Jun 222016


My obsession with finding the best way to visualize data will often infiltrate my dreams. In my slumbers I find myself dragging Tableau pills in an ongoing pursuit to come up with the ideal dashboard that shines light on whatever data set has invaded my psyche.

But is the pursuit of the perfect dashboard folly?

Probably, as I’ll explain in a minute, but I don’t want to suggest anyone not at least try for the clearest, most insightful and most enlightening way to display information.

Is this way is the best way?

This pursuit of the ideal chart preoccupies a lot of people in the data visualization community. Consider this open discussion between Stephen Few and Cole Nussbaumer Knafflic that transpired earlier this year.

As you will read, Few weighs in on Knaflic’s book Storytelling with Data and her use of 100% stacked bar charts.  He cited this particular example.

Figure 1 -- Knafflic's 100% stacked bar

Figure 1 — Knafflic’s 100% stacked bar

Few argued that there was a better approach and that would be to have a line chart with a separate line for each goal state.

Figure 2 -- Few's line chart

Figure 2 — Few’s line chart

Having written about visualizing sentiment and proclivities, I chimed in suggesting that a divergent stacked bar chart would be better (see Figure 3.) I think this presents a clearer and more flexible approach, especially if you have more than three categories to compare as the 100% stacked bar chart and line chart can become difficult to read.

Figure 3 -- My divergent stacked bar chart

Figure 3 — My divergent stacked bar chart

The ongoing public discussion was engaging and congenial but I’ve seen similar cases where one or more of the parties advocating a solution become so certain that his / her approach is without a shadow of a doubt the only right way to present the data that tempers flare high. Indeed, I’ve seen instances where some well-respected authors have declared a type of “Sharia Law” of data visualization and have banned so-called heretics and dilettantes from leaving comments on blogs and even following on Twitter!

My take? While I prefer the divergent stacked bar, the real question is whether the intended audience can see and understand the data. In this case, if management cannot tell from any of the three charts that there was a problem that started in Q3 2014 and continued for each quarter, then that company has some serious issues.

In other words, if the people that need to “get” it can in fact make comparisons, see what is important, and make good decisions on their new-found understanding of the data  — all without having to work unnecessarily hard to decode the chart — then you have succeeded.

I’m not saying don’t strive to be as efficient , clear, and engaging as possible, it’s just that the goal shouldn’t be to make the perfect chart; it should be to inform and enlighten.

And in this case I think all three approaches will more than suffice.  So stop arguing.

Understanding and educating your audience

Earlier this year I got a big kick out of something that Alberto Cairo retweeted:

Figure 4 -- Avoid Xenographphobia: The fear of unusual graphics / foreign chart types.

Figure 4 — Avoid Xenographphobia: The fear of unusual graphics / foreign chart types.

Xenographphobia! What a wonderful neologism meaning “fear of unusual graphics.”

So, why do I bring this up? While it’s critical to know your audience and not overwhelm them with unnecessary complexity, you should not be afraid to educate them as well. I’ve heard far too often people proclaim “oh, our executive team will never understand that chart.”

Really? Is the chart so complex or the executive so close-minded that they won’t invest a little bit of time getting up to speed with an approach that may be new, but very worthwhile?

I remember the first time I saw a bullet chart (a Stephen Few creation) and thought “what is this nonsense?”  It turns out it wasn’t, and isn’t, nonsense.  It took all of 60 seconds for somebody to explain how the chart worked and I immediately saw how valuable it was.

Figure 5 -- A bullet chart, explained.

Figure 5 — A bullet chart, explained.

I had a similar reaction when I first heard about jump plots from Tom VanBuskirk and Chris DeMartini. My thoughts at the time were “oooh… curvy lines.  I love curvy lines! But I suspect this is a case where the chart is too much decoration and not enough information. I bet there are better, simpler ways to present the data.”

Figure 6 -- Jump plot example. Yes, these are very decorative, but they are also wickedly informative.

Figure 6 — Jump plot example. Yes, these are very decorative, but they are also wickedly informative.

Then I spent some time looking into the use cases and came to the conclusion that for those particular situations jump plots and jump lines worked really well.

That said, there are some novel charts that I don’t think I will ever endorse, with the pie gauge being at the top of my list.

Figure 7 -- The pie gauge, aka, a donut chart within a donut chart, aka, stacked donut chart. I won't go into the use case here but a bullet chart is a much better choice.

Figure 7 — The pie gauge, aka, a donut chart within a donut chart, aka, stacked donut chart. I won’t go into the use case here but a bullet chart is a much better choice.

So, what should we do?

I’ve argued that you should always try to make it as easy as possible for people to understand the data but you should not go crazy trying to make the “perfect dashboard.”

I also argue that that while you should understand the skillset and mindset of your audience, you should not be afraid to educate them on new chart types, especially if it’s a “learn once, use over and over” type of situation.

But what about aesthetics, engagement, and interactivity? What roles do these play?  Is there a set of guidelines or framework we should follow in crafting visualizations?

Alberto Cairo, in his book The Truthful Art, suggests such a framework based on five key qualities.

I plan to write about these qualities (and the book) soon.

Sep 232015


I recently wrote about emotional vs. accurate comparisons and several people questioned whether the word “emotional” was appropriate.  (Several people questioned my assertions, too.  You can read their comments here.)

For this discussion I’ll use the term “engagement” in place of “emotion” and we’ll look into the challenges of creating public-facing visualizations that attract and engage, are clear and accurate, and do these things without “dumbing down” the subject matter.

Time Magazine and a cumbersome infographic

Stephen Few recently wrote a great post about the following infographic that appeared in Time Magazine in August, 2015.

Figure 1 -- Time Magazine's "Why we still need women's equality day" infographic. See http://time.com/4010645/womens-equality-day/.

Figure 1 — Time Magazine’s “Why we still need women’s equality day” infographic. See http://time.com/4010645/womens-equality-day/.

I have three major problems with this treatment.

  1. This is an important subject but the cutesy approach trivializes it.
  2. With so many chart types I have to work very hard to make comparisons among the different areas (Federal, Congressional, etc.). In addition, the chart is very long and requires a lot of scrolling.
  3. I strongly suspect that most people thought this was a dashboard having to do with Republicans and Democrats. I know that for me, whenever I see red and blue in a political context I think Republicans and Democrats and I had to fight this expectation to see that this was about men and women.

Stephen Few’s redesign

Here is Few’s redesign.

Figure 2 – Stephen Few’s clear and compact redesign.

Figure 2 – Stephen Few’s clear and compact redesign.

The collection of stacked bars makes it very simple to compare across the various categories and treats an important subject with the seriousness that is warranted.


Few’s treatment is rather clinical and may be a little too dry for Time Magazine.

So, is there a way to fashion a graphic that is clear and accurate, like Few’s, but does more to draw the reader in?

Alberto Cairo’s redesign

Stephen Few asked Alberto Cairo to have a look at the source graphic and Cairo was able to turn out the following in a matter of minutes.

Figure 3 -- Cairo's redesign of Few's redesign.

Figure 3 — Cairo’s redesign of Few’s redesign.

Here are Stephen Few’s comments upon seeing the redesign:


You’re the man! I love your improvements to the graphic.

You described your version as middle ground between my position and that of the embellishers, but I don’t see it that way. I’m an advocate of the kinds of embellishments that you added to the graphic for journalistic purposes, for they don’t detract from the information in any way. I’ve always said that journalistic infographics can be both informative and beautiful without compromising either. Doing this takes skill, however, that relatively few of the folks producing infographics possess. It also takes graphic design skill that I don’t possess, which is why I don’t design journalistic infographics. You’ve illustrated what it takes to do this well. As I said, you’re the man.”

I think Cairo would be the first to agree that there are many shortcomings to his rendering (e.g., colors, the guy on right looks like he’s holding a boomerang and not reading a book, etc.) but remember, Cairo put this together in a few minutes simply to show that it is in fact possible to create something that is beautiful and emotionally engaging without sacrificing one pixel of analytic integrity.


Jun 042015

Showing Differences between Periods and Statistical Significance in Tableau


Addressing this scenario has been the most popular request I’ve received over the past year. Here’s a summary what my clients and students have asked:

  • How do I show the change in Sales, Percentage of Promoters, Number of Visits, etc., between this month / quarter / year, and the previous month / quarter / year?
  • How do I make it easy to see which areas of the organization had an increase this period and which had a decrease?
  • How do I make it easy to see how much greater / less this period’s numbers are than the previous period?
  • How do I determine and show if this change is statistically significant? That is, how do I apply the stat test we like to use in our organization?
  • If the change is statistically significant, is it a one-time thing or should I start hyperventilating?

This is a LOT to take on and we won’t be able to fit all of it into a single visualization.

But we can fit it into a compact dashboard.

Important Ground Rules

In the example that follows I look at the percentage of people that responded with a “9” or “10” to a survey question. That is, I am only looking at the percentage of people that selected one of the top two boxes.  I am NOT trying to see if there is statistical significance or calculate the margin of error in the change in Net Promoter Score over time.

The concepts I explore are not just for survey data; I just happen to have some good longitudinal survey data that is well-suited for seeing how to build a stat test formula in Tableau.

I hope you will indulge me and accept that “the company stat guru” has a fine reason for applying a particular statistical test to the data we’ll be analyzing. That said, you should push back on “business-as-usual” assumptions to determine if what you are visualizing and testing really is important (this is the focus of the work Stacy Barr is doing with her Measure Up blog and is the foundation for Stephen Few’s most recent book Signal.)

So, with the assumption that the particular stat test we want to apply – or any stat test, for that matter – is warranted, how do you show it and how do you build it?

Let’s first explore the working dashboard then see how to build it with Tableau.

Note: A very heartfelt thanks to Kelly Martin,, Joe Mako, Vicki Reinhard, Susan Ferarri, and Tiffany Spaulding who helped vet the dashboard.  I went through many different approaches before settling on the one shown below.

A very special thanks to Jeffrey Shaffer who reviewed the blog post and asked some very good questions, and also to Helen Lindsay who provided sample data.

The data and what we want to show

The data below contains the first few rows of Net Promoter Score survey data with fields for date and role.

Figure 1 -- Net Promoter Score survey data with dates and roles

Figure 1 — Net Promoter Score survey data with dates and roles

For the dashboard I built I only focused on the percentage of people that were Promoters; that is, people who responded with a 9 or 10 when asked if they would recommend a product or service.

I decided to look at the data broken down by quarters as this particular data set didn’t lend itself to month over month comparison.  Note that the techniques we’ll see will work for any time period.

Here’s the top portion of the interactive dashboard.


Figure 2 — Top portion of dashboard.  Notice that you can change the selected period, the confidence percentage, and filter by company.

Understanding the chart

Figure 3 -- The key features of the chart

Figure 3 — The key features of the chart

Let’s review what we can glean from the chart.  We can see

  • The percentage of promoters for a particular period and sort them by role, using a bar chart.
  • Which roles have a percentage of promoters that is greater than the previous period and which have less, using color to distinguish (blue for greater, brown for less).
  • Just how much more or less the percentage for this period is compared to the previous using a reference line (the bar is the current period; the vertical line is the previous period).
  • Which roles showed a significantly significant increase or decrease (the red dot).

Note that that the chart uses “Cotgreavian” tooltips that allow you to glean more detail for a particular role when you hover over a bar:

Figure 4 -- Hover over a bar for in-depth information about the role for the current period and the previous period

Figure 4 — Hover over a bar for in-depth information about the role for the current period and the previous period

So, we can see from the red dot that something is up with Lawyers, Doctors and Nurses; that is, the percent increase from the previous period for Doctors and Lawyers is statistically significant and the percent decrease for Nurses is also significantly significant.  Is this a one-time thing or a trend?

Looking at changes over time

Clicking a role or roles will display trends for that role / roles.  For example, if we select Nurse in the top chart a second chart showing percentage of promoters over time will appear, as shown here.

Figure 5 -- Percentage of nurses that are promoters, over time.

Figure 5 — Percentage of nurses that are promoters, over time.

The big takeaway for me is that up until the first quarter of 2013 there were very few responses and after that there was both a consistent number of responses along with a consist decline in the percentage of nurses that were promoters.

Should you be hyperventilating because of the four-month downward trend?  That discussion is beyond this blog post but I again encourage you to check out the work Stacy Barr is doing at her Measure Up blog as well as Stephen Few’s most recent book Signal.

How the This Quarter vs. That Quarter Chart is Built

Let’s dig into how to build this in Tableau, starting with the top viz in the dashboard.

Figure 6 -- What's under the hood.

Figure 6 — What’s under the hood.

  1. Promoters – Current Quarter. This is the measure that drives the bars.  It’s also driving what appears on the labels.
  2. Promoters – Previous Quarter. This measure is on the Level of Detail and drives the reference lines.
  3. Greater / Less. This is a discrete measure that determines the color of the bar.

Promoters – Current Quarter

What we want is the percentage of people that were promoters for the selected quarter, the “selected” quarter being determined by a parameter that the user can control.

Specially, we want to add up everybody that responded with a 9 or 10 for the selected quarter and divide by the total number of people that responded.  Here’s the calculation that handles this.


IF [Value]>=9 and DATETRUNC(‘quarter’, [Select Period])==DATETRUNC(‘quarter’,[Date])
then 1 else 0



IF DATETRUNC(‘quarter’, [Select Period])==DATETRUNC(‘quarter’,[Date])
then 1 else 0

The translation into English is

Take the sum of

If the value from a respondent is greater than or equal to 9 and the date value, truncated to the nearest quarter from the parameter drop down [Select Period] is the same as the date value, truncated to the nearest quarter for [Date], then 1, else 0.

Divide this by the sum of

If the date value, truncated to the nearest quarter for the selected period is the same as the date value, truncated for the nearest quarter for [Date], then 1, else 0.

Not sure about the [DATETRUNC] function vs. the [DATEPART] function?  Have a look at Joshua Milligan’s excellent post explaining date values vs. date parts.

Promoters – Previous Quarter

This calculation is very similar to the calculation for the Current Quarter, except we want to find results for the quarter that occurred just prior to the selected quarter.  Here’s the calculation.


IF [Value]>=9 and DATETRUNC(‘quarter’, [Select Period])=DATETRUNC(‘quarter’,DATEADD(‘quarter’,1,[Date]))
then 1 else 0



IF DATETRUNC(‘quarter’, [Select Period])==DATETRUNC(‘quarter’,DATEADD(‘quarter’,1,[Date]))
then 1 else 0

The formula is the same except we use the DATEADD function to add an additional quarter; that is, we’re saying that we only want to find results where, when we add an additional quarter, we get a value equal to the current quarter; i.e., the previous quarter, plus one quarter, gives us the current quarter.

Greater / Less

The color of the bars is determined by this discrete measure:

IF [Promoters — Current Quarter] > [Promoters — Previous Quarter] then “Greater than previous”
else “Less than previous”

Yes, I suppose we should have a contingency for when the percentage of promoters for the current period is the same as the previous period; I leave it as an exercise for the reader to add this functionality.

So, we’ve explained everything except … The Red Dot.

The Red Dot – Computing Statistical Significance on the Fly

Most of my clients and students are surprised to find out that you can fashion a test for statistical significance inside Tableau and it can test for statistical significance “on the fly”; e.g., you can apply filters and Tableau will recalculate based on the filter settings.

The first step is determining just how the client wants to test for statistical significance. This usually entails sending an inquiry to “the stats person” who responds with something that looks like this:

Figure 7 -- Z-test formula for statistical significance

Figure 7 — Z-test formula for statistical significance

I hope your eyes aren’t glassing over as this really isn’t very complicated; it just might look complicated if you’re not used to seeing stat formulas with square root symbols.  Here are the critical things you need to know:

p1            Percentage of promoters for the current period

p2            Percentage of promoters for the previous period

n1            Number of respondents for the current period

n2            Number of respondents for the previous period

If z1 is greater than or equal to 1.96 then there is a 95% degree of confidence that the difference between the two periods is statistically significant.

So, how do we build this formula?

Slowly, and in easy-to-digest pieces.

The Dot Itself

Figure 8 -- The discrete measure Z-Test Significance Dot is responsible for displaying the dot

Figure 8 — The discrete measure Z-Test Significance Dot is responsible for displaying the dot

The calculation that produces the dot is called Z-Test Significance Dot and it is defined as follows.

IF ABS([Promoters — Z-Score Quarter])>=[Confidence] THEN “•”

This translates as

If the absolute value of [Promoters – Z-Score Quarter] is greater than or equal to the confidence parameter (currently set to 1.96, or 95%) then display a dot; otherwise, display a null string.

And just how is [Promoters – Z-Score Quarter] defined?  Let’s explore the next layer of the onion.

Promoters – Z-Score Quarter

This is defined as follows:

[Promoters — Z-Score Quarter Numerator] /


([Promoters — Z-Score Quarter Denom – Current] +
[Promoters — Z-Score Quarter Denom – Previous])

Here’s how it maps to the stat formula we saw earlier:

Figure 9 -- Mapping the components of the formula to different calculated field

Figure 9 — Mapping the components of the formula to different calculated field

So now we just need to understand the three different pieces that go into the stat function.

Promoters – Z-Score Quarter Numerator

This is very simple and refers to calculations we’ve already used.

[Promoters — Current Quarter] –
[Promoters — Previous Quarter]

Promoters — Z-Score Quarter Denom – Current

This is fairly straightforward given what we’ve already explored.

([Promoters — Current Quarter]*(1-[Promoters — Current Quarter]))
/SUM([Promoters — Current Quarter Count])

Where [Promoters – Current Quarter Count] is defined as follows.

IF DATETRUNC(‘quarter’, [Select Period])==DATETRUNC(‘quarter’,[Date])

So SUM(Promoters — Current Quarter Count]) is just adding up all the people that responded during the selected quarter.

Promoters — Z-Score Quarter Denom – Previous

([Promoters — Previous Quarter]*(1-[Promoters — Previous Quarter]))/
SUM([Promoters — Previous Quarter Count])

This uses the same logic as [Promoters – Z-Score Quarter Denom – Current] but instead aggregates results from the previous quarter.

Putting it all together

In addition to building the components in a piecemeal fashion I will often build a crosstab of all these components to see if they are working as I would expect.  Consider the crosstab shown here.

Figure 10 -- Crosstab showing all the pieces that contribute to the red dot

Figure 10 — Crosstab showing all the pieces that contribute to the red dot

The cross tab allows us to examine all the intermediate calculations to see how the contribute to the determining calculation in the last column.

What about the secondary chart?

So we’ve now seen how to build the top chart that shows current and previous quarters broken down by role.  How does the secondary chart – the chart that appears when you click a role or roles in the first chart – work?

Figure 11 -- Percentage of promoters for Nurses over time

Figure 11 — Percentage of promoters for Nurses over time

Here we have a dual axis chart so that we can have both a line (gray) and a circle (colored based on whether the change for the previous period is statistically significant).

In this case we have to construct all of the pieces using a table calculation, but the process of putting together the different components is identical to what we saw earlier.  For example, the calculation that determined the color of the circle, [LONG_Z-Test Significance], is defined as follows.

IF ABS([LONG_Z-Score])>=[Confidence] then “Significant”
else “Not significant”

And [LONG_Z-Score] is defined this way:

[LONG_Z-ScoreNumerator] /


([LONG_Z-Score Denom Current] +
[LONG_Z-Score Denom Previous])


I also built a crosstab to see how all the pieces fit together, as shown below.

Figure 12 -- Crosstab to help put together a z-test calculation for values shown over time

Figure 12 — Crosstab to help put together a z-test calculation for values shown over time


The dashboard in this blog post shows the percentage of promoters, sorted by role, for a particular quarter, compared with the percentage of promoters for the previous quarter.  Roles where the percentage difference is statistically significant are marked with a red dot. You can drill down on a particular role (or role) and see how scores have changed over time.

While the critical visual component was showing bars and reference lines, most of the “heavy lifting” went into determining if a change was statistically significant.  The key here was to not be intimidated by a statistical formula and to build the calculations in small pieces, using crosstabs to check the work.


Jun 102014

… and some thoughts on the evolving art and science of visualizing data

I tend to gravitate towards occupations that are hard to explain.  I started my professional life, and continue to be, a music arranger and orchestrator.  I can tell by people’s perplexed looks that they are wondering if I’m the guy that decides where the brass section should sit in the pit.

I run into similar problems when I tell people I’m a data visualization consultant.  I was trying to come up with a concise way to explain what that is when I came across an excellent blog post from Stephen Few.

I take this and turn it into that

I do encourage you to read the full post (you can do it now if you like; I’ll wait).

I was struck by the first example where Few shows how hard it is to glean any meaning from a text table.  Here’s his example of poll results published on the PBS website from a 2004 study by the Pew Center for Research.


Figure 1 — Favorable and Unfavorable views of the U.S.A.

I have to work very hard to get a sense of which countries have the most positive sentiments towards the U.S.A. and which have the most negative.

Few proposes a different way to present the data that makes it much easier to see, rank, and understand the findings.


Figure 2 — Few’s alternative to presenting the findings

Yes!  This exercise encapsulates what it is that I do!  I take “this” and turn it into “that”, thereby allowing companies to better see, understand, and glean insights into their data.

An alternative to the alternative and how the industry keeps evolving

I cannot just listen to music.  My training and proclivities force me to dissect the music I hear so that I can understand what’s going on inside the music.

A similar thing happens when I see a data visualization.  After taking in the presentation I stop and wonder if there is an alternative approach that would allow me to better understand what’s going on and thereby draw better conclusions that in turn allow me to make better decisions.

In a moment I’m going to suggest an alternative to Few’s approach but I do want to emphasize that the data visualization field is very new and it’s the free exchange of ideas that’s pushing people to create new ways visualize data.  A perfect example of this is my own evolution in displaying Likert scale data (see Likert Scales – The Final Word).  It was discussions with friends and colleagues Naomi Robbins and Joe Mako that resulted in what I think is a better way to explore and glean insights from the World Opinions data.

The divergent (or staggered) stacked bar chart

Consider the screenshot of a dashboard below where we skew the stacked bars right and left based on overall positive and negative sentiment. Note that you will find a working dashboard at the end of this post.


Figure 3 — Conveying sentiment using a divergent stacked bar chart.

If you split the neutral responses evenly you see that, overall, Poland has the most positive sentiment and Egypt the most negative.

But what happens if you eliminate the neutrals?  If you sort by least negative you see certain things pop out.


Figure 4 — Neutral responses are hidden results are sorted by least negative

Here Poland is ranked first and Jordan is last (and notice how polarized Jordan is).

Compare this with the view when you remove the neutral responses and sort by most positive.

Figure 5 -- Neutral responses are hidden results are sorted by most positive

Figure 5 — Neutral responses are hidden results are sorted by most positive

In this case Kenya is ranked first and Egypt is last.


The divergent stacked bar is my “go to” viz type whenever I deal with Likert scale data.  The only downside is that is takes a bit more time to create in Tableau and it warrants using a color legend, something I try to avoid where possible.

But this divergent stacked bar chart is my Likert-scale viz of choice today.  Who knows what people will create in the coming years that does an even better job of helping people understand their data.

Oh, and I now have a compact explanation of just what it is I do.  I turn a this into a that.

Postscript: I’ve been thinking about this and want to modify my explanation… let’s change it to “I take this and I try to turn it into the best that that’s possible”.

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.


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.