Jan 172017
 

By Steve Wexler

January 17, 2017

This is a follow-up to the post Jeff Shaffer and I wrote about what can happen when people fail to question sources and inadvertently amplify baseless findings.

Overview

There’s been great feedback on things the community can do to maintain all the good things about Makeover Monday (MM) and at the same time reduce the occurrence of bad things.  Before I go any further I want to reiterate that I both like and value MM.  I’ve seen some incredibly good dashboards that inform my consulting practice and I plan to publish a blog on some of the design and analytic masterpieces I’ve seen.

But first…

Guilty as charged

Before presenting some recommendations, I want to cite an e-mail I received from Ben Jones pointing out some hypocrisy on my part. Ben writes:

“…did you know that the Axalta coating systems Global Automotive color popularity report that you used to make this viz doesn’t even mention the sample sizes, sampling plan, or methods of their surveys anywhere in the report?”

Ben is completely correct, and I immediately followed my own recommendations for the 100+ errant visualizations from week 1 of Makeover Monday 2017 and added this disclaimer to my dashboard:

Figure 1 -- The disclaimer / warning that now appears on my dashboard.

Figure 1 — The disclaimer / warning that now appears on my dashboard.

So, why did I get in such a huff about folks not vetting their sources for the Australian pay gap but didn’t bother to vet the data for my makeover example?

It comes down to three things:

  1. The number of people participating
  2. The magnitude of the mistake
  3. The importance of the data

For the Australian pay gap makeover there were

ONE — Over 100 people making

TWO — Big mistakes with

Three — “High-stakes” data

Recommendations

Avoid “high-stakes” data sets. Some folks will make the argument that iPhone sales could be considered “high stakes” as someone might make a stock buying or selling decision based on this. Fair enough, but for MM I suggest Andy and Eva avoid data that deals with gender, race, guns, shootings, and anything else that is politically charged.

Get ideas from reputable data sources. Realize that source data from the Australian government is, in fact, good data. The problem is that it isn’t good for making meaningful gender comparisons. So, where did the assertion of giant wage gaps come from? The MM folks cited an article from Women’s Agenda.  I spoke with data journalist Chad Skelton and he suggested that for high-stakes subjects MM should stick with ideas from the likes of The Guardian, The Wall Street Journal, and other well-known entities where reporters confer with statisticians and economists before publishing conclusions.

Note: Jeff Shaffer did some more digging and it appears that Women’s Agenda had in fact republished findings from Business Insider Australia. I have sent e-mails to editors at both publications.

Place a prominent Makeover Monday logo and disclaimer on every dashboard. Since Makeover Monday is an exercise in data visualization redesign, why not have participants trumpet MM’s purpose on every dashboard?  By having a small but prominent logo along with a disclaimer we can both raise awareness of the project and caution people that the purpose of the dashboard is to practice data visualization techniques. My only concern is that people will think this gives them a free pass to present a bogus headline and/or specious findings.

While I think the logo below is spoken for, maybe something like it would help viewers know that for the dashboard in question, it’s not about the data, it’s about the design.

Figure 2 -- Possible logo for Makeover Monday?

Figure 2 — Possible logo for Makeover Monday?

Vetting the data — the birth of “Find the Flaw Friday”?

In the previous post, I indicated that the one hour MM recommends people spend on a makeover is rarely enough time to properly vet the data, let along craft a visualization.  And asking Eva and Andy to vet all the data is completely impractical as I’m sure it’s hard enough to curate the project as it stands now.

Many of the people I corresponded with have said that exploring and really understanding the data is as important, if not more important, than designing a cool dashboard (this gets us back to the substance over style discussion.) A lot of people indicated they find exploring and vetting data more interesting than fashioning visualizations.

So, how can we help people practice analysis and not just design and presentation?

At least five people indicated they would be up for what I will call “Find the Flaw Friday” (FFF) where people are tasked with exploring a data set and determining what analysis would be sound and what would likely to be flawed. I’m not sure how easy this will be to manage and how much time people will need to spend on each project, but I will put the folks that expressed an interest in touch with each other and we’ll see what materializes.

If this works perhaps the data sets from FFF could feed MM?

Final thought — Please fix (or take down) your erroneous dashboards

I consider wage inequality a big deal, and if you are going publish anything that’s about a big deal, you need to get it right.

So, now that people know that their week 1 MM dashboards are wrong, what should they do?

FIX THEM!

In conferring with Chad, he wonders how people would deal with something like this in their jobs.  For example, suppose you create a beautiful visualization for the CEO and realize later that the central point of the viz is deeply flawed?  Even if the mistake weren’t your fault would you rush to correct the record to keep your reputation intact?

Everyone — and I would argue that this should come from the Andy and Eva as well as the folks that manage Tableau Public — should tweet about the errors to make sure people don’t continue to perpetuate the misinformation.

It’s one thing to get something wrong. It’s another thing to know something is wrong and not fix it.

So please, fix it.

Postscript

Tableau Zen Master Brit Cava recommends a fascinating Freakonomics podcast with economist and gender gap expert Claudia Goldin.  See http://freakonomics.com/podcast/the-true-story-of-the-gender-pay-gap-a-new-freakonomics-radio-podcast/.  VERY worthwhile.

 

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Jan 092017
 

By Steve Wexler and Jeffrey Shaffer

January 9, 2017

Please also see follow-up post.

Overview

Makeover Monday, started by Andy Kriebel in 2009 and turned into a weekly social data project by Kriebel and Andy Cotgreave in 2016, is now one of the biggest community endeavors in data visualization. By the end of 2016 there were over 3,000 submissions and 2017 began with record-breaking numbers, with over 100 makeovers in the first week. We are big fans of this project and it’s because of the project’s tremendous success and our love and respect for the two Andys (and now Eva Murray) that we feel compelled to write this post.

Unfortunately, 2017 started off with a truly grand fiasco as over 100 people published findings that cannot be substantiated. In just a few days the MM community has done a lot damage (and if it doesn’t act quickly it will do even more damage.)

What happened

Woah!  That’s quite an indictment. What happened, exactly?

Here’s the article that inspired the Makeover Monday assignment.

So, what’s the problem?

The claims in the article are wrong.  Really, really wrong.

And now, thanks to over 100 well-meaning people, instead of one website that got it really, really wrong there are over 100 tweets, blog posts, and web pages that got it really, really wrong.

It appears that Makeover Monday participants assumed the following about the data and the headline:

  • The data is cited by Makeover Monday so it must be good data.
  • The data comes from the Australian Government so it must be good data that is appropriate for the analysis in question.
  • The headline comes from what appears to be a reputable source, so it must be true.

Some Caveats

Before continuing we want to acknowledge that there is a wage gap in Australia; it just isn’t nearly as pronounced as this article and the makeovers suggest.

The data also looks highly reputable; it’s just not appropriate data for making a useful comparison on wages.

Also, we did not look at all 100+ makeovers. But of the 40 that we did review all of them parroted the findings of the source article.

Some makeover examples

Here are some examples from the 100+ people that created dashboards.

Figure 2 -- A beautiful viz that almost certainly makes bogus claims. Source: https://public.tableau.com/profile/publish/Australias50highestpayingjobsarepayingmensignificantlymore

Figure 2 — A beautiful viz that almost certainly makes bogus claims. Source: https://public.tableau.com/profile/publish/Australias50highestpayingjobsarepayingmensignificantlymore

example2

Figure 3– Another beautiful viz that almost certainly makes bogus claims.  Source: https://public.tableau.com/profile/publish/MM12017/Dashboard1#!/publish-confirm

example3

Figure 4 — A third beautiful viz that almost certainly makes bogus claims.  Source: https://public.tableau.com/profile/publish/AustraliaPayGap_0/Dashboard1#!/publish-confirm

example4

Figure 5 — Yet another beautiful viz that almost certainly makes bogus claims.  Source: https://public.tableau.com/views/GenderDisparityinAustralia/GenderInequality?:embed=y&:display_count=yes&:showVizHome=no#1

Goodness! These dashboards (and the dozens of others that we’ve reviewed) are highlighting a horrible injustice!

[we’re being sarcastic]

Let’s hold off before joining a protest march.

Why these makeovers are wrong

Step back and think for a minute. Over 100 people created a visualization on the gender wage gap and of the dashboards we reviewed, they all visualized, in some form, the difference between male Ophthalmologists earning $552,947 and females that only earned $217,242 (this is the largest gap in the data set.)

Did any of these people ask “Can this be right?”

This should be setting off alarm bells!

There are two BIG factors that make the data we have unusable.

One — The data is based on averages, and without knowing the distributions there’s no way to determine if the data provides an accurate representation.

Here’s a tongue-in-cheek graphic that underscores why averages may not be suited for our comparison.

problems-with-averages

Figure 6 — The danger of using averages.  From Why Not to Trust Statistics.

Here’s another real-world graphic from Ben Jones that compares the salaries of Seattle Seahawks football players.

benjones_salaries

Figure 7 — Seattle Seahawks salary distributions. Source: Ben Jones.

Ben points out

The “average” Seahawks salary this year is $2.8M. If you asked the players on the team whether it’s typical for one of them to make around $3M, they’d say “Hell No!”

Two — The data doesn’t consider part time vs. full time work. The data is from tax returns and doesn’t take into account the number of hours worked.

Let’s see how these two factors work with a “for instance” from the source data.

Figure 8 -- A snippet of the source data in question.

Figure 8 — A snippet of the source data in question.

So, there are 143 women Ophthalmologists making an average of $217K and 423 males making an average of $552K.

Are the women in fact being paid way less?  On average, yes, but suppose the following were the case:

Of the 143 women, 51 work only 25 hours per week

And of those 423 men, 14 of them are making crazy high wages (e.g., one of them is on retainer with the Sultan of Brunei).

Could the 51 part-time workers and the 14 insanely-paid workers exaggerate the gap?

Absolutely.

Is this scenario likely?

About the Sultan of Brunei?  Who knows, but about hours worked?

Very likely.

We did some digging and discovered that as of 2010, 17% of the male workforce in Australia was working part time while 46% of the female workforce was working part time.

This single factor could explain the gap in its entirety.

Note: Not knowing the number of hours worked is only one problem. The data also doesn’t address years of experience, tenure, location, or education, all of which may contribute to the gap.

Findings from other surveys

We did some more digging…

Data from the Workplace Gender Equality Agency (an Australian Government statutory agency) shows that in the Health Care field, 85% of the part-time workers in 2016 were female. This same report shows a 15% pay gap for full-time Health Care employees and only a 1% gap for part-time employees.

Finally, a comprehensive study titled Differences in practice and personal profiles between male and female ophthalmologists, was published in 2005. Key findings from this survey of 254 respondents show:

  • 41% of females worked 40 hours per week compared with 70% for males.
  • 57.5% of females worked part-time compared with 13.6% for males.
  • The average income for females was AUS$ 38,000 less than males, not $335,000 less.
    (Yes, that’s still a big gap, but it’s almost 10 times less than what the article claims).

Why this causes so much damage

It would keep me up at night to think that something I did would lead to somebody saying this:

“Wait!  You think the wage gap here is bad; you should see what it’s like in Australia.  Just the other day I was looking at this really cool infographic…”

So, here we are spreading misinformation. And it appears we did it over 100 times! The visualizations have now been favorited over 500 times, retweeted, and one was featured as the first Tableau Viz of the Day for 2017.

We’re supposed to be the good guys, people that cry foul when we see things like this:

Figure 9 -- Notorious Fox News Misleading Graphic.

Figure 9 — Notorious Fox News Misleading Graphic.

Publishing bogus findings undermines our credibility. It suggests we value style over substance, that we don’t know enough to relentlessly question our data sources, and that we don’t understand when averages work and when they don’t.

It may also make people question everything we publish from now on.

And it desensitizes us to the actual numbers.

Let us explain. There is clearly a gender wage gap in Australia. The Australian government reports the gender wage gap based on total compensation to be around 26% for all industries, 23% for full-time and 15% for full-time health care (base pay is a smaller gap). While we can’t calculate the exact difference for full-time or part-time ophthalmologists (because we only have survey data from 2005), it appears to be less than 15%.

Whatever the number is, it’s far less than the 150% wage gap shown on all the makeovers we reviewed.

And because we’ve reported crazy large amounts, when we see the actual amount — say 15% — instead of protesting a legitimate injustice, people will just shrug because 15% now seems so small.

How to fix this

This is not the first time in MM’s history that questionable data and the lack of proper interrogation has produced erroneous results (see here and here.) The difference is that this time we have more than 100 people publishing what is in fact really, really wrong.

So, how do we, the community, fix this?

  • If you published a dashboard, you should seriously consider publishing a retraction. Many of you have lots of followers, and that’s great. Now tell these followers about this so they don’t spread the misinformation. We suggest adding a prominent disclaimer on your visualization.
  • The good folks at MM recommend that participants should spend no more than one hour working on makeovers. While this is a practical recommendation, you must realize that good work, accurate work, work you can trust, can take much more than one hour. One hour is rarely enough time to vet the data, let alone craft an accurate analysis.
  • Don’t assume that just because Andy and Eva published the data (and shared a headline that too many people mimicked without thinking) that everything about the data and headline is fine and dandy. Specifically:
  • Never trust the data! You should question is ruthlessly:
    • What is the source?
    • Do you trust the source? The source probably isn’t trying to deceive you, but the data presented may not be right for the analysis you wish to conduct.
    • What does the data look like? Is it raw data or aggregations? Is it normalized?
    • If it’s survey data, or a data sample, is it representative of the population? Is the sample size large enough?
    • Does the data pass a reasonableness test?
    • Do not trust somebody else’s conclusions without analyzing their argument.

Remember, the responsibility of the data integrity does not rest solely with the creator or provider of the data. The person performing the analysis needs to take great care in whatever he / she presents.

Alberto Cairo may have expressed it best:

Unfortunately, it is very easy just to get the data and visualize it. I have fallen victim of that drive myself, many times. What is the solution? Avoid designing the graphic. Think about the data first. That’s it.

We realize that the primary purpose of the Makeover Monday project is for the community to learn and we acknowledge that this can be done without verified data. As an example, people are learning Tableau everyday using the Superstore data, data that serves no real-world purpose. However, the community must realize that the MM data sets are real-world data sets, not fake data. If you build stories using incorrect data and faulty assumptions then you contribute to the spread of misinformation

Don’t spread misinformation.

Jeffrey A. Shaffer
Follow on Twitter @HighVizAbility

Steve Wexler
Follow on Twitter @VizBizWiz

Additional reading

Why not trust statistics. Read this to see why the wrong statistic applied the wrong way makes you just plain wrong (thank you, Troy Magennis).

Simpson’s Paradox and UC Berkeley Gender Bias

The Truthful Art by Alberto Cairo.  If everyone would just read this we wouldn’t have to issue mass retractions (you are going to publish a retraction, aren’t you?)

Avoiding Data Pitfalls by Ben Jones. Not yet available, but this looks like a “must read” when it comes out.

Sources:

1. Trend in Hours worked from Australian Labour Market Statistics, Oct 2010.

http://www.abs.gov.au/ausstats/abs@.nsf/featurearticlesbytitle/67AB5016DD143FA6CA2578680014A9D9?OpenDocument

2. Workplace Gender Equality Agency Data Explorer

http://data.wgea.gov.au/industries/1

3. Differences in practice and personal profiles between male and female ophthalmologists, Danesh-Meyer HV1, Deva NC, Ku JY, Carroll SC, Tan YW, Gamble G, 2007.

https://www.ncbi.nlm.nih.gov/pubmed/17539782?dopt=Citation

4. Gender Equity Insights 2016: Inside Australia’s Gender Pay Gap, WGEA Gender Equity Series, 2016.

http://business.curtin.edu.au/wp-content/uploads/sites/5/2016/03/bcec-wgea-gender-pay-equity-insights-report.pdf

5. Will the real gender pay gap please stand up, Rebecca Cassells, 2016.

http://theconversation.com/will-the-real-gender-pay-gap-please-stand-up-64588

Oct 172016
 

Overview

So, you’ve created a wonderful collection of survey data dashboards that have innumerable demographic filters so that users can, for example, just see responses from left-handed Los Angeles Lakers fans between the ages of 34 and 39.

So, what’s the problem?

Actually, there are two problems. The first occurs when extreme filtering reduces the number of responses so much that the results are statistically meaningless. The second is that you may inadvertently allow people to “glean” who has answered a survey.  For example, if you conduct a salary survey you want participants to be assured that nobody will be able to see individual responses. But if you have too many filters it may be possible to winnow down the results so you can guess who provided the answer.

Fortunately, it’s easy to set up a graceful way to suppress a chart and display an error message in its place when the “n” count gets too low.

How it works

Consider the dashboard shown in Figure 1.  Notice that the upper right corner shows that with nothing filtered there are a total of 350 responses.

01_checkall

Figure 1 — A simple dashboard showing results for a check-all-that-apply question.

Also notice there’s a parameter control that allows you to specify the cut-off point for displaying the visualization.

Now let’s see what happens if we use the filters to winnow down the number of responses to the point that there are fewer than 20 (Figure 2.)

Figure 2 -- With too few responses the bar chart is suppressed and the warning message is displayed.

Figure 2 — With too few responses the bar chart is suppressed and the warning message is displayed.

What’s happening here is that there are two floating charts both with similar filters that looks at how many survey responses there are. The bar chart in Figure 1 is set to appear if the number of responses is greater than or equal to 20. The warning message (it’s just a Tableau worksheet) appears if the number of responses is fewer than 20.

How the filters work

Let’s look first at what drives the bar chart (Figure 3.)

Figure 3 -- Pill settings and filters for the bar chart.

Figure 3 — Pill settings and filters for the bar chart.

Notice in particular there is a field called [Minimum Count] that is on Filters card and that it is set to True. The field [Minimum Count] is defined as follows:

Figure 4 -- How [Minimum Count] is defined.

Figure 4 — How [Minimum Count] is defined.

Here [Count Threshold] is the fill-in-the-blank parameter (currently set to 20).

So, the visualization will only appear if there are at least 20 responses; otherwise the filter “kills” the viz and the only thing we see is the title.

Now, how does the secondary visualization work?  Let’s have a look (Figure 5.)

Figure 5 -- Pill and filter settings for the "warning" visualization.

Figure 5 — Pill and filter settings for the “warning” visualization.

Notice that [Minimum Count] is also on the Filter card but is set to False. We’re seeing the viz (the red message) because the filters in place result in fewer than 20 responses.

And just what is producing the message? It’s the field [Too Few] that’s been placed on the Text button on the Marks card. The field is defined as follows.

Figure 6 -- Definition of the field [Too Few].

Figure 6 — Definition of the field [Too Few].

Why use floating elements?

We certainly could cram the two visualizations into a container and make sure that when one is displayed the other only takes up a few pixels.  I elected to go with the floating approach but made sure that the secondary viz was set to be in back of the primary viz.

Is that all there is to it?

For a check-all-that-apply question, that’s all you need to know, but some question and visualization types may need different approaches.

Consider Figure 7 where we see a jitterplot comparing salary data for men and women where each dot represents a response from an individual survey participant.

Figure 7 -- Pill and filter configuration for a jitterplot visualizing salary data.

Figure 7 — Pill and filter configuration for a jitterplot visualizing salary data.

Notice that we have a different field (one that uses a Table calculation) on the Filters card.

There’s also a very different setup to display the warning message for this visualization as we cannot simply base this on SUM([Number of Records]).

I will leave it to the reader to explore how this these are set up.  Just let me know when you come up with a better approach.

Conclusion

If you’re visualizing survey data and giving users filters you should come up with a game plan for what to do if there are too few responses. In this blog post (and the embedded, downloadable workbook) I present two approaches for two types of survey questions. Other questions types (for example, Likert-scale questions) will need some modifications to what I’ve presented here.

Sep 072016
 

Overview

TruthfulArtImagine a terrific introductory college course presented by a terrific professor.

That’s the feeling I had in reading The Truthful Art, Alberto Cairo’s follow up to his first book The Functional Art.

Whereas his first book took a “look at what you can and should do” approach to help people see and understand data, The Truthful Art is more of a “here’s what you need to know” if you want to be a data journalist — and there’s a lot of things you need to know if you are going to do a proper job.

I’m reluctant to use the term “data journalism” as Cairo’s book is for anyone that that is tasked with helping people make sense of data. The difference is that the data journalist’s work is likely to be public and yours may only be seen by people working in your organization. But while you may not have to make a dashboard that is as polished as an infographic from the New York Times, both you and the data journalist need to adhere to a particular doctrine and have sufficient skills across a wide range of topics if you are going to build functional, truthful, and meaningful visualizations.

First, Be Truthful

If the credo for doctors is to “first, do no harm” Cairo might argue that the credo for data journalists is to “first, be truthful.” Cairo makes the case that a good visualization must be

  • Truthful
  • Functional
  • Beautiful
  • Insightful
  • Enlightening

And it must be these things in this order of priority. That is, the visualization must first be “relevant, factual, and accurate” and only then should it be “accessible and engaging.” Cairo further states that “honesty, clarity, and depth come first.” Indeed, this is why he bristles with outrage over deceitful graphics like this one.

So, how, exactly, does one create something that is truthful, functional, beautiful, insightful, and enlightening?

By achieving a sufficient level of competence in a LOT of different areas.

And just what are those areas?

The Data Journalism Landscape

In reading The Truthful Art you may feel like you are in a helicopter several thousand feet above the data visualization landscape. In each section Cairo, as expert guide, will gently descend to several hundred feet above a particular area and allow you to examine varied topics including design, statistics, color, storytelling, psychology, and ethics. While the book never gets deep into any of these subjects Cairo does provide excellent resources for anyone interested in exploring a particular topic in depth as every chapter of the book ends with a section titled “To Learn More.”

While Cairo’s writing is disarmingly warm and engaging he takes the responsibility of data storytelling very seriously. By the end of the book you will have an excellent understanding of the investment needed to make a worthwhile contribution to your company, society, or both.

Conclusion

Whether you are new to the field or have been practicing for years, I’m confident you’ll find The Truthful Art, like its predecessor, to be fun, elucidating, and inspiring.

The Truthful Art

Paperback: 400 pages

Publisher: New Riders; 1 edition (February 28, 2016)

Aug 112016
 

Overview

As readers of this blog know, I have my problems with donut charts.

That said, I acknowledge that they can be cool and, under certain circumstances, enormously useful.

On a recent flight I was struck by how much I liked the animated “estimated time to arrival” donut chart that appeared on my personal TV screen. An example of such a chart is shown in Figure 1.

Figure 1 -- Donut chart showing progress towards completion of a flight.

Figure 1 — Donut chart showing progress towards completion of a flight.

I find this image very attractive and very easy to understand — I can see that I’m almost three-quarters of the way to my destination and that there are only 49 minutes left to the flight.

So, given how clear and cool this is, why not use them on a dashboard?  And if one is good, why not use lots of them?

It’s the “more than one” situation that may lead to problems.

Trying to make comparisons with donut charts

The flight status chart works because it shows only one thing only: a single item’s progress towards a goal.

Let’s see what happens when we want to compare more than one item.

Consider the chart in Figure 2 that shows the placement rates for Fremontia Academy.

Figure 2 -- Donut chart showing placement percentage.

Figure 2 — Donut chart showing placement percentage.

A 95% placement percentage is really impressive.  Is that better than other institutions?  If so, how much better is it?

Figure 3 shows a comparison among three different institutions using three different donut charts.

03_3Donuts

Figure 3  — Three donut charts displaying placement percentages for three different institutions.

Before digging deeper let’s replace the three separate donuts with a donut-within-a-donut-within-a donut chart (Figure 4.)

Figure 4  -- A concentric donut chart (also called a “radial bar chart” or a “pie gauge.”)

Figure 4  — A concentric donut chart (also called a “radial bar chart” or a “pie gauge.”)

“What’s the problem?” you may ask, “these comparisons are easy.”

While you may be able to make the comparisons you are in fact working consierably harder than you need to be.

Really.  Let me prove it to you.

Let’s suppose you wanted to compare the heights of three famous buildings: One World Trade Center, The Empire State Building, and The Chrysler Building (Figure 5).

Figure 5  -- Comparing the size (in feet) of three large buildings.

Figure 5  — Comparing the size (in feet) of three large buildings.

Now that’s an easy comparison. With virtually no effort we can see that One World Trade Center (blue) is almost twice as tall as The Chrysler Building (red).

Now let’s see how easy the comparison is with donuts (Figure 6.)

Figure 6  -- Three large buildings twisted into semi-circles.

Figure 6  — Three large buildings twisted into semi-circles.

Here are the same buildings rendered using a concentric donut chart (Figure 7).

Figure 7  -- Three skyscrapers spooning.

Figure 7  — Three skyscrapers spooning.

Yikes.

So, with this somewhat contrived but hopefully memorable  example we took something that was simple to compare (the silhouettes of buildings) and contorted them into difficult-to-compare semi-circles.

With this in mind, let’s revisit the Placement example we saw in Figure 3.

Here is the same data rendered using a bar chart.

Figure 8 -- Placement percentage comparison using a bar chart.

Figure 8 — Placement percentage comparison using a bar chart.

The comparison is much easier with the bars than with the donuts / semi-circles. You can tell with practically no effort that the blue bar is approximately twice as long as the red bar, even without looking at the numbers.

Indeed, that’s a really good test of how clear your visualization is: can you compare magnitude if the numbers are hidden?

Pop quiz — how much larger is the orange segment compared to the red segment?

Figure 9 -- Trying to compare the length of donut segments is difficult.

Figure 9 — Trying to compare the length of donut segments is difficult.

Now try to answer the same question with a “boring” bar chart.

Figure 10 -- Comparing the length of bars is easy.

Figure 10 — Comparing the length of bars is easy.

With the circle segments you are squinting and guessing while with the bars you know immediately: the orange bar is twice as large as the red bar.

More downsides for donuts

In addition to comparisons being difficult, how would you handle a situation where you exceeded a goal?  For example, how do you show a salesperson beating his / her quota?  With a bar chart you can show the bar going beyond the goal line (Figure 11).

Figure 11 -- With a bar chart it's easy to show more than 100% of goal.

Figure 11 — With a bar chart it’s easy to show more than 100% of goal.

How do you show this with a donut chart?

Rhetorical question.  You can’t.

Conclusion

If you only have to show progress towards a single goal and don’t need to make a comparison then it’s fine to use a donut chart. If you need anything more complex you should use a bar chart as it will be much easier for you and your users to understand the data.

Special thanks to Eric Kim for creating the building images.

 

Jun 222016
 

Overview

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.

Apr 112016
 

Overview

This past week I enjoyed looking at and interacting with Matt Chambers’ car color popularity bump chart.

 Figure 1 -- Matt Chambers' car color popularity bump chart.

Figure 1 — Matt Chambers’ car color popularity bump chart.  You can find the original Datagraver visualization upon which this was based here.

The key to this dashboard is interactivity as it’s hard to parse all the car colors at once. If you hover over one at a time it’s easy follow the trends, as shown here.

Figure 2 -- Hovering over a color shows you that color’s ranking over time

Figure 2 — Hovering over a color shows you that color’s ranking over time

Showing Rank Only

Over the past few months I’ve seen a lot of people making bump charts (myself included). As much as I like them I fear that people are leaving some critical insights out of the discussion as bump charts only show ordinal information and not cardinal information. That is, they show rank but not magnitude.

Consider the bump chart above.  In 2009 White was the number one color, Black was number two, and Red was a distant sixth.

Figure 3 -- Red appears to be a distant sixth

Figure 3 — Red appears to be a distant sixth

But was Red in fact “distant” or its popularity closer than it would appear?  When you just show rank there’s no easy way to tell.

Showing Rank and Magnitude

Consider the dashboard below that shows the overall ranking and percentage popularity for car colors over the last ten years.

Figure 4 -- Ranked Bar Chart dashboard with no colors selected

Figure 4 — Ranked Bar Chart dashboard with no colors selected

Right now we can see that over the last ten years white came in first place with 22% and Red came in fifth place with 11%.  Now let’s see what happens if we select red and white, as shown below.

Figure 5 -- Comparing popularity of white and red car over the last ten years.

Figure 5 — Comparing popularity of white and red car over the last ten years.

Here we can see everything that the bump chart had plus so much more. Specifically, we can see that White was in first place for the past ten years and that Red was as high as fourth place in 2007 and as low as sixth place in 2008 and 2009. But we can also see that in 2009 White was only 50% larger than Red while in 2015 it was almost 150% larger!

Try it yourself

Click here to interact with the color popularity ranked bar chart.

Ranked Bars are Versatile

The ranked bar approach works well showing rank and magnitude over time and across different categories.

Consider the dashboard below that shows the sales for the top 20 products overall and then a ranked breakdown by one of three possible categories (Customer Segment, Region, and Year)

Figure 6 -- Overall sales / rank and sales / rank broken down by Customer Segment.

Figure 6 — Overall sales / rank and sales / rank broken down by Customer Segment.

Here we can see not only how the Bosch Full Integrated Dishwasher is ranked overall and within the four Customer Segments, but we can also see how much more and less the other products’ sales were.

Here’s the same dashboard showing a breakdown by Region.

Figure 7 -- Overall sales / rank and sales / rank broken down by Region.

Figure 7 — Overall sales / rank and sales / rank broken down by Region.

The Bosch Dishwasher is fifth overall but it isn’t even in the Top 20 in the South.  We can also see that it is Second in the East, ever-so-slightly behind the first ranked product (the Whirlpool Upright Freezer.  You can see for yourself when you interact with the dashboard that’s at the end of the post).

Here’s the same data but presented using a bump chart.

Figure 8 -- Overall sales / rank and just rank by Region.

Figure 8 — Overall sales / rank and just rank by Region.

The bump chart looks cool but we only get part of the story as I can only glean rank.

Conclusion

The bump chart is a great choice if you want to show “soft” rankings, such as what place a team came in over time, but if you want to show rank and magnitude, consider the ranked bar chart instead.

Note: for step-by-step instructions on how to build a dashboard like the one below, see Visual Ranking within a Category.

The Ranked Bar Dashboard — Kick The Tires

Mar 302016
 

Some thoughts on functionality, beauty, crown molding, and lollipop charts

Overview

I’ve been writing a book about business dashboards with Jeffrey Shaffer and Andy Cotgreave and we’ve conducted screen-sharing sessions with dozens of people and reviewed scores of dashboards. We had a particularly enjoyable jam session with Tableau Zen Master Mark Jackson last week. When we asked him why he had done something in particular he replied with a comment that has been haunting me (in a good way) ever since:

“I look at this dashboard first thing every morning. I want to look at something beautiful.”

This really resonated with me. Mark was not tasked with making a public-facing dashboard that had to compete with USA Today infographics. He just wanted to make something that was both functional and beautiful. It made me think of waking up in a lovely room with crown molding. You don’t need crown molding, but as long as it isn’t blocking sunlight or clashing with the decor it’s certainly delightful to have crown molding.

This got me thinking about a topic I come back to often — how to make visualizations that are both functional and beautiful.

Unfortunately, this isn’t so easy and often leads to people sacrificing clarity for the sake of coolitude (see “Balancing Accuracy, Engagement, and Tone” and “It’s Your Data, not the Viz, That’s Boring” for some more thoughts on the matter).  I did, however, want to share a case study that had a delightful outcome and that employed a chart type that combines the accuracy of a bar chart with a bit of the “oooh” from packed bubbles and “ahhh” from donut charts.

Marist Poll and Views of the 2016 Presidential Election

Marist Poll is one of my clients and they are tasked with providing nationwide survey results to The Wall Street Journal and NBC News.  In November 2015 they conducted a poll asking people to describe in one word the tone of the 2016 presidential election. Here were the results.

Figure 1 -- Marist Poll results in tabular form

Figure 1 — Marist Poll results in tabular form

Attempt One — Word Cloud

The results from the poll are very compelling but the results as depicted in the text table don’t exactly pop.

The client tried a word cloud as shown below.

Figure 2 -- Marist Poll results using a word cloud

Figure 2 — Marist Poll results using a word cloud

I’ll admit that the graphic “pops” but it’s hard to make sense of the six terms let alone discern that the results for “Crazy” were almost three times greater than the next most popular term.

Attempt Two — Packed Bubbles

People love circles and this chart certainly looks “cool” but what does it tell other than that the “Crazy” circle is larger than the other circles?

Figure 3 -- Marist Poll results with packed bubbles

Figure 3 — Marist Poll results using packed bubbles

Why not use a simple bar chart?

Attempt Three — A Simple Bar Chart

Here are the same results rendered using the chart type Tableau’s “Show Me” suggests you use when working with this type of data.

Figure 4 -- Marist Poll results using a bar chart

Figure 4 — Marist Poll results using a bar chart

This is a big improvement over the word cloud and packed bubbles with respect to clarity — you can easily sort the responses and see how much larger “Crazy” is than the other responses.

But the chart is a bit sterile. What can we do to make the “Crazy” pop out without distorting the information?

Attempt Four — A Colored Bar Chart

The major takeaway from the poll is that 40% of the respondents characterized the election as “Crazy.” We can make that easier to glean by making that bar a bold color and all the other bars muted, as shown here.

Figure 5 -- Marist Poll results using a bar chart with one bar colored differently

Figure 5 — Marist Poll results using a bar chart with one bar colored differently

I’ll confess that this does the trick for me, but the client wanted to pursue some other options so we looked into a lollipop chart.

Attempt Five — Lollipop Chart

The lollipop chart is not native to Tableau;  it’s simply a dual axis chart that superimposes a circle chart on top of a bar chart that has very thin bars.

Figure 6 -- Marist Poll results as a lollipop chart

Figure 6 — Marist Poll results using a lollipop chart

This strikes me as an excellent compromise between the analytical integrity of the bar chart and the “ooh… circles” appeal of the packed bubbles.  I have no qualms about using this chart type.

But there’s still something if we want the chart to have some impact.

Final Attempt — Adding a Compelling Title

A concise, descriptive title can make a huge difference in garnering attention and making a chart more memorable. In the example below the client added some graphic design artistry to the typography to make the title compelling.

Figure 7 -- Marist Poll results as a lollipop chart with compelling headline.  I love this.

Figure 7 — Marist Poll results using a lollipop chart with compelling headline.  I love this.

Conclusion

My bass-playing friends will probably agree that “groove” is more important than “chops.”  That is, being able to play “in the pocket” with a rock-steady beat is more important than being able to play a great solo with a flurry of notes all over the neck.

But it sure is great to be able to do both.

The same goes for data visualization. Functionality needs to come first, then beauty.

But it sure is great to have both.

And in many cases, with a little extra effort, you can have both.

So go ahead, try putting some “crown molding” into your data visualizations and delight yourself and your stakeholders.

 

Mar 172016
 

Overview

I’m a big fan of Andy Kriebel’s and Andy Cotgreave’s Makeover Monday challenge. For those of you not familiar with this, each week Kriebel and Cotgreave find an existing visualization / data set and ask the data visualization community to come up with alternative ways to present the same data.

As Cotgreave points out in one of his blog posts “It’s about using a tool to debate data. It’s about improving people’s data literacy.”

With one major exception that I’ll discuss in a moment the challenge is meeting its goals as each week several dozen people participate and the submissions and accompanying discussions have been enormously valuable.

But there was one week where the community failed.

Worse than that, the community did some damage that will be difficult to repair.

Bad Data Make Bad Vizzes Make Bogus Conclusions

Week four of the Makeover Monday challenge used survey data from GOBankingRates that posed the question “how much money do you have saved in your savings account?” Here are some of the baseless conclusions from people that participated in the makeover:

Figure 1

Figure 1 — From the source article that spawned the makeover.  Yes, the exploding donut needs a makeover, but it’s the headline “Survey finds that two-thirds of Americans don’t have enough money saved” that presents the bigger problem.

  • Americans Don’t Have Enough Money Saved (See link).
  • 71% of Americans Have Less than $1,000 in Savings. Yikes! (See link).
  • Americans Just Aren’t Saving Money (See link).
  • Most Americans Have Miniscule Savings (See link).
  • 80% of Americans Have Less than $5,000 in Savings! (See link).
  • Americans Are Not Saving Enough Money! (See link).
  • Americans Have Too Little Savings (See link).

So, what’s the problem?

It turns out the key finding from the original publication is not true — and thanks to the makeovers that spurious finding has been amplified dozens of times.

How did this happen?

Let’s dig into the data a little bit.

Is There a Relationship Between Age and Savings?

As I mentioned before I think the Monday Makeover Challenge is great and I’ve participated in a couple of them. I started to work on this one and took a stab at showing the relationship between age and savings, as shown here.

Figure 2 -- Divergent stacked bar chart showing the percentage of people that have different savings amount, sorted by age

Figure 2 — Divergent stacked bar chart showing the percentage of people that have different savings amounts, sorted by age

This looked odd to me as I expected to see a correlation between age and savings; that is, I expected to see a lot more blue among Seniors and Baby Boomers.

I decided to make the demarcations less granular and just compare people with minimal savings and those with $1,000 or more in savings, as shown here.

Figure 3 — Less granular divergent stacked bar chart

This result seemed way off, so either my supposition is wildly incorrect (i.e., as people get older they save more) or there was something wrong with the data.

Note: I try to remind people that Tableau isn’t just for reporting interesting findings. It’s a remarkably useful tool for finding flaws in the data.

It turns out that while there is indeed something wrong with the data, there was a much bigger problem:

Most people didn’t bother to look at the actual question the survey asked.

What the Survey Asked

The survey asked “How much money do you have saved in your savings account?”  It did not ask “How much money do you have saved?

The difference is titanic as the average American savings account yields but .06 percent interest!  That’s infinitesimal — you might as well stick your money in a mattress!

Indeed, I am of the Baby Boomer generation and I have but $20 in my savings account — but (thankfully) more in my savings.

So, the vast majority of people that participated in the makeover didn’t bother to look at the actual question and came to — and published — a bogus conclusion.

Were there any other problems with the survey?

You betcha.

What’s Wrong with the Survey?

A visualization is only as good as its underlying data and the data in question has nothing to do with the savings habits of Americans; it only has to do with having a savings account.

But there are other shortcomings with the survey that should make us question whether the data is even useful for analyzing how much money Americans have sitting in a savings account.

Consider this excellent review of the same Makeover Monday challenge from Christophe Cariou.  He points out the following shortcomings with the survey itself:

  • In the article, we read: ‘The responses are representative of the U.S. internet population’. It is therefore not representative of the US population. See this report by Pew Research Center for age and online access.
  • We also read ‘Demographic information was not available for all respondents, and analysis of responses by demographics is based solely on responses for which the targeted demographic information was available.’ Normally, if it was demographically representative, this would be clarified. This comment adds a doubt.
  • The average savings amount in the article is the sum of the averages of the groups divided by 6. It is not weighted by the size of each group.

Note: Kudos to Bridget Cogley who also saw the problems with the conclusions when the makeovers first appeared in late January 2016.

Further note: In a subsequent makeover challenge blog post Cotgreave alerted participants to questionable data.

So, Where Exactly is the Harm?

So, dozens of people have created visualizations based on bad data and came up with bogus conclusions. Given the number of articles from allegedly reliable sources reporting shortcomings in savings, what’s the harm of sounding an alarm bell?

I suppose if you are an “ends justify the means” type of person then it’s fine to publish bogus findings as long as they change behavior in a positive way.

But I know many of the people in this community and they would be aghast at using data visualization this way.

I also fear that with collective missteps like this people will question the ability of makeover participants to relay accurate information.

So What Should We Do?

Andy Cotgreave and Andy Kriebel have earned their leadership positions in the data visualization community, so I hope they will make note of this makeover mishap and encourage people that published the bogus result to modify their headlines.

I also strongly encourage anyone working in data visualization to understand the data — warts and all — before rushing to publish. Andy Kriebel is providing the data set and we shouldn’t ask him to find all the flaws in it.  Indeed, that’s part of our job.

Finally, I ask others in the community to be more diligent: only publish work that has been carefully vetted and do not tolerate unsubstantiated work from others.

While it’s true that nothing terrible will happen if more Americans open savings accounts, there may be other situations where publishing spurious conclusions will do some serious damage.

Dec 092015
 

Overview

In an October 2015 meeting of the Tableau Zen Masters each Zen Master was asked to name his / her favorite thing about Tableau.  Most people started by saying “well, besides the community, my favorite thing is…”

At the time I said “undo”.  Don’t laugh.  Let’s not take it for granted that with Tableau we can try things, fail, and go back to where we were before we failed… gracefully.

After the meeting I thought more about this question and how there are unheralded aspects of the tool and the ecosystem that I count on but that don’t get the attention they deserve.  I realized that there is one thing in particular that I use almost every day and without it I, and scores of others, would be at a major loss.

Tableau Public.

What is Tableau Public

Tableau Public consists of a free downloadable version of Tableau desktop to explore and visualize data, and a free cloud platform to host, share and embed interactive visualizations.

While I use my regular copy of Tableau desktop to explore data and fashion visualizations, it’s the free hosted version of Tableau Server (the cloud platform) that allows me to do so much and to learn so much.

Sharing

My website consists mostly of examples and blog posts and most of those posts contain Tableau dashboards that are embedded right inside the post.  I don’t have to ask people to download a reader and then download my dashboards.  The dashboards are right there.

Learning

But it gets better. Anyone with a copy of Tableau who is curious about how the viz works can just download the workbook, open it up, and see how it’s put together.

For example, a couple of years ago I wanted to see if anyone had recreated Hans Rosling’s famous Gap Minder demo in Tableau.  A quick Google search lead me to this example from Jeffrey Shaffer.

Figure 1 -- Jeffrey Shaffer recreates Rosling's Gap Minder.  See http://public.tableau.com/profile/jeffs8297#!/vizhome/shared/FQWYZ95DJ

Figure 1 — Jeffrey Shaffer recreates Rosling’s Gap Minder.  Click here.

I was curious to see how Jeff had gotten the year to display as big block letters in the middle of the viz, so I downloaded the workbook and “looked under the hood.”

Over the years I’ve downloaded hundreds of workbooks and have analyzed the individual brush strokes of Kelly Martin, Andy Cotgreave, Ben Jones, Anya A’Hearn, and dozens of others.  That I can do this so easily  is nothing short of amazing.  I don’t just get to interact with cool vizzes; I can download them and see how they work.

Adding to the conversation

Because the vast majority of Tableau Public authors do make their work downloadable, you’ll see people modify and repost the work they’ve downloaded. Andy Cotgreave wrote about this in late 2014 where one way of visualizing something begat other ways of visualizing the same data.  Here’s a particularly beautiful example from Michal Mixon.

Figure 2 -- A stunning dashboard from Michael Mixon that you can download from here.

Figure 2 — A stunning dashboard from Michael Mixon that you can download from here.

It Isn’t Perfect

Before you accuse me of writing a hagiography, there are several shortcomings with Tableau Public.

Row limit

You can only have 100,000 rowsNo, now you can only have 1,000,000 rows.  Okay, as of May of 2015 you can have 10 million rows of data.  Not really a shortcoming.

Locking down your data

Your workbooks and the underlying data can be downloaded and examined.  As of May of 2015 you can “lock down” your workbooks and your data.  This is huge as it allows public-serving organizations with proprietary data to publish interactive workbooks without fear that somebody will download and examine the underlying data.

That said…

Unless you indeed have proprietary data please, please, please don’t stop your workbooks from being downloaded.

Figure 3 -- This setting is on by default.  Please don’t turn it off unless you have a good reason (e.g., the underlying data is proprietary).

Figure 3 — This setting is on by default.  Please don’t turn it off unless you have a good reason (e.g., the underlying data is proprietary).

Tableau and the community that supports it are giving you this amazing free platform for you to showcase your work.  Please allow others to benefit by making your work downloadable.

Just because something is anointed a Tableau Public “Viz of the Day” does not mean it is a good viz

Many Viz of the Day selections are examples you should emulate, but I’ve seen some really bad vizzes that made the cut because the subject matter was “discussion-worthy”.  The problem is that those who are new to data visualization won’t know that these vizzes aren’t worth emulating.  They’ll just think “hmm, that viz with the word cloud and donut charts was a ‘viz of the day.’  I guess it’s a good thing to make vizzes with word clouds and donut charts.”

As Tableau comes out with new versions they tinker and break things

I understand that Tableau Public is an evolving platform and that Tableau wants to improve that platform by adding new features. The problem is that I’ve had many dashboards that suddenly stops working. Tableau has been great at responding to notices that things are broken, but sometimes it can take hours, if not days, to fix. So…

Favor: Please add my website to your test suite. In other works, do not implement any new features until you’re sure everything on my site works perfectly.

Okay, okay, okay… but it doesn’t hurt to ask.

Things I cannot do without

Here’s a summary of the things I cannot do without in my practice.

I would not be able to do what I do without Tableau Desktop.

I would not be able to do what I do without the community that supports Tableau.

And I would not be able to do what I do without Tableau Public.

My sincere thanks to the Tableau Public team.

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