Promising difficulties

At the recent VisWeek conference, Jessica Hullman and her coauthors presented “Benefitting Infovis with Visual Difficulties (pdf)”, a paper that suggests that the charts which are read almost effortlessly are not necessarily the ones that readers understand or remember best. To answer that claim, Stephen Few wrote a rather harsh critique of this paper (pdf). As I read this I felt the original paper was not always fairly represented, but more importantly, that the views develop by both parties are not at all inreconcilable. Let me explain.

What is cognitive efficiency, or “say it with bar charts”

For quite some time, we were told that to better communicate with data, we had to make visuals as clear as possible.

The more complicated way of saying that is talking of “cognitive efficiency”. By reducing the number of tasks needed to understand a chart and simplifying them, which is sometimes called reducing the “cognitive cost” or “cognitive load”, we improve all virtues of the chart.

Various charts based on the same data points, shown in order of cognitive cost

Various charts based on the same data points. From left to right, they make the task of comparing individuals value increasingly easier

For instance: bar charts are easier to process than pie charts, because it’s easier for the human eye to compare lengths than angles. So, with equivalent data, bar charts have a lower cognitive cost than pie charts. Likewise, bar charts which are ordered by value (smallest bars to largest bars) are easier to read than unordered ones. Ordered bar charts have an even lower cognitive cost than unordered ones.

Conversely, adding non-data elements add extra tasks for the reader and increase cognitive cost. These non-data elements have been reviled by Edward Tufte as “chartjunk”. His data-ink theory says that out of all the ink used for the chart, as much as possible should be devoted to data elements. Again, this goes in the direction of data efficiency.

Engagement rather than immediacy?

Again for quite some times those rules were held to be universal. Yet, several tried to challenge them, the latest being Jessica Hullman in her paper “Benefitting Infovis with Visual Difficulties“. This paper was so thought-provoking that it received an honorable mention at the recent IEEE Information Visualization Conference 2011 (as a note to the non-academic reader, this is quite a competitive achievement).

New information visualisation techniques are often evaluated.  This paper argues that such evaluations typically consider response time or accuracy, and not how well users are able to interpret and remember visuals. When only the former criteria are taken into account then cognitive efficiency is the superior framework. But this is not the case of data storytelling (which is, arguably, a small subset of  all data visualizations).

When visualizations attempt to transmit a message, then how well users can receive this message, as well their capacity to remember this for a long time are of utmost importance, much more than the ease with which a visualization is read.

In that case, Jessica Hullman proposes a trade-off between cognitive efficiency and “obstructions”. The idea is that such obstructions, or visual difficulties, can trigger active learning processes. In other words, if when trying to read a chart, a user doesn’t understand it effortlessly, but is somehow willing to get to the bottom of it, she will apply all her active brainpower to it. This effort surge will lead her to not only better interpret it but also to better remember it. To sum up, these obstructions can have positive effects, this is why when this effect works, they are called desirable difficulties.

Desirable difficulties are tricky, because if the “obstruction” is too large, if a small additional effort is not enough to understand the chart, then it will not work. So, this is definitely not about maximizing the difficulty to understand the visualizations.

In the recommendations parts of the paper the authors say:

Instead of minimizing the steps required to process visualization, induce constructive, self-directed, cognitive activity on the part of the user.

This doesn’t mean that anything goes. This paper does not argue to add as many difficulties as possible, to use every gratuitous effect in the book. Instead, the paper goes on to give actionable design suggestions to enhance reader stimulation and active information processing.

In my practice, for instance with the Better Life Index, I verify the analyses of the Hullman paper: the novelty of the form and the aesthetic appeal of the representation drive the users to overcome the difficulty posed by the unusual shape of the flower/glyph. Would bar charts have conveyed the data more efficiently and more accurately? Definitely! would the user engagement have been comparable? Definitely not.

A critique by Stephen Few

Stephen Few, whose work I have praised at multiple occasions in this blog, has published a critique of this paper (pdf). Reading his article, then the paper again, I had the feeling that they didn’t talk about the same things. In certain contexts, difficulties are not desirable at all and must be eradicated. Yet, in other contexts, cognitive efficiency does not provide the  optimal solution.

For instance, Stephen writes:

Long-term recall is rarely the purpose of information visualization.

Fair enough! so let’s agree that when it is not the case, we should not trouble ourselves with seeking to add obstructions to the display. For instance: business intelligence systems, dashboards (for monitoring), visual analytics (and more on this shortly). Spreadsheets, mostly. All usages of data that support decision, and most usages in the corporate world. The Hullman paper only applies in the other cases anyway.

He would also write (emphasis by me):

Skilled data analysts learn to view data from many perspectives to prevent knee-jerk conclusions based on inappropriate heuristics.

Agreed! and by all means, let them analyse and let them view data from as many perspectives as they see fit, and don’t get in the way of their job.

For context, check out

This here is taken from a demo from Palantir government. Here analysts are tracking mortgage fraud. Each yellow dot on the top display is a transaction where a house has been sold for over 200% of its purchase value, and the ones which are connected are about the same house. We can immediately see 2 suspicious clusters where a property has been resold 4 times in these conditions. And if at the end of their work day the analysts don’t remember the address of the fraudulent transaction, it’s no big deal as long as they have identified a wrong practice.

Conversely, at the risk of repetition, the paper authors write of a trade-off between efficiency and obstructions – cognitive efficiency being generally positive. They say that obstructions become desirable difficulties only if they are constructive, that is if they are able to trigger active information processing. They are not championning 3D pie charts or atrocious dashboards as the one at the end of Stephen’s article.  Jessica signals that novelty enhance active information processing. I don’t know how to characterize speed dials in dashboards, for instance, but novel would not be the word I’d use, and again they wouldn’t be favoured by the authors of the paper. So, I think it’s a bit unfair to associate the paper with the terrible, terrible visuals presented in Stephen’s article, the ones in the original paper being a little bit more defendable.

To see this chart in context see

Consider this other chart (and let’s assume for the sake of discussion that its cognitive cost is low, while it could be much lower by showing fewer time series for instance). This was published in an OECD publication almost 2 years before the 2008 crisis. I would say this chart is easy to read (we see mortgage delinquency rates dropping in most countries) but difficult to interpret and to recall. Like other charts of the document, this one is an oracle of financial apocalypse, as the proportion of delinquent mortgage in the US, the only one without a downward trend, will have the consequences that we know. So if a different way of showing the same data could have made that more obvious at the cost of legibility, I think it would have been worth a shot.

Are we on common ground yet?

If not, let’s assume now that there exists visualizations where long-term recall is, indeed, the main purpose. Examples would include use in journalism, politics, advocacy, marketing… Jessica has been involved in the series of workshop Telling stories with data at VisWeek. This suggests an interesting distinction.

  • visualizations which are tools with which a user accesses or manipulates data.
  • visualizations where an author, with a specific intent, tries to frame data in a certain way to an audience. In that case, the author wants to make sure the audience receives the message as intended, and remembers it.
See where I’m going?
In the first case, we want cognitive efficiency all the way.
In the second, we are mostly concerned with getting our message across and making it stick.
So, there is no contradiction between having a set of rules for one category of visuals, and a different one for the other, especially since the criteria of success are so different. To illustrate this I note that both the article and the paper refer to Tableau, a “cognitive efficiency” company. Yet, it turns out that Tableau is also very interested in doing as well as possible in the story telling front, and that questions asked at the paper’s presentation by Tableau representatives show their interest in this research.

Where to from there?

We have proven methods to reduce the cognitive cost of a visual, and we can thank Stephen Few for making them more accessible. It’s much more difficult, though, to optimize the characteristics of a successful “data narrative”, that is interpretation and memorability. It’s an infographics jungle out there. Those of us who haven’t seen their share of undefendable visuals just haven’t searched enough, but absolutely anything goes.
We still do not have an equivalent framework for visualizations that tell stories. InfoVis started to study them (such as in the remarkable Narrative Visualization: Telling Stories With Data) and characterize them, but we don’t have a systematic, reproducible way to make sure that data narrative will work well, just as we can do the perfect dashboard.  We do know that the best examples at large do not comply with the rules of cognitive efficiency though. Fortunately, practitioners have not waited for convincing resarch and are leading the way, even though many get lost in the process. This is why we need more research on that front! I for one is looking forward new developments in this area of InfoVis.


Better life index – a post mortem

Today, OECD launched the Your better Life Index, a project on which I had been involved for months.

The short

I’m happy with the launch, the traffic has been really good, and there was some great coverage.

The three main lessons are:

  • Just because data is presented in an interactive way doesn’t make it interesting. The project is interesting because the design was well-suited to the project. That design may not have worked in other contexts, and other tools may not have worked in this one.
  • The critical skill here was design. There are many able developers, but the added value of this project was the ability of the team to invent a form which is unique, excellent and well-suited for the project.
  • Getting a external specialists to develop the visualization was the critical decision. Not only did we save time and money, but the quality of the outcome is incomparable. What we ended up with was far beyond the reach of what we could have done ourselves.

The less short

Before going any further, I remind my kind readers that while OECD pays my bills, views are my own.

Early history of the project

OECD had been working on measuring progress for years and researching alternative ways to measure economy. In 2008-2009 it got involved in the Stiglitz-Fitoussi-Sen commission which came up with concrete recommendations on new indicators to develop. It was then that our communication managers started lobbying for a marketable OECD indicator, like the UN’s Human Development Index or the Corruption Perception Index from Transparency International.

The idea was to come up with some kind of Progress Index, which we could communicate once a year or something. Problem – this was exactly against the recommendations of the commission, which warned against an absolute, top-down ranking of countries.

Eventually, we came up with an idea. A ranking, yes, but not one definitive list established by experts. Rather, it would be a user’s index, where said user would get their own index, tailored to their preferences.

Still, the idea of publishing such an index encountered some resistance, some countries did not like the idea of being ranked… But at some point in 2010 those reserves were overcome and the idea was generally accepted.

Our initial mission

It was then that my bosses asked  a colleague and I to start working on what such a tool could look like, and we started working based on the data we had at hand. I’ll skip on the details of that but we first came up with something in Excel which was a big aggregation of many indicators. It was far from perfect (and further still from the final result) but it got the internal conversation going.

Meanwhile, our statistician colleagues were working on new models to represent inequality, and started collecting data  for a book on a similar project, which will come out later this year (“How is Life?”). It made sense to join forces, we would use their data and their models, but will develop an interactive tool while they write their book, each project supporting the other one.

From prototypes to outsourcing

It wasn’t clear then how the tool would be designed. Part of our job was to look at many similar attempts online. We also cobbled some interactive prototypes, I made one in processing, my colleague in flash. Those models were quite close to what we had seen, really. My model was very textbook stuff, one single screen, linked bar charts. Quite basic too.

I was convinced that in order to be marketable, our tool needed to be visually innovative. Different, yes, but not against the basic rules of infovis! No 3D glossy pie or that kind of stuff. Unique, but in a good way. There was also some pressure to use the existing infovis tools used by OECD. We have one, for instance, which we had introduced for subnational statistics and which was really good for that, and which we have used since then in other contexts, with mixed success. My opinion was that using that tool as is on this project would bury it.

That’s my 1st lesson here. I’ll take a few steps back here.

In 2005, the world of public statistics was shaken by the introduction of gapminder. The way that tool presented statistics, and the huge success of the original TED talk – which attracted tens of millions of viewers – prompted all statistical offices to consider using data visualization, or rather in our words to produce “dynamic charts”, as if the mere fact that gapminder was interactive was the essence of its success. The bulk of such initiatives was neither interesting nor successful. While interactivity opens new possibilities, it is a means and certainly not than an end in itself. Parenthese closed.

At this stage, the logical conclusion was that we needed to have a new tool developed from scratch, specifically suited for the project. Nothing less would give it the resonance that we intended. My colleague lobbied our bosses, who took it to their bosses, and all the way to the Secretary-General of OECD. This went surprisingly well, and soon enough we were granted with a generous envelope and tasked with finding the right talent to build the tool.

Selecting talent

So our job shifted from creating a tool and campaigning for the project to writing specifications that could be understood by external developers. We had to “unwrite” our internal notes describing our prototypes and rewrite them in a more abstract way, trying to describe functionality rather than how we thought we could implement it (i.e. “the user can select a country” rather than “and when they click on a country, the middle pane changes to bla bla bla”.)

Being a governmental organization we also had to go through a formal call for tenders process, where we’d have a minimum number of bidders and an explicit decision process that could justify our choices.

This process was both very difficult and very interesting. Difficult because we had many very qualified applicants and not only could we only choose one, but that choice had to be justified, vetted by our bosses, which would take time. And it was rewarding because all took a different approach to the project and to the selection process. What heavily influenced the decision process was (nod to the 2nd lesson I outlined) whether the developers showed potential to create something visually unique. We found that many people were able to answer functionally to what we had asked. But the outcome probably wouldn’t match the unspoken part of our specifications. We needed people who could take the project beyond technical considerations, and imbue it with the creative spirit that would make it appealing to the widest audience.

Working with a developer

When we officially started to work with the selected developer – a joint effort by Moritz Stefaner and RauReif, some time had passed since we had introduced the project to them. When Moritz started presenting some visual research (which by the way has very little to do with the final site) I was really surprised by how much this was different what we had been working on. And that’s my 3rd lesson here.

We had become unable to start again from a blank sheet of paper and to re-imagine the project from scratch. We were too conditioned by the other projects we had seen and our past prototypes that we lacked that mental agility. Now that’s a predicament that just can’t affect an external team. Besides, even if we had the degree of mastery of our developers in flash or visual design (and we don’t), we still had our normal jobs to do, meetings to attend and all kind of office contingencies, and we just couldn’t be that productive. Even if we had equivalent talent inhouse, it would still had been more effective to outsource it.

What I found most interesting in our developers approach is that it underplayed the accuracy of the data. The scores of each country were not shown, nor the components of that score. That level of detail was initially hidden, which produced a nice, simple initial view. But added complexity could be revealed by selecting information, following links etc. At any time, the information load would remain manageable.

Two things happened in a second phase. On one hand, Moritz had that brilliant idea of a flower. I instantly loved it, so did the colleagues who worked with me since the start of the project. But it was a very hard sale to our management who would have liked something more traditional. Yet that flower form was exactly what we were after: visually unique, a nice match with the theme of the project, aesthetically pleasing, an interesting construction, many possibilities of variation… Looking back, it’s still not clear how we managed to impose that idea that almost every manager hated. The most surprising is that one month after everybody had accepted that as an evidence.

On the other, the written part of the web site, which was initially an afterthought of the project, really gained in momentum and importance, both in terms of contents and design. Eventually the web site would become half of the project. What’s interesting is that the project can cater to all kinds of depths of attention: it takes 10 seconds to create an index, 1 minute to play with various hypotheses and share the results on social networks, but one could spend 10 more minute reading the page of a country or of a topic, and several hours by checking the reference texts linked from these pages…

Closing thoughts

Fast forward to the launch. I just saw a note from Moritz that says that we got 60k unique visitors and 150k views. That’s about 12 hours after the site was launched (and, to be honest, it has been down during a couple of these 12 hours, but things are fine now)!! those numbers are very promising.

When we started on that project we had an ambition for OECD. But beyond that, I hoped to convince our organization and others of the interest of developing high-quality dataviz projects to support their messages. So I am really looking forward to see similar projects that this one might inspire.


VisWeek 2010: the one-minute edition.

Visweek 2010 is just over.

With lectures and presentations going on in up to 4  rooms simultaneously for 6 straight and very full days, it’s impossible to see everything let alone to describe it. And even that would be ignoring all exchanges and social interactions which are precisely the point of visweek.

So instead, I’m showing what I liked best,  one image per day.


Changing the world with data visualization

This Wednesday, I had the privilege of talking at Visweek at a panel with Robert Kosara, Sarah Cohen and Martin Wattenberg. That was a truly great experience (at least from that side of the microphone). We all had a different approach to the subject. Sarah showed some of the stories she ran on the Washington Post where showing data visually helped expose scandals and move things forward. Martin made insightful comparisons with writing – information verbalization. As for myself I elaborated on the OECD mantra that if people had better knowledge, they could make better decisions and that data visualization can help by providing the people that knowledge, without requiring them to actually know the data.

But as with panels, the most interesting part is always the discussion. And I was quite surprised to see where it was headed.

I have reserves in my belief that data visualization can save the world. For instance, I have been slightly disappointed by the outcome of the sunlight foundation apps for America contest. I thought the idea was fantastic and the finalist applications were very well designed, but not necessary useful. But I had read many positive reactions on blogs on this, or on anything related to, I thought I would be the skeptical one.

But during the panel, during the discussions and in the subsequent days, I really found myself in the opposite role. I think data visualization can achieve much more than what we ask it to do!

let’s put it this way. Currently there are approximately 1.7 billion internet users. That’s a order of magnitude of  the number of people that data visualization could help. Now before the before the panel, we had a talk about the number of visits that a successful data representation gets, and we convened that 100,000 viewers for one visualization is a lot. In other words, we still have more than 99.99% of the population to reach!

True, we can use data visualization to inform better. But we can do more! use it to support decisions! couldn’t the subprime crisis have been avoided, for instance, if households were helped to make the right ones?

Raising the level of adoption of data visualization – not increasing it, but multiplying it – should really be a challenge of the field. However, academics seem to be more concerned with designing novel solutions which could turn into published papers. Then again, if public interest for data visualization was higher, funding would be more easily available to researchers.

As an aside, Excel has also been discussed. Is it the problem? Partly. If a data representation is not a canonical chart type in Excel, people are not aware it exists, and mainstream media or others with a long reach will not use it for fear that potential users may be confused. Even scatterplots, to Martin’s lament, although they are in Excel and that they are pretty straightforward to use and understand, generate that aura of fear.

Another comment which I really took to heart was the regret that while data visualization was thought to computer scientists, using data analytics isn’t tought in business schools. Wouldn’t it be part of the solution?


Review of Tableau 5.0

Those last 2 weeks, I finally found time to give Tableau 5.0. Tableau enjoys a stellar reputation among the data visualization community. About a year ago, I saw a live demo of Tableau by CEO and salesman extraordinaire Christian Chabot. Like most of the audience, I was very impressed, not so much by the capacities of the software but by the ease and speed with which insightful analysis seemed to appear out of bland data. But what does it feel on from the user perspective?

Chartz: ur doing it wrong

Everyone who wrote about charts would pretty much agree that the very first step in making one is to decide what to show. The form of the display is a consequence of this choice.

Most software got this wrong. They will ask you how you want your display to look like, then ask you for your data. Take this screenshot from Excel:


When you want to insert a chart, you must first choose what kind of chart (bar, line, column, pie, area, scatter, other charts) and one of its sub-types. You are not asked, what data does this apply to, and what that data really is. You are not asked, what you are trying to show through your chart – this is something you have to manage outside of the software. You just choose a chart.

I’m picking Excel because with 200m users, everyone will know what I’m talking about, but virtually all software packages ask the user to choose a rather rigid chart type as a prerequisite to seeing anything, despite overwhelming theoretic evidence that this approach is flawed. In Excel, like in many other packages, there is a world of difference between a bar chart and a column chart. They are not of the same nature.

A reverted perspective

Fortunately, Tableau does it the other way round. When you first connect with your data in Tableau, it distinguishes two types of variables you can play with: dimensions and measures. And measures can be continuous or discrete.

tableau-dimensions(This is from an example file).

Then, all you have to do is to drag your dimensions and your measures to the center space to see stuff happening. Let’s drag “close” to the rows…

tableau-dragging-1We already see something, which is not terribly useful but still. Now if we drag Date into the columns…


Instant line chart! the software found out that this is the type of representation that made the most sense in this context. You’re trying to plot continuous variables over time, so it’s pretty much a textbook answer. Let’s suppose we want another display: we can click on the aptly name “show me!” button, and:


These are all the possible representations we have. Some are greyed out, because they don’t make sense in this context. For instance, you need to have dimensions with geographic attributes to plot things on a map (bottom left). But if you mouse over one of those greyed out icons, you’ll be told why you can’t use them. So we could choose anything: a table, a bar chart, etc.

A simple thing to do would be to switch rows and columns. What if we wanted to see date vertically and the close horizontally? Just drag and drop, and:


Crafting displays

Gone are the frontiers between artificial “chart types”. We’re no longer forcing data into preset representations, rather, we assign variables (or their automatic aggregation, more on that shortly) to possible attributes of the graph. Rows and columns are two, which shouldn’t be taken too literally – in most displays, those would be better described as abcissa and ordinate – but all the areas in light grey (called “shelves”) can welcome variables : pages, filters,path, text, color, size, level of detail, etc.


Here’s an example with a more complex dataset. Here, we’re looking at sales figures. We’re plotting profit against sales. The size of the marks correspond to the volume of the order, and the colour, to their category. Results are presented year by year. It is possible to loop through the years. So this display replicates the specs of the popular Trendalyzer / Motion chart tool, only simpler to set up.

Note that as I drag variables to shelves, Tableau often uses an aggregation that it thinks makes more sense. For instance, as I dragged Order Date to the page shelf, Tableau picked the year part of the date. I could ask the program to use every value of the date, the display will be almost empty but there would be a screen for each day. Likewise, when I dragged Order Quantity to the Size shelf, Tableau chose to use the sum of Order Quantity instead. Not that it makes much of a difference here, as each bubble represents only one order. But the idea is that Tableau will automatically aggregate data in a way that makes sense to display, and that this can always be overridden.

But if I keep the data for all the years in the display, I can quickly see the transactions where profit was negative.

sets1And I can further investigate on this set of values.

So that’s the whole idea. Because you can assign any variable to any attribute of the visualization, in the Tableau example gallery you can see some very unusual examples of displays.

Using my own data

When I saw the demos, I was a little skeptical of the data being used. I mean, things were going so smoothly, evidence seemed to be jumping at the analyst, begging to be noticed. Tableau’s not bad at connecting with data of all forms and shapes, so I gave it a whirl with my own data.

Like a lot of other official data providers, OECD’s format of choice for exporting data is SDMX, a flavor of XML. Unfortunately, Tableau can’t read that. So the next easiest thing for me was Excel.

I’m not going to get too much into details, but to come up with a worksheet that Tableau liked with more than a few tidbits of data required some tweaking and some guessing. The best way seems to be: a column for each variable, dimensions and dates included, and don’t include missing data (which we usually represent by “..” or by another similar symbol).

Some variables weren’t automatically reckognized for what they were: some were detected as dimensions when they were measures, date data wasn’t processed that well (I found that using 01/01/2009 instead of 2009 or 1/2009 worked much better). But again, that was nothing that a little bit of tweaking couldn’t overcome.

On a few occasions, I have been scratching my head quite hard as I was trying to understand why I could get Y-o-Y growth rates for some variables, but not for some others, or to make custom calculated fields. Note that there are plenty of online training videos on the website. I found myself climbing the learning curve very fast (and have heard similar statements of recent users who quickly felt empowered) but am aware that practice is needed to become a Tableau Jedi. What I found recomforting is that without prior knowledge of the product, but with exposure to data design best practices, almost everything in Tableau seems logical and simple.

But anyway – I was in. Here’s my first Tableau dashboard:

my-dashboardA Dashboard is a combination of several displays (sheets) on one space. And believe me, it can become really sophisticated, but here let’s keep it simple. The top half is a map of the world with bubbles sized after the 2007 population of OECD countries. The bottom half is the same information as a bar chart, with a twist: the colour corresponds to the population change in the last 10 years. So USA (green) have been gaining population while Hungary has seen its numbers decrease.

I’ve created an action called “highlighting on country” to link both displays. The best feature of these actions is that they are completely optional, so if you don’t want to have linked displays, it is entirely up to you and each part of the dashboard can behave independantly. You can also bring controls to filter or animate data which I left out for the sake of simplicity. However, you can still select data points directly to highlihght them in both displays, like this:

my-dashboard-highlight-bottomHere I’ve highlighted the top 5 countries. The other ones are muted in both displays. Here my colour choice is unfortunate because Japan and Germany, which are selected, don’t look too different from the other countries. Now I can select the values for the countries of Europe:


And you’ll see them highlighted in the bottom pane.

Display and style

Representing data in Tableau feels like flipping the pages of a Stephen Few book, which is more than coincidiential as he is an advisor to Tableau. From my discussion with the Tableau consultant that called me, I take that Tableau takes pride in their sober look and feel, which fervently follows the recommendation of Tufte, and Few. I remember a few posts from Stephen’s blog where he lashed as business intelligence vendors for their vacuous pursuit of glossiness over clarity and usefulness. Speaking of Few, I’ve upgraded my Tableau trial by re-reading his previous book, Information Dashboard Design, and I could really see where his philosophy and that of Tableau clicked.

So there isn’t anything glossy about Tableau. Yet the interface is state-of-the-art (no more, no less). Anyone who’ve used a PC in the past 10 years can use it without much guessing. Colours of the various screen elements are carefully chosen and command placement makes sense. Most commands are accessible in contextual menus, so you really feel that you are directly manipulating data the whole time.

When attempting to create sophisticated dashboards, I found that it was difficult to make many elements fit on one page, as the white space surrounding all elements becomes incompressible. I tried to replicate displays that I had made or that I had seen around, I was often successful (see motion chart reproduction above) but sometimes I couldn’t achieve the level of customization that I had with visualizations which are coded from scratch in Tableau. Then again even Tableau’s simplest representations have many features and would be difficult to re-code.

Sharing data

According to Dan Jewett, VP of product development at Tableau,

“Today it is easier to put videos on the Web than to put data online.”

But my job is precisely to communicate data, so I’m quite looking forward this state of affairs to change. Tableau’s answer is twofold.

The first half is Tableau Server. Tableau Server is a software that organizes Tableau workbooks for a community so they can access it online, from a browser. My feeling is that Tableau Server is designed to distribute dashboards within an organization, less so with the anyone on the internet.

That’s where the second part of the answer, Tableau Public, comes into play. Tableau Public is still in closed beta, but the principle is that users would have a free desktop applications which can do everything that Tableau Desktop does, except saving files locally. Instead, workbooks would have to be published on Tableau servers for the world to see.

There are already quite a few dashboards made by Tableau Public first users around. See for instance How Long Does It Take To Build A Technology Empire? on one of the WSJ blogs.

Today, there is no shortage of tools that let users embed data online without technical manipulations. But as of today, there is no product that could come close to this embedded dashboard. Stephen McDaniel from Freakalytics notes that due to Tableau’s technical choices (javascript instead of flash), dashboards from Tableau Public can be seen in a variety of devices, including the iPhone.

I’ve made a few dashboards that I’d be happy to share with the world through Tableau Public.

This wraps up my Tableau review. I can see why the product has such an enthusiastic fan base. People such as Jorge Camoes, Stephen Few, Robert Kosara, Garr Reynolds, Nathan Yau, and even the Federal CIO Vivek Kundra have all professed their loved for the product. The Tableau Customer Conference, which I’ve only been able to follow online so far, seems to be more interesting each year. Beyond testimonies, the gallery of examples (again at, but do explore from there to see videos and white papers), still in the making, shows the incredible potential of the software.