Sep 212015
 

Overview

I’ve conducted a lot of Tableau training classes and have found three things that confuse students simply because of the nomenclature Tableau uses for these things.  These three terms are

  • Headers
  • Table Calculations
  • Quick Filters

Headers

Consider the chart below that has both mark labels and an axis along the bottom.

Figure 1 -- Bar chart with visible axis.

Figure 1 — Bar chart with visible axis.

Because each bar has a label we don’t need to see the axis.  We can hide the axis by right-clicking it and selecting…

Figure 2 -- Turning off the header turns off the... footer.

Figure 2 — Turning off the header turns off the… footer.

… Show Header.

Yes, indicating that we don’t want to display a header will make Tableau hide…

the footer!

As I explain to students, in Tableau anything that surrounds a chart is called a Header.  If it’s along the top of a chart, it’s a Header.  Left side of the chart?  Header?  Bottom?  Header.  Right side?

Header.

Table Calculations

I know the first time I saw this I thought “Table Calculations” pertained to a visualization that used text tables. As I explain to students, I think of table calculations as Tableau having the ability to do math in its head.

Consider the example below where we show the raw vote count for each candidate from the 2012 US presidential election.

Bar chart based on query to the back-end database

Figure 3 — Bar chart based on query to the source database

Here, Tableau has queried the underlying database and is displaying the results based on that query.

With a table calculation, Tableau looks at the results that are already on display, as it were, and then does some additional internal calculations.  In the case of asking Tableau to show the percent of total, Tableau adds up the total for all three candidates and then divides the tally for each candidate by that total.

As I said, I find it helpful to think of Tableau Calculations as Tableau doing math in its head.

Quick Filters

To filter results in Tableau, you drag dimensions and measures from the Data window to the Filters card and then apply the settings you want for the various filters.

If you want easier access to the filter settings you can right-click a filter and select Show Quick Filter.

The problem with this term is that people new to Tableau think this pertains to speeding up the filter when it in fact means that you just want the filter control to be visible on a worksheet or a dashboard.  It has nothing to do with making filters quick.  In fact, having lots of quick filters on a worksheet can slow Tableau down because Tableau has to calculate what selections should appear in each of the quick filters.

The only rationale I can see for the name is that it allows you to access the settings quickly rather than having to go through the Filters dialog box.  Still, it’s quite confusing for those first learning Tableau.

Summary of confusing terms

Here’s a summary of the terms that often confuse people new to Tableau.

Term What students think it means What it actually means
Header Something at the top of a chart Anything that surrounds a chart
Table Calculation Something having to do with text tables / cross tabs The ability for Tableau to do math “in its head”
Quick Filters Some setting that makes filters work faster Make the filter control visible

What should we call these things and should Tableau rename them?

Given just how entrenched Tableau is it may be too late to change these terms, but if it’s not too late…

In the case of Show Quick Filters I would change it to Show Filter Control.

What about Table Calculations and Headers?  Got any ideas?

 

May 112015
 

Much thanks to Susan Ferrari for exposing me to the concept of Net Promoter Score, Susan Baier for encouraging me to blog about it, and Helen Lindsey for providing anonymized NPS data.

Overview

My wife and I recently went out to a restaurant to celebrate our anniversary.  Accompanying the check was a survey card with three questions, one of which looked like this.

Figure 1 -- The classic Net Promoter Score question

Figure 1 — The classic Net Promoter Score question

We both agreed that the restaurant was very good, if not excellent, and that we would indeed recommend it to friends.  My wife suggested we circle the “8”.

I told her that if we were enthusiastic about recommending the restaurant we should give it a “9” as a 7 or 8 would be tabulated as a “neutral” or “passive” response.

She looked at me quizzically and asked why an “8” would be considered neutral.

I then explained how the Net Promoter Score works.

Understanding the Score

Respondents are presented with the question “Using a scale from 0 to 10, would you recommend this product / service to a friend or colleague?”

  • Anyone that responds with a 0 through 6 is considered a Detractor.
  • Anyone that responds with a 7 or 8 is considered a Passive (or Neutral).
  • Anyone that responds with a 9 or 10 is considered a Promoter.

The Net Promoter Score (NPS) is computed by taking the percentage of people that are Promoters, subtracting the percentage of people that at Detractors, and multiplying that number by 100.

How to compute NPS, courtesy B2B International.

Figure 2 — How to compute NPS, courtesy B2B International.

If you are like me (and my wife) you’re probably thinking that a “6” is a pretty good score and that it shouldn’t be bunched among the detractors.

I’m not going to get into a debate about NPS methodology and its usefulness, but I do want to show you some good ways to visualize NPS data.

The Problem with the Traditional Presentation

Consider this snippet of NPS survey data with responses about different companies from people in different roles.

Figure 3 -- Raw NPS data about different companies from people with different occupations.

Figure 3 — Raw NPS data about different companies from people with different occupations.

If we just focus on the NPS and not the components that comprise the NPS we can produce an easy-to-sort bar chart like the one shown here.

Figure 4 -- Traditional way to show NPS

Figure 4 — Traditional way to show NPS

Yes, it’s easy to see the company D has a much higher NPS than company H, but by not showing the individual components – and in particular the Neutrals / Passives –  we’re missing an important part of the story as the Neutrals / Passives are right on the cusp of becoming promoters.

For example, a Net Promoter Score of 40 can come from

  • 70% Promoters and 30% Detractors
  • 45% Promoters, 50% Passives, 5% Detractors

Same score, big difference in makeup.

An Alternative Approach to Displaying NPS Results

Consider the dashboard below which presents the data as a divergent stacked bar chart.

Figure 5 -- NPS dashboard with toggle to show percentages and score.

Figure 5 — NPS dashboard with toggle to show percentages and score.

The chart is easy to sort and you can also see that Company B and Company F have a relatively large group of Neutrals.

That said, being able to see the NPS score is very useful so the dashboard (see working version at the end of this post) has a toggle that switches between percentages and the score, as shown below.

Figure 6 -- Divergent stacked bar chart with NPS overlay.

Figure 6 — Divergent stacked bar chart with NPS overlay.

Note that the NPS divergent stacked bar chart is just a variation on a Likert scale divergent stacked bar chart.  You can find an explanation of how to build this type of visualization here.

What’s Next?

We now have what I think is a more insightful way to visualize Net Promoter Score data.

But clients and readers of my blog have asked me to address some of these questions as well:

  • How do you show the difference in NPS, or just the difference in percentage of promoters, between this quarter and the previous quarter?
  • If there is a difference, is the difference statistically significant?
  • What’s a good way to visualize and analyze NPS over time?

I will be addressing these issues in an upcoming post.  Stay tuned.

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.

Dec 092014
 

In Part One of this series I discussed why the Tableau support community is unique and why you should care. In Part Two I shared my thoughts on the early years of the community and how one person in particular set the tone for sharing knowledge and expertise.  In this final post I make recommendations on the things you can do to ensure that the community continues to thrive.

What you can, and should do, to ensure the community thrives

I rely on this community to inspire me, cheer me on, and help me when I need it.

I don’t want to lose this invaluable asset, so I’m going to enlist you to contribute to its wellbeing, assuming you are not already doing so. Here are some things you can do.

Ask for help

If you can’t find what you need through a web search, ask for help as it will help the community as a whole. While counterintuitive, asking for help will generate a discussion that will lead to solutions that will help not just you but others that are having or will have the same problem you have.

And just where should you ask for help?  Tableau’s community forum is a great place to start.  If you look you will see a lot of Zen Masters who have posted questions, not just answers, through the years.

In addition to asking, if you want to observe noteworthy Tableau activity, make sure to check out the Twitter hashtag #tableau and also check out the list of Tableau-related tweeters Andy Cotgreave has assembled here.

Show the love

If someone has helped you or something has inspired you, send them a “thank you” e-mail, launch a tweet, comment that person’s blog, but above all please let the person who helped you know you appreciate what he / she has done (and in my case feel free to send dark chocolate and / or red wine).

Cheer1

Cheer2

Cheer3

Figure 1 -- Beers were free at The 2014 Tableau Conference.  But I appreciate the sentiment.

Figure 1 — Beers were free at The 2014 Tableau Conference. But I appreciate the sentiment.

Cheering each other on is a big reason the community thrives.

Post your work to Tableau Public.

If you create something worthwhile, share it with the world.  Tableau Public makes it easy, and it’s free.

If you recall from Part One, I stated that Tableau Public is a masterstroke in fostering community and visualization excellence in that it provides a free service for people to post their work.  The public will in turn remark on the work, but the really amazing thing is that a Tableau user can download packaged workbooks to see how they work.

Consider this great “how-to” example from Josh Milligan.

Figure 2 -- A great "how to" example from Josh Milligan that anybody can download and dissect.

Figure 2 — A great “how to” example from Josh Milligan that anybody can download and dissect.

Notice the “Download” button in the bottom right corner.  With Tableau Public I can do more than just interact with the viz; I can download the workbook and see how the person built it.

Help others whenever and wherever you can.

You may not be able to pay back the person or people that helped you, but you can help others.  Do not feel pressure to change the world or have the same impact as a Joe Mako or a Jonathan Drummey, but there’s a lot you can do including participating on the Tableau forum, writing a blog, attending a user group meeting (live or virtual), helping a non-profit understand their data, or just commenting on someone else’s work.

With respect to the forum, try “lurking” (just hanging out and observing the various conversations) to see if this might be an outlet for your abilities. If nothing else you’ll learn a great deal.

With respect to blogging, the barrier to entry has never been lower and this is a great way to find your voice and contribute to the community.  Indeed, Andy Cotgreave maintains that if you can have a Google account you can create a blog and publish a post in fewer than three minutes.

Figure 3 --  Dan Montgomery, Paul Banoub, and Anya A'Hearn, and Lewell Loree stopping traffic and evangelizing blogging at the 2014 Tableau Conference.

Figure 3 — Dan Montgomery, Paul Banoub, Anya A’Hearn, and Jewel Loree stopping traffic and evangelizing blogging at the 2014 Tableau Conference.

Do not celebrate or reward mediocre work.

We should, as a community, be working to improve the art and should not reward stuff that isn’t good.  I’m not saying that you should be a jerk (remember, there are no jerks in this community, at least not yet) but if you see something that you know can be better, please let the person – and the world – know what you would do to make it better.

I’ve written about this on several occasions (please see My Problems with a Company’s Iron Viz Competition and Ask These Three Questions.)

Incidentally, people critique my work all the time and I’m grateful for the feedback.  Indeed, if I have any “high-stakes” dashboards I want to publish I will always ask both colleagues and laypeople to review the work before it goes live (please see the “Usability” section of Your Tableau Public Viz is Ugly *and* Confusing.)

Don’t be too hard on yourself

I remember something Joe Mako told me several years ago:

I like Tableau because it allows me to fail faster.

Do not be afraid to fail, and to fail easily and often. It takes time, study, and practice to get good at data visualization and Tableau.  Do not be afraid to post something on Tableau Public and ask for help or criticism.  Most people will offer constructive help and you’ll get better, fast.

I look forward to seeing your work, reading your tweets, and pondering your questions.

 

Dec 012014
 

In this continuation from Part One I share my thoughts on the early years of the community and how one person in particular set the tone for sharing knowledge and expertise.  

How did this start?

There were a lot of really great people contributing to the community in Tableau’s early years.  I’ve already mentioned Jonathan Drummey and Richard Leeke.  Others I recall include Alex Kerin, Andy Cotgreave, James Baker, and Russell Christopher.

But there’s one person in particular that I think set the tone and established the precedent for discovering and sharing Tableau knowledge.

Meet Joe Mako

At the 2011 customer conference in Las Vegas, Tableau singled out Joe Mako for responding to an unfathomable number of Tableau forum posts that directly helped hundreds and indirectly helped thousands of people.

And what prompted Tableau to do this? This is a great example of the community raising its voice and recognizing contributions of one of its own.  Several people, including Matt Shoemaker, Richard Leeke, Dan Murray, Mel Stephenson, Tim Costello, and Tom Brown, petitioned Tableau, urging the company to recognize Joe for his incredible contributions.

This citing by Tableau led to the creation of the Tableau Zen Master program.

For the first two years of the program Tableau used the following Venn diagram to show the confluence of skills and temperament that comprise a Zen Master:

Figure 7 -- Generic Zen Master Venn Diagram

Figure 7 — Generic Zen Master Venn Diagram

Here’s what I think would be the appropriate diagram for Joe:

Figure 8 -- Joe Mako Zen Master diagram

Figure 8 — Joe Mako Zen Master diagram

Anyone who has been on a screen sharing call with Joe (and there are probably hundreds of people who have availed themselves of Joe’s generosity) can attest to stunning, fully-fleshed solutions emerging from Joe’s brain.

I’ve spent enough time with Joe to realize it isn’t just Robin Williams-like brilliance but Bruce Lee-type discipline Joe has applied to really understanding Tableau.

Joe is also remarkably kind and reassuring, offering a soothing, Mr. Rogers-like “don’t worry, we’ll figure this out together” encouragement whenever I’ve been stuck and needed help.

The only downside of a screen sharing session with Joe is that you get off the phone and think that “jeez, this guy is smarter than I am, more disciplined than I am, and … he’s nicer than I am” (and I’m a very nice guy.)

We are very lucky to have him in our community.

How did these seeds produce so much fruit?

How did the contributions of Joe and a handful of others lead to such a large, rich community?

I can’t speak for others, but my contributing stemmed from a desire to repay those people (especially Joe) that had given me so much help.

The problem was that I could not pay these people back directly as there was not much I had to offer them, save appreciation and gratitude.*

But I could give back to the community as a whole.  In my case I don’t attempt to answer forum posts in near real time.  This skill is best left to folks like Shawn Wallwork, Mathew Lutton, Grayson Deal, Joe Oppelt, Noah Salvaterra, Joshua Milligan, KK Molugu, and many others that do an amazing job.  I give back through pro bono work and blog posts.  Specifically, in addition to helping out non-profit organizations I try to publish a useful “here’s how you do this” blog post at least once a month.  The posts can take hours to write, but that’s a small price to pay for what the community gives me in return (and I will confess that they do generate interest in my work).

If you are like me you rely on this community to help and inspire you.  I, for one, love having the safety net of knowing that there are literally dozens of great minds that I can tap for help and inspiration.

I’ve already told you what I do to contribute to the community.  In Part Three of this series I’ll provide ideas on what others can to ensure the community thrives.

* Note: Expressing appreciation and gratitude are essential to the community.  I’ll discuss this more in Part Three of this series.

Nov 242014
 

This is the first in a series of three blog posts that explores why what makes Tableau’s support community so special, why you should care, and what you can do to ensure that the community thrives.

My sincere thanks go to Andy Cotgreave and Jonathan Drummey who reviewed an early draft and provided invaluable feedback.  Their actions exemplify what this community is about.

72467,47

Anybody recognize that form of user ID?  It’s my CompuServe ID, circa 1985.

compuserve-672x600_cropped

Figure 1 — Getting help from a support forum looked like in the Australopithecus era of personal computing.

I bring this up because over the past 30 years I’ve been involved with dozens of different software communities and I’ve never seen anything that matches the quality, creativity, generosity, and, well, love, that surrounds Tableau.

And just what makes me say that, and why should you care?

Let me start with the second question first.

Why you should care

When people ask me why I like Tableau and why I recommend the product I tell them the following:

  • Tableau more often than not allows “mere mortals” to discover what is important in their data, create compelling visualizations, and share these visualizations with others. By “mere mortals” I mean people not steeped in data science or graphic design.
  • If, however, you are not able to figure out what to do there is a community of people at your beck and call that will help you. The community will NOT allow you to fail.
  • This same community will inspire you to do better work.

Examples of the community at work and what makes it unique

Forum Posts

I was at a client earlier this year and we had our collective heads-down trying to find a visualization that would really make the underlying data sing.

The client asked about displaying pie marks on a scatterplot. Although I was almost certain that this would not yield useful insights (my polite way of saying it would look really dumb), I figured we might as well give it a try as it’s so easy to just try stuff with Tableau and see if it yields good results.

Well, it’s usually really easy to just try stuff.

In this instance I was spinning my wheels for a bit so I invoked what I call the Tableau ten-minute rule:

If, after ten minutes of working in Tableau you come to the conclusion that you are not making any progress, see if anybody else has already solved the problem.

So, I typed the following into Google:

“scatterplot with pie marks Tableau”

My first hit was this:

http://community.tableausoftware.com/message/225437

01_DrummeyHelp

Figure 2 — A snippet for an amazingly cogent response, typical of Jonathan Drummey and so many others who share their expertise freely on the Tableau support forum.

Holy Venn Diagram, Batman!  Not only was this exactly what I needed, but the post contained a wonderfully-written discourse on how Tableau works.  Who takes the time to write stuff like this?

Ah, of course.  Jonathan Drummey:  Tableau Zen Master, Tableau forum contributor extraordinaire, and author of Drawing with Numbers, one of the “must read” data visualization and Tableau blogs.

This experience reminded me of many times that I’ve been flummoxed and I’ve sought help on the forum  One “career-critical” incident occurred just as I was starting out as an independent consultant and I was in a quandary.  I managed to find my post and the attendant response from Richard Leeke (another generous-with-his-time-and-expertise Zen Master) at http://community.tableausoftware.com/thread/111283.

01a_Leeke

Figure 3 — Richard Leeke, saving my hide at a critical juncture in my career.

I can still remember my relief when I tried Richard’s solution and it worked.  Perfectly.

Blogs

While the forum posts make the community so helpful, it’s the blog posts that make it so rich.  There are scores of people sharing their solutions and creativity and their influence on me has been profound.

I’ll cite one example.

In mid-2013 I decided to check out the “viz of the day” and see if there was anything good.  I’ll confess that I can be an insufferable snob when it comes to dashboard design and my previous visits to this site had left me unimpressed.

But then I saw this http://www.tableausoftware.com/public/gallery/socialworld

BestCommunity_Kelly1

Figure 4 — An example of Kelly Martin’s work

F&*k!  While I wasn’t paying attention a bunch of people really raised the bar on Tableau dashboard design.  Now aware that I was not quite as bad-ass as I thought, I decided to find out who built this particular dashboard and discovered Kelly Martin of VizCandy.

There is so much great work here, and so many useful blog posts.  This combination of quality and quantity is indicative of dozens of the Tableau bloggers that contribute to the community by creating something either useful or beautiful (or both), posting for the world to see using Tableau Public, and then explaining how they did it.

(I won’t attempt to list all of the great blogs and attendant posts, but in early 2014 Andy Cotgreave assembled a list of influential Tableau-related blog posts.)

Tableau Public

Tableau Public is a masterstroke in fostering community and visualization excellence in that it provides a free service for people to post their work.  The public will in turn remark on the work (both praise and brickbats), but the really amazing thing is that a Tableau user can download packaged workbooks to see how they work.

Consider this great example from Mark Jackson.

MarkJackson

Figure 5 — A great example of Tableau’s Story Points feature from Mark Jackson.

Notice the “Download” button in the bottom right corner.  With Tableau Public I can do more than just interact with the viz; I can download the workbook and see how the person built it.

I can cite dozens of times where somebody posts something cool that somebody else downloads, dissects, improves, and then re-posts, only to inspire somebody else to repeat the process.

I’ve been part of this “cycle-of-improvement” on several occasions and have downloaded hundreds of workbooks to see for myself how a fellow Tableau author built something wonderful.  I’ve gotten much better at what I do because of this free exchange.

Note: I encourage you to read Andy Cotgreave’s post about why a chart should start, not end, a conversation.  In this article you will find a great example of how three Tableau users found different and important truths in the same data set.

Tableau Employees

When I first started working on this essay in the spring something had gone horribly wrong with the forum: when I conducted a search through Google – or even on the forum itself – I could not find any forum posts.

This was not a good thing; my entire thesis of “if you can’t figure it out yourself, conduct a search on Google” was not going to withstand any scrutiny if none of the forum posts would show up in the search.

I won’t get into the details of what happened but it was an unintended consequence of making a change to the way the underlying support system (Jive Software) had been implemented.

I was irate over what had happened but as much as it was bothering me, it was bothering the folks tasked with making the forums work even more.  These are Tableau employees that care deeply about nurturing the community and I suspect they weren’t sleeping well while this was going on. It took a while, but they fixed it, so major kudos to Tracy Fitzgerald, Dustin Smith, Ross Perez, Patrick Van Der Hyde for their efforts in remedying the problem and in nurturing the online community.

Note: There’s so much more I should write about Tableau employees but I’ve decided to mention just those employees of which I am aware whose work focuses mostly on nurturing the support community.

No Jerks

I mentioned at the beginning of this essay that I’ve been involved with a lot of software communities and they have had more than their fair share of dysfunctional, bordering on toxic, personalities.

Figure 6 –For whatever reason, there don’t seem to be any jerks (at least of which I’m aware) in the Tableau community.

Figure 6 –For whatever reason, there don’t seem to be any jerks (at least of which I’m aware) in the Tableau community.

I have yet to meet any jerks, either in person or online, within the Tableau community.  They may exist, but so far I’ve only met people that were smart, eager to learn, eager to share, and remarkably well adjusted.

I have some ideas on why the community is as functional as it is, and it had a lot to do with the temperament of some of the earliest contributors (and one contributor in particular).

I will share my thoughts in Part Two of this series.

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

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.