images, just because they look cool?

Interviewer: Fine, explain to me why this chart types doesn’t work, but I’d like to see an alternative that isn’t BOR-ING!

Me:  Okay, allow me to do the following:

  • Explain why donut charts don’t tell you much (or not as much as a bar chart)
  • Present a better alternative
  • Show how to have your cake (not your donut) and eat it, too

Why donut charts don’t tell you much

Consider the chart in Figure 1, above.

I always recommend that people ask the following questions when coming up with a visualization:

  • Do I need different colors?
  • Do I need a legend?
  • Do I need measure labels?

Let’s see what happens when we remove the measure labels:

Figure 3 -- donut chart without measure labels.

Figure 3 — donut chart without measure labels.

The chart does pass some of the “can I figure this out test”.  For example, it’s easy for me to see that West is around one quarter of the way to goal and that East is a little more than half way.  Where the chart fails is with comparison among regions.  For example, can you tell how much closer North is to its goal than West?  This comparison is particularly hard to determine as it’s very difficult to gauge how much longer one arc is than another arc.

A better alternative

I think a bar chart with a goal line is easier to grok.  It tells me more and takes up less screen real estate, too.

Figure 4 -- Bar chart with goal line.

Figure 4 — Bar chart with goal line.

There’s an added advantage in that I can easily see both the progress towards a goal and that the goal is $100,000.

Better yet, suppose the goals were different for each region?  Right now they each have a shared goal of $100,000 but suppose the goal for North is $125,000 and the goal for East is $75,000?  With the donut chart, how will you show the actual goal and the progress towards the goal at the same time?

Why is it easy to compare progress across regions using the bar chart?  I’ve discussed this in length here, but the bottom line is that humans are much better at judging the length of bars than they are judging the area of circles or the lengths of arcs.

But does the chart pass the “no measure labels” test?  Have a look.

Figure 5 -- Bar chart without measure labels.

Figure 5 — Bar chart without measure labels.

While I prefer having labels, it’s pretty easy for me to the following:

  • North is more than twice as long as West
  • East is a little more than half way
  • South is more than a third of the way to goal
  • East is about twice as long as West

In other words,  I can draw conclusions more easily from this chart than the donut chart.

Another Example

Consider the chart below that shows the percentage of confirmed judicial nominees that are women, broken down by president.

Figure 6 – Donut Chart showing Female Judicial Nominees (source: Alliance for Justice)

Figure 6 – Donut Chart showing Female Judicial Nominees (source: Alliance for Justice)

There are some good stories in here but they are buried.  Compare this with a bar chart that contrasts the different presidents and underscores the differences between Republicans and Democrats.

Figure 7 -- Bar Chart showing Female Judicial Nominees (source: Alliance for Justice)

Figure 7 — Bar Chart showing Female Judicial Nominees (source: Alliance for Justice)

I think this is a lot clearer.

But it is, well, boring.

Have your cake and eat it, too

I admit that most of my practice has me building stuff that looks more like it would appear in The Economist than in USA Today, but I do understand that you may need to create something that is eye catching.

And I agree that the donut chart is eye catching, but I hate to sacrifice information for the sake of decoration.

Is there a way to get both?

I think there is.  Let’s work on the first example where we were examining progress towards a goal broken down by region.

Want some sugar?  Try a lollipop chart

Figure 8 -- Lollipop chart

Figure 8 — Lollipop chart

Creating a lollipop chart is easy in Tableau. You create a dual axis chart where both measures are identical but you have a different chart type (in this case a bar chart combined with a circle chart).

Figure 9 – Tableau settings for a lollipop chart

Figure 9 – Tableau settings for a lollipop chart

Try some fun shapes

We can also take the lollipop chart and dress it up with a custom shape, like the one shown below.

Figure 10 -- Combination bar and shape chart

Figure 10 — Combination bar and shape chart

While I prefer the lollipops to the runner, I have no problem with the chart shown above because I don’t have to work hard to see both the distance from the goal and to compare among regions.  That is, I did not fight Tableau’s suggested default chart type but instead took it and dressed it up a bit.

Conclusion

NoDonutEven if you are tasked with having to create visualizations for mass public consumption I urge your to use caution before creating a donut chart. I understand that you may need something that is more visually arresting than a simple bar chart, but take that as a challenge: find a way to make something that looks cool but does not sacrifice one bit of analytical clarity.

And if you do create a donut chart, please look carefully at what Ryan and Andy did (and did not do) in fashioning theirs.

Sep 182014
 

Overview

A dashboard from Radio Free Europe / Radio Liberty has received a lot of views since it was published earlier this week and for good reason: There’s a lot of important information packed into a compelling story.

There’s a lot I like about the dashboard but two things that I believe desperately need to be corrected.

Before going any further you can see the dashboard here.

What I Like and Don’t Like

Here’s a re-sized snapshot of the dashboard.

01_Fighters

What I Like:

  • Colors
  • Hovering over a country provides more information about the country.
  • Syria and Iraq are labelled so I can find the focus of the story quickly.
  • The author has presented normalized data in the bottom chart  to show proportion of fighters from a particular country. This is brilliant and important.

What I Do Not Like:

  • There are two different axes at the bottom of each bar chart with very different values. If I don’t look at the axes I would think that Belgium has the same number of fighters per million as Tunisia. This defeats the brilliance of presenting the data in a normalized form.
  • The fighter icon takes away from the gravity of the story as this should not be a frivolous visualization. The visualization should skew more towards The Economist and less towards USA Today (see this post.)
  • The bars are in alphabetical order.

The Makeover

Here’s what I’ve changed.

  • Clicking a country name in one of the visualizations will highlight that country in the other visualization making it easy to find.
  • The axes for the two sets of bar charts at the bottom are consistent.
  • I’ve replaced the fighter icons with labeled bars.
  • You can switch between displaying normalized data (fighters per million) and the number of fighters.
  • The bars are color coded to reflect the same color legend in the top chart.

Click here or the image below to access the interactive dashboard.

02_Makeover

Mar 182014
 

Note: Since writing this post in 2014, I have, in fact, become a fan of sparklines. That said, I continue to see many instances where I think the dashboard author could present data more clearly using a different approach. Make sure to read the comments at the end of the post.

I’ve never been a big fan of sparklines and I’m a bit concerned with how often they are cropping up in dashboards.  While I appreciate that this chart type provides a compact mechanism for showing how a collection of measures wax and wane over time, I believe there are many cases where other chart types will do a better job getting the message across.

Stephen Few’s Dashboard Design Competition

I’ve been reading the second edition of Stephen Few’s Information Dashboard Design and was drawn to a discussion of the design competition Few ran in 2012.

Consider this data snippet from the competition where we see student test performance over time:

Student test results

Student test results

The winning entry, the runner up, and Few’s own solution rely heavily on sparklines to present this and similar data.

My Attempt at Sparklines

I’ll be honest that I have a very difficult time being able to understand any of the sparkline renderings from any of the design entries. Perhaps if I took a stab at myself…?

Consider my attempt below:

Student test results rendered using sparklines

Student test results rendered using sparklines

I ask you if you can see — at a glance — that the best performing students are at the top and the lowest performing students are at the bottom?  Can you see that Regan Petrero (about 60% of the way down the list) received “C”s for his first three assignments, a “B” for the fourth assignment, and a “D” for the fifth assignment?

Granted, I can try to make certain things stand out better by adding banding and not having the axis start at zero, but even with these additions I’m not able to come up with anything that tells as clear a story as what I get with a simple highlight table.

Student Data, Take Two – A Highlight Table

Here’s the same data rendered using a highlight table:

Student test results rendered using a highlight table

Student test results rendered using a highlight table

I can see immediately that Holly Norton is a straight “A” student, that Donald Chase just missed being a straight “A” student, and that Xu Mei has had some wide fluctuations.  The chart is compact, easy-to-read, and I can discern both comparative performance and relative performance with very little effort.

What about Frederick Chandler?

If you look at my sparklines tendering  you will see that there may be an interesting story with respect to Frederick Chandler and the third assignment.  In the sparkline you can see there was a big dip; in the highlight table you can only see that Mr. Chandler received an “F”.

It turns out that Mr. Chandler received a zero on the assignment.  Is it important to show this, versus just showing a failing grade?  I don’t know the answer, but if it is important then we can create a six point color scale, as shown here:

Mr. Chandler’s zero, for all the world to see

Mr. Chandler’s zero, for all the world to see

 

See For Yourself

I present the sparklines and highlight table side-by-side in the dashboard below. Have a look and let me know what you think.  If you have a way to make the sparklines “sing” better by all means please share it.

Please realize that I’m not suggesting that you should never use sparklines; I only ask that you consider whether sparklines are the best way to show what is important about the data before you publish. I very much encourage your to explore other options.

Jan 162014
 

Overview

One of the new features in Tableau 8.1 that Tableau Software is trumpeting quite a bit is one-click Box and Whisker Plot generation.  While I appreciate the new functionality, this chart type doesn’t “sing” to me the as much as jittering does.  Indeed, this “jittering” capability was the BIG discovery for me in 2013.

Let’s see how a box and whisker plot compares with jittering using a simple example.

Note: Interactive dashboards that illustrate jittering techniques may be found at the end of this blog post.  Feel free to download and explore.

Salary and Age Bins – Default

Consider the following pre-Tableau 8.1 salary chart that shows how salaries are distributed across age bins.

1_Salarydistribution_Age

Figure 1 — Default Salary Distribution by Age Bins

 

While we can see that the top salaries are enjoyed by people in their 50s, there’s nothing that gives us concrete percentiles nor shows us where the outliers are.  We also can’t tell that there are in fact thousands of dots in the visualization as so many marks are sitting on top of each other.

Salary and Age Bins – Box and Whisker Plot

To see percentiles and outliers we can use Tableau’s Show Me feature and click the Box-and-Whisker Plot button.

2_SalaryDistrib_BoxWhisker

Figure 2 — Salary Distribution by Age Bins with Box and Whisker Overlay

 

This is definitely an improvement, but I really don’t “feel” the data as I can’t see how the dots are distributed; they are all stacked on top of each other.

Salary and Age Bins – Jitters

Here’s the original chart, but with the marks “jittered” using a modified version of Tableau’s built-in INDEX() function.

3_SalDisJitters

Figure 3 — Salary Distribution by Age Bins with the marks “jittered”

This gives me a much better feel for the data as I can how the thousands of marks cluster.  Of course, I can still superimpose the box plot, as shown here.

4_SalDisJittersBox

Figure 4 — Salary Distribution by Age Bins with the marks “jittered” and box plot overlay

Getting Jitters Using INDEX()

To “jitter” the marks I create a calculated field called “Index” that uses Tableau’s INDEX() function.  I put this on the Columns shelf and compute using ID, as shown here.

5_Index

Figure 5 – First attempt using Tableau’s INDEX() function

It turns out that for this particular example INDEX() by itself works because there is an equal distribution of IDs across each of the age bins.  Consider the example below where we show a distribution of Superstore Sales across different customer segments.

6_superstore

Figure 6 – Shortcomings of using INDEX() by itself.

Notice that the strip of dots within “Corporate” is much wider than the other segments because there were more orders within “Corporate” than there are in the other segments.

The easiest way to fix this is to edit the axis and select “Independent ranges for each row or column” from the Edit Axis dialog box.  While this will work fine we’ll look at a different technique that will allow us to control the degree of jittering.

Using Modulus to Control Jittering

When I first blogged about this technique last year, Alex Kerin of Data Driven suggested a simple and elegant solution to different-sized partitions using Tableau’s Mod function.   For those of you that forgot your high school mathematics, we use a modulus is to determine the remainder when you divide one number by another.  Here’s an example

14 ≡ 30 Mod 8

Translation: 14 is equivalent to 30 Mod 8 because you get the same remainder when you divide 14 by 8 as when you divide 30 by 8 (both remainders are equal to 6).

So, how do we use this capability in our visualization?  We want the same number of dots in each segment, so instead of using INDEX() we will instead use INDEX()%25

This will create 25 “rows” of dots within each segment.

Specifically, when

INDEX()=1, INDEX()%25 will be mapped to 1
INDEX()=2, INDEX()%25 will be mapped to 2


INDEX()=26, INDEX()%25 will be mapped to 1
INDEX()=27, INDEX()%25 will be mapped to 2
etc.

Note that 25 is not a magic number.  For this example anything above 15 will do the trick (and in the demo workbook I have a parameter slider that controls the MOD setting).

Conclusion

Jittering is a very simple technique and it helps overcome the problem of marks being stacked atop each other when plotting a distribution within a dimension.  It only takes up a little more screen real estate and it packs a terrific visual wallop.

 

Oct 312013
 

If I see a visualization that is poorly designed or worse, misleading, I’m going to say something about it. I hope you will do the same.

In March of 2013 Stephen Few published a scathing review of Tableau 8. Few’s thesis was that Tableau had caved to marketing pressure and its new product would encourage users to craft “analytically impoverished” visualizations.

At the time I thought that Few’s screed was unfair (see my blog post), but a recent post from Emily Kund about a company’s internal “Iron Viz” competition made me wonder if perhaps Few was right.

Before I get into what deeply troubles me about the aftermath from the contest I do want to applaud Kund and her colleagues for fostering interest in Tableau and data visualization best practices.  Clearly, I have a fondness for these types of contests and like the excitement they generate about visualization.  I also believe strongly in making interactive visualizations that are fun and inviting.

My problem is that while everybody is rightfully patting Kund on the back for having the contest, nobody in the Tableau data visualization community (and it is an amazing community) has pointed out what is wrong with the dashboard — and there is a lot that is wrong with the dashboard.

Too Much Sugar

Let’s have a look at the winning entry from the Halloween data visualization competition.

DTSS Winning Viz Image

Winning entry

This winning viz epitomizes the type of creation Stephen Few feared that people would construct in his now infamous review as this dashboard sacrifices clarity and accuracy for whimsy. Why have the stacked bubble chart, and why have the pumpkins representing annual spending? Humans are absolutely horrible at comparing areas of circles — why use them here? I also don’t buy the size of the pumpkins at all as the $4.7B pumpkin for 2009 is considerably smaller than the $5.0 billion for 2006.  It looks to me like the author exaggerated the size of the pumpkins.

More importantly, by fighting Tableau’s own default settings the author has hidden the biggest story the data is trying to tell us.

Why Didn’t You Let Tableau Make a Line Chart?

Let’s focus on the pumpkin chart along the left side of the dashboard:

DTSS Winning Viz Image_leftside

Unreliably-sized pumpkin chart

Here we see annual sales by year.  Using the same data, in Tableau if we simply select the two fields and click the Show Me button Tableau will automatically generate the following visualization.

Halloweed_Tableau1

The default chart Tableau creates

Now, tell me you didn’t just think “whoa… what happened in 2009?”

THAT’S the big story.

Have Your Candy and Eat It, Too…

I “get” that the nobody is going to get very excited about the viz Tableau creates by default.  Without something to capture the viewer’s interest he/she may not bother with the viz (see Ben Jones’ excellent posts on this subject.)

So, if we must add some “viz candy” why not start with the line chart and dress it up, like the one below?

Line chart with pumpkins

A “fun” chart. 10 seconds to build the default line chart and five minutes to apply some graphic design.

Are Stacked Bubbles Inherently Bad?

I don’t think the stacked bubbles work in the dashboard.  I have to work too hard to see that “Candy” at $22.37 is slightly larger than “Decorations” at $20.99.  With a bar chart I could see the differences immediately.

That said, there are some good examples where bubbles elicit an emotional response and just fit with the design flow (see this example from Kelly Martin).

I also like having this chart type in my quiver, even if I never use it on a published dashboard.  I welcome anything chart type that will help me better understand the data, even if I never use that chart type in production.

Getting People to Use The Tools Correctly

I still don’t agree with Few — I don’t think Tableau should remove features for fear that people will use them incorrectly.

But I am very concerned that visualizations that are poorly rendered are being presented as examples to emulate.  As a community we need to do our best to prevent this from happening, so if you see something that is poorly designed — or worse, misleading — point out the problem and show the person a better way to get the desired result.

I have tried to do that here.

 

 

 

 

Aug 202013
 

In this installment we’ll look at Utah State University’s publication of student engagement results.  Utah State is one of many collegiate institutions that have participated in NSSE’s national survey of student engagement (see http://nsse.iub.edu/ and http://nsse.iub.edu/html/about.cfm).

Special thanks to Allan Walker for making the underlying data available to me.

Note: I’ve published four sets of questions from the survey as interactive dashboards that you can find at the end of this blog post.

The Good

Utah State University should be lauded for making its survey results available in an interactive format.  This is a great way to foster engagement from students, faculty, administration, and other interested parties.

The Bad and The Ugly

It’s almost impossible to glean anything useful from the published results.

The “Before” Picture

Here’s a screenshot of the analysis of the first set of questions in the survey (see http://usu.edu/aaa/nsse_paged.cfm?pg=1)

Five of the ten questions in the group -- this requires lots of scrolling and makes it impossible to compare results across questions

Five of the ten questions in the group — this requires lots of scrolling and makes it impossible to compare results across questions

Note that there are a total of ten Likert scale questions in this set and they are presented in the same order that they appeared in the survey.

Here are the things I would like to know, but cannot at all glean from the visualizations:

  • Which activities where done most often and which were done least often?
  • Are there any significant differences when you compare results by gender?
  • Are there any significant differences when you compare results by ethnicity?

The “After” Picture

I’ve written extensively on the best ways to visualize Likert Scale data (see http://www.datarevelations.com/likert-scales-the-final-word.html and http://www.datarevelations.com/mostly-monthly-makeover-masies-mobile-pulse-survey.html).

Here’s what happens if we apply this approach to the Utah State University NNSE data.

Divergent stacked bars showing all responses

Divergent stacked bars showing all responses

And if we apply a parameter setting to only show extremes (e.g., “very often/often” vs. “sometimes/never”) the results are even easier to sort and grok.

Divergent stacked bars combining responses

Divergent stacked bars combining responses

This approach also allows us to break the data down by gender and see if there are any questions where there are major differences (and there are major differences).

Comparing results by gender

Comparing results by gender

We can likewise distinguish major differences from Caucasian / non-Caucasian respondents when we look at the results from Question 14.

Comparing results by ethnicity

Comparing results by ethnicity

Seven-Point Likert Scale Examples

Here’s another set of results for questions where the students could provide seven possible responses.

Impossible-to-compare seven-point LIkert scale questions

Impossible-to-compare seven-point LIkert scale questions

I can’t make any sense of the data when it’s presented as a bunch of bars, but when I use divergent stacked bars it becomes very easy to compare and sort the results.

Combined values for seven-point Likert scale questions

Combined values for seven-point Likert scale questions

Recommendations to Utah State University

  1. Continue to make these results public, but make the results usable.  You can do this by…
  2. Reshaping the data to make it much easier to manage in Tableau (see http://www.datarevelations.com/using-tableau-to-visualize-survey-data-part-1.html).
  3. Using divergent stacked bar charts to display Likert scale data.

Click HERE to see interactive dashboard.

Mar 052013
 

My problem with most infographics is that they sacrifice accuracy and clarity for whimsy and cuteness. While I understand the desire to “draw the reader” in, I believe it’s critical that the information and the story not be misleading.

So, imagine my delight when I thought I had found an infographic that was spot-on accurate and fun and engaging.

Last month a friend had posted a link to a Huffington Post article about the Ten Most Read Books in the World.  This article contained Jared Fanning’s clever  infographic.

Wow, I thought, this is fun, clever, and clear.

But then I saw that the zero value for the Y-axis was in the middle of the chart and realized that the graphic was very misleading.  If you don’t look carefully you would think that readership of The Holy Bible is a little more than twice that of The Diary of Anne Frank.  If, however, you hide the clever part of the graphic and have the y-axis start at zero, you see a much more accurate interpretation of the data.

So, how would I display the data?

If I did not feel pressure to be mirthful I would go with something like this (rendered using Tableau 8 in about five minutes):

If I felt compelled to add some eye candy I might try something like this:

Then I would spend around three hours trying to make the book icons easier to read.

By the way, I’m the first to admit that this approach is not nearly as much “fun” as the first infographic.

But this graphic is accurate and clear, and that has to come first.

Note: One of the problems with the data itself is that The Holy Bible so dwarfs most of the other books.  I did experiment with a Bubble chart (see below) but didn’t want to spent valuable time getting all the book icons to be “just so.”

Jan 312013
 

I spend half my time as a musician and the other half as a data visualization “scientist”.  I love both professions but one downside shared by both professions is that I cannot listen to music nor glance at a chart without trying to figure out what is going on inside the music and inside the chart.

Consider this snippet from a recent NY Times / CBS Poll on Americans’ Views on Gun Control:

I was able to interpret this and all the other charts in the article quickly, but I found myself wondering if the information would read or “sing” better with a divergent stacked bar chart instead of a standard stacked bar chart.  Here’s a version I created using Gantt bars in Tableau:

I like how the divergent (or” staggered”)  approaches shows the skew in sentiment.

For information on how to create this type of chart, see Likert Scales: The Final Word and Masie’s Mobile Pulse Survey.

Note: I’m not able to post the workbook as I created it using Tableau 8 and I do not have access to Tableau 8 Public yet (it is in restricted beta).  As per Joe Mako’s comments below, you can find a downloadable solution at http://public.tableausoftware.com/views/firearmownership/Dashboard.

 

Dec 032012
 

We begin a new feature this month where I look at some recently-published data visualizations and offer suggestions on how they can be improved.

I’ll start with the MASIE Center’s Mobile Pulse Survey results.  For those of you that don’t know, The MASIE Center is a Saratoga Springs, NY think tank focused on how organizations can support learning and knowledge within the workforce.  They do great work.

So, why focus on this report?  There are three reasons:

  1. The subject is survey data and as readers of this blog know I’ve done a lot of work in this area (see http://www.datarevelations.com/using-tableau-to-visualize-survey-data-part-1.html).
  2. The subject matter, mobile learning, is near and dear to me after my stint with the eLearning Guild.
  3. There’s good stuff in the data, but the published visualizations make it difficult to understand what the data is trying to say.

Ah, Likert-Scale Questions

Consider this chart below that attempts to describe the results to the question “Current level of interest in providing the following learning elements on mobile devices.”

This chart is a tough read. With the exception of the fourth item, “Access to the Web” which clearly has a really big “Strong Interest” bar, it’s very difficult to determine which of the ten reasons are high on respondents’ lists and which are low.

Consider instead how easy the chart below is to “grok” when we superimpose an average Likert score atop a divergent stacked bar chart.

With this rendering it’s very easy to see that “Access to Corporate Databases and Intranets” is only slightly behind “Access to the Web”.  It’s also trivial to sort the ten items by respondent sentiment.

Particularly surprising to me is the negative sentiment (e.g., no interest or low interest) towards accessing simulations.  I would have expected there to be quite a bit more interest here.  That fact was buried in the other chart.

Note: For those of you that want to see the exact values for particular items as well as just compare positive vs. negative sentiment, there is a fully-interactive version of this visualization at the end of this blog post.

Yes / No Questions

Here’s another chart that attempts to show responses to which factors cause concern about Mobile Learning.

 

Why diamonds, and why two sets of them?   Here’s an alternative that I think is easier to understand and prioritize.

Conclusion

There’s some great stuff in the Masie report, but the published charts are obfuscating rather than illuminating the data.

Here’s the interactive version of the first chart. This would be WAY cooler if we could filter by industry or company size, but that data is not available to me.

Have fun.

 Posted by on December 3, 2012 3) Mostly Monthly Makeovers, Blog 7 Responses »