Tableau European Conference – Freakalytics update Day 2

This week Tableau are holding their inaugral European customer conference in Amsterdam. I’m delighted that Stephen and Eileen McDaniel of Freakalytics have provided key updates from the event and have kindly allowed me to share their observations.

On Tuesday I published the headlines from Day 1, published below are updates from Day 2′s main event. These observations are a direct lift from the Freakalytics ‘Thoughts’ page which published these updates straight from the conference floor.

Please note, some comments are the opinion of Freakalytics and not necessarily those of Tableau. This content was live blogged; there may be occasional errors or omissions.


Day 2 Schedule (note many are concurrent sessions)

8:30 to 9:30 | The Disinformation Age: Technology is Making us Stupid

with Stephen Few, Perceptual Edge


There is no way to easily weave data into knowledge…
Are we enlightening our audience or frustrating our audience?
Data and the use of data are the sexy jobs of the next decade

A video with Hal Varian at UC- Berkeley and Google was shown in an interview

  • Managers should be able to examine the data themselves
  • Old organizations required armies of people for leaders to see the data
  • Instead, we should enable access to the data for everyone

Pie charts by Homer, the real reason everyone loves pie, it isn’t the ability to see the data…


Skills and tools

We must have good skills and good tools to achieve the possibility of data enlightenment

Bad tools can imprison us- so many tools seem to assume that we are dumb and we just want entertainment.

A plethora of flashy, uninformative and even misleading charts available from many companies

The Business Intelligence industry has delivered many of the tools to date
BI SUCCESS! in collecting data, cleaning data, transforming data, integrating data, storing massive amounts of data and reporting on data

BUT, traditional BI has hit the wall- we can’t explore our data, easily analyze our data, clearly communicate our findings or easily use it to predict the future

Traditional BI is very engineering and feature oriented
NEW BI needs to be much more human-centric and design-oriented. We must understand how people see, perceive and use data to effectively serve them in their quest for better decisions.


Data visualization

Data visualization is powerful because it weaves numbers into information

In the 1700’s, William Playfair invented the line, bar and pie chart. He had a bad day when he invented the pie chart! Well, 2 out of 3 innovations that are useful isn’t a bad record!

Many of the new, shiny graphs are much worse than traditional, simplistic graphs at explaining the situation. This is because they misdirect, mislead and often can’t inform us.

Stephen then showed a clip from “The Onion“- concentric circles hitting misshapen areas.  Parody on earthquake reporting.  Lots of talk and graphs with no insights about what is actually happening in the earthquake location.

Stephen then showed a Fox News example showing supporters of various Republican nominees adding up to 190% of audience!

When you add up the slices in this pie chart, you will find that 193% of the electorate was polled!


The process

Search, discovery, examination, understanding and making decisions. Stephen calls this Search, Examine and Explain or “SEE”

Note that vision is often our dominant sense- “I see!” 70% of sense receptors are in the retinas of your eyes.

Trends, patterns, outliers- a picture makes it stand out. Can easily see patterns such as seasonality (domestic), overall trends (domestic up and international flat) and exceptional outcomes (international in August).

We should attempt to balance thinking with visual power- traditional methods put much of our burden on thinking, but evolution has made the visual system exceptional over the thinking part of our intellect.


Brick wall is uninformative, reflective light on pie chart is deceptive, minimal value from pie chart with two categories

Sexy graphs are fun but often not useful or even misleading! DON”T bury the truth under layers of makeup, but rather choose simplicity in your graphs to inform.

A quote from Edward Tufte, “Above all else, show the data!“ Tufte argued that data ink should be high relative to non-data ink

  1. Reduce non-data ink
  2. Enhance the data ink

The objective we should strive for is to make the situation clear and simple.

An example, avoid distracting displays in your presentations.  Reflections in graphs are a great example of wasted data ink.  In the real world, reflections in the outdoors are something we find annoying!  Why did developers build it into graph tools!?!?

Stephen then showed 3-D bar charts shown that make it nearly impossible to read.  It was a graph from major BI vendor documentation manual.  They have added a third dimension when it had no meaning or purpose except to confuse.

Avoid visual puzzles- this is not a game, we are trying to make the best decision.  Decisions could involve the future of your career, your company, your bonus or even people’s lives.

Save the pies for dessert, not your presentation.  Also, pies on a map are useful since they are self-contained and bars or lines are not self-contained.

Very bad bar chart with unneeded third dimension



Referred to his latest book, “Now You See It

We must bridge the gap between data and knowledge which should be built on an understanding of how we see and how we think.

What is the question?  Organize the data appropriately.

Stephen showed a simple example demonstrating that visual perception is not just camera work. Your eyes do NOT work like cameras!  The CONTEXT influences our perception; data with poor context is misinterpreted quite easily.

A 2nd example- gradient of fill colors misleads you in bar and line charts. Then showed dots versus dots connected by lines (budget versus actual data.)

A good example showing how it is hard to read more than a few values in the table but easy to compare the two series as lines. With the lines you can see the overall, upward trend in domestic traffic, the lack of trend in international traffic, the seasonality of domestic traffic and the exceptionally low results in international traffic in August.

Some techniques that tools should effortlessly enable include

  • sorting
  • filtering
  • add/remove items
  • highlighting
  • aggregating/disaggregating
  • drilling
  • grouping
  • zoom/pan
  • revisualize
  • re-express and
  • rescaling the data.


Visual analysis at the speed of thought

See-> Think -> Modify iteration again See -> Think -> Modify and so on- the flow of thinking that leads new discovery and insights.

To achieve this we must eliminate distraction and augment our limited working memory.

An example for the University of British Columbia Visual Cognition Lab- we are easily distracted!  Too much noise exists in our world of visual analysis due to poor software design.

How it works, from the World -> Working memory -> Long-term memory

Imagination can also feed into the working memory

We can only hold so much information in our working memory, 3-4 chunks of information based on extensive research since the 1950’s.

There are visual aids for working memory, so we can quickly see and understand a lot at once to aid our limited working memory. An example,

1 data point = 1 chunk
BUT one line with 24 data points = 1 chunk in working memory, suddenly you can see and easily compare 5 regions across two years instead of 5 regions for one month!

Another example, the story of three blind men and the elephant- tree trunk, snake whipping around, like a huge fan.  One felt the leg, the tail and the trunk.  They could only see a small amount.

Unfortunately, many data analysts are like the blind men.  They have only been trained in a limited, directed way –OR– their tools impede their ability to explore and understand the data!


Where should be headed?

Information -> Knowledge -> Wisdom -> Which leads to a better world and life

Our ultimate goal is not knowledge, but rather wisdom.  To make better decisions in the world.

Stephen then closed with a poem by TS Elliott.


Mike NealeyMay 12th, 2011 at 1:51 pm

At the top of this page, you write that Tableau “…are holding there inaugural European customer conference…”
It should be “their”.
Visualising data is important – as is language.

Mike NealeyMay 12th, 2011 at 1:52 pm

p.s. thanks for a great visualisation site though.

Andy KirkMay 12th, 2011 at 2:33 pm

You’re absolutely right Mike, thanks for the close reading and the feedback!
Now corrected – their should be no more… :)

Francois MercierMay 12th, 2011 at 5:25 pm

Thanks a lot Andy for this post. I really enjoy reading your blog. I also enjoy reading Stephen Fews’ blog and books and I totally support the idea that better ‘data visualization’ is needed.

However … I would like to bring some ‘insider’ view from one of these ‘old organizations which requires armies of people for leaders to see the data’.
I find the idea that ‘Managers should be able to examine the data themselves’ a bit naive.

Take the pharma industry for instance. Big nest of data.

Terabytes of data are collected to evaluate efficacy and safety of new medicine and hundreds of statisticians/programmers are required to analyze these data. Do you really believe that Managers (better to say Decision Makers) could spend their time in looking at these data by themselves ?

In a typical phase 2 clinical trial, as a rule of thumb, may be 100 response variables are examined, over, say, 6 months (~10 assessment time points), in, say, 100 patients = 100,000 data points.
These data points are usually reported in ‘clinical study reports’ as listings, tables and figures. All these representations needs to be programmed (scripts) so that it can be reproduced.
Also, response variables are compared the one to the others, which multiplies the number of ways you can display data.

This translates into thousands of exploratory graphics !
You may say: “This is crazy. If I need thousands of graphics, it means that the question is not clear.”

Wrong. Thousands of graphics are required, because
- this is about data “exploration” or “signal detection”; for instance, you may expect to see a signal in response variable Y1, but you may not know how Y1 will affect variables Y2; you need to explore !.
- medicine is not a “hard” science, there is uncertainty, variability (within and between patients), and we still know very little of the human body !
- and finally, yes, many questions are at stake !

Now, repeat this exercise for 100 clinical trials run simultaneously and you start understanding why you need armies of people to SEE the data.

Andy KirkMay 12th, 2011 at 8:16 pm

Many thanks for your comments Francois, very interesting and helpful insights from the alternate perspective.