Category Archives: Thoughts

Ideas, thoughts, open issues, questions.

Where are the data visualization success stories?

I see a lot of visualization around me now and I am extremely excited about it. Yet, are we making any real difference? I mean, are we having any real impact in people’s life other than telling them beautiful stories?

Yes I know, impact could be defined in a million different ways and it may be hard to capture. But why? Why I never stumble into an article or blog post showing, I don’t know, for instance, how visualization helped a group of doctors doing something remarkable with visualization?

Is it just because this stuff does not get reported or what?

Here are a few possible explanations:

  • Explanation#1: Impactful visualization is hidden. Those people who are using visualization successfully, who have a real impact, are too busy to report their success.
  • Explanation #2: Visualization is just a fragment of a much larger process. Visualization, when is not used as a communication/story telling tool is part of a much larger process, which includes many other steps and tools so simply success is not ascribed to visualization.
  • Explanation #3: Visualization impact has yet to come. Maybe we just have to wait a bit longer and we’ll get all the success we want.

What do you think? Do you have other explanations? Is my question just too pretentious? Or did I just miss a ton of success stories and this post is totally nonsense?

P.S.1 On a side note: other areas of data analysis, especially automatic approaches like machine learning and data mining have plenty of stories to tell. Why? Food for thought …

P.S. 2 After writing this post I discovered my friend Andy Kirk has written a much longer post on this issue.

What Is Progress In Visualization?

Being a visualization researcher means a very large body of my work revolves around pushing the boundaries of visualization further. I do that by mostly developing innovative techniques but also trying to better understand how humans interact with this amazing tool we call visualization.

You might think I have at least a rough idea of what progress means in visualization then, but in fact I don’t. And I guess I am not alone: researchers are trained to dive into tiny details and speculate for ages. The purpose of this post is to explore bigger questions:

  • What is progress in visualization?
  • How do we make progress in visualization?
  • And how do we measure it?

I ask that because honestly I don’t see a direction in what we are doing. We researchers are mostly focussed on developing yet another technique, practitioners on (understandably) satisfying their customers. But what is our ultimate goal? Here I propose s few ways we can look at progress in visualization.

Progress As Real-World Impact

First and foremost I propose progress in visualization is the extent to which we are able to help people do remarkably useful things with data. This is for me the gold standard, the holy grail. It is a broad and vague definition but it helps. When I say “remarkably useful” I mean: can we say visualization played a critical role in curing or preventing diseases? Reducing poverty? Solving or preventing economic crises? Make people richer or happier? Etc. Think about it, why not? Why do we do visualization if not for these purposes?

Despite some few isolated cases I don’t see this happening now. We should keep our eyes open and focus more on having an impact in the real world. Visualization has this potential, I am sure, and progress is made, I believe, when we help people do remarkable things. The VisWeek conference used to host a very nice session called Discovery Exhibition with the specific intent to showcase success stories. Unfortunately, (its hurts to admit it) I think it was quite a failure. I remember a similar frustrating post from Stephen Few some years ago: “True Stories about the Benefits of Data Visualization“. And I have yet to see persuasive answers to his call.

Progress As Knowledge Construction

I have to admit measuring progress exclusively in terms of impact and success stories might be a bit fuzzy, not very practical and ultimately a bit subjective. Another possibility is to define progress as the accumulation of knowledge that permits to build more effective visualization. But what do we need to know that we don’t know yet? Broadly speaking we need to know:

  1. How humans work.
  2. How to translate knowledge about humans into visualization design.

Are we doing that right now? Partly, in academic environments and a bit outside, but not enough in my opinion. It’s surprising to see how much more foundational work has been done in the past and how little today. We have a rough idea of how visual variables (position, length, color, size, etc.) work in isolation but very little understanding of how they interact in complex environments. We have alternative visualizations for the same kind of data and little understanding of how they influence information extraction (parallel coordinates vs. scatter plot matrix? node-link diagrams vs. matrices? maps or abstract representation? animation or small multiple?) And we have not even started scratching the surface of muddier issues like semantics, influence, persuasion, etc.

Progress as Technical Achievement

I don’t even know if I need to comment on this one, it’s pretty straightforward: technical achievement is the development of visualization and interaction techniques that solve unsolved technical problems or improve performance over existing solutions. Typically this takes the following form:

  • New visualization or interaction design.
  • Faster and/or more accurate algorithms.
  • Increased scalability in terms of data size and dimensionality.
  • Accommodation of new data formats and tasks.

I think it’s safe to say academic research is mostly focused on this. I am not sure whether technical achievement translates into real benefits in real-world applications but from time to time we have really useful stuff coming out. Edge bundling and horizon graphs are the first things that come into my mind. Are we making progress in this area? Yes. Would I like to see more? Yes and no … In a way sometimes I feel like we are spinning the wheel (please note that I include myself into this description and I am not immune to many many faults) so I’d like to see less spinning-the-wheel technical contributions and more useful stuff. But I also realize we cannot invent a new edge bundling every year. Progress happens with valleys and peaks.

Progress As Education and Adoption

Maybe this is the most neglected kind of progress, yet it very much lies at my heart. The last way to define progress in visualization I propose is the extent to which we are able to teach people how to judge and use visualization effectively and how many people will use visualization in their work. We need to reach more people (visualization at school?) but more importantly we need to teach proper visualization. We need courses, seminars, teaching material, web sites, and a whole army of evangelists. I am lucky enough to know quite a bunch of them but we need more.

I want to measure progress in a few years by counting how many people are able to criticize a chart. I also want to measure progress by assessing whether visualization will be part of the standard toolbox of scientists, business men and decision makers around the world.


This is what I had to say about progress. I know it’s not perfect, it’s just a draft. And now it’s your turn. How do you define progress in visualization? Are we making progress? How would you measure progress in visualization in, let’s say, 5 or 10 years from now?

And by the way, do you care about making progress? Why not? It is not necessary to be “a researcher” to make progress, you can make progress in a thousand ways. The only thing we need is to bring more focus. Or maybe we just have to let things happen and have some fun? I am looking forward to hearing from you guys. Thanks for reading.

On a side note: I have been out of the scenes with FILWD for a very long while. There are good reasons why that happened (I’ll tell you more about that later) but I want to assure you FILWD is not going to fade away. To the contrary, I have many plans on how to grow it further and offer a better service. If you are still there reading me after so much time well … thank you so much from the bottom of my heart! -Enrico


Telling a story doesn’t tell the whole story

story tellingI was reading the description of a new data visualization contest coming out today, the Nielsen Data Visualization Contest, and an apparently insignificant sentence caught my attention: “The challenge is to make data tell a story, conveying what’s most important effectively and efficiently.

There is a lot of attention lately around using visualization to “tell a story” and I can understand why: visualization, when designed properly, has a tremendous effect on people. Not only it has the power to convey a clear message and to make complex concepts very easy to grasp, but it also has the power to persuade. I guess the main reason being that when a statement is backed up by data then people believe it is true(er).

I have nothing against using visualization to tell stories, to the contrary I am fascinated by this use of visualization and I think it’s very relevant. For instance, raising awareness about important facts or democratizing access to complex information are very noble intents of visual story telling, and I fully support them.

But, I don’t know, call me old-style, conservative, bigot: I am concerned by an excessive focus on story telling. It’s an itch I cannot scratch. And because I cannot express it in a closed form the only thing I can do is to make a list of concerns I have (hoping your comments will make it easier to dispel the fog).

There’s no story telling without data exploration. Creating a story with visualization doesn’t mean there is not role for data exploration in visualization in its making. People looking at the final product might think the power of visualization is exclusively in the effective presentation of the facts. But what people don’t see is the amount of exploratory work behind every story. I know as a matter of fact that many great visualization designers start with a thorough visual exploration of the data at hand using standard tools like Tableau or R. Without this preliminary phase it’s very hard to tell a compelling story and it is also very hard to come up with an enlightening visualization.

It’s the data that makes the story not the visualization. I always laugh a bit when people complain about David McCandless’ work. They say that their visualizations are not optimal and that he makes many “mistakes”. In a way I agree but why does he have such a big success then? I think the reason rests in his ability to select amazing stories to tell. The story is hidden in the data. Well, not even in the data, I guess everything starts in his mind, the rest just follows naturally. So, if we are passionate about visualization and dare about its proper use I believe story telling is (maybe) not the most challenging area to test it.

Many people need visualization to build our future not to tell a story. While I cannot resist a catchy well-crafted data visualization that tells a compelling story, I also know from my experience how desperately professionals of all kinds need visualization to just do their work best. I am talking about doctors, engineers, biologists, policy makers, etc. Part of our life, or of our future generations, might depend on them and we have the opportunity to help them help us. Don’t you think this use of visualization is a bit under represented on the web when compared to the whole set of story telling visualizations out there? For instance, why don’t we have contests to help these people with their data and have plenty of those asking to vaguely find a story to tell in this or that data set?

A story is not THE truth. I have no evidence for that but my feeling is that visualization can be used to more easily persuade people. By the mere fact of being built on top of data people might think it is truer than other kind of stories. Again, you can see that in McCandless’ work. Many of his pieces are evidently conceived to be provocative and touch hot topics. But I bet that for every provocative visualization out there there is the possibility to build a counter argument with another one. I might be proven wrong on that but I haven’t seen any evidence on the contrary so far.

Not all stories are worth telling. Since the power of a story resides in the data, it is not always possible to tell a compelling story. Regardless the beauty or inventiveness of your visualization if the data is dull you might not get a compelling story. And I have experienced it so many times that I am almost inclined to say that this is pretty much the standard for any given data set. You can see it in the recent Information is Beautiful Award: there are many cool and pretty entries, some that I really like from the design point of view, but is there anything really interesting there to see? Do we leave the stage enriched by new knowledge?

That’s all folks. Any ideas, comments, thoughts? There’s no truth carved in stone here and I’d love to hear your opinion. What do you think about visualization as a vehicle to tell stories?

Visualizing’s Answer to My Concerns with Marathons

As promised yesterday here is the answer I received from Visualizing after sending them a draft of my post. Given their answer and the whole bunch of controversial but constructive comments I received (check them out, they are full of insights) I am really glad to have started this. I have the feeling this can in a way help all of us, regardless our opinions, make the whole field at least a tiny bit better.


First, thanks very much for taking the time to share your feedback and for your thoughtful suggestions. We’re all committed to advancing the field of data visualization and healthy debate towards that goal is always useful.

The aim of the Visualizing Marathon program, which we started in 2010, is to encourage design students around the world to take up data visualization and generally to use design to help improve our collective understanding of complex world issues. The structure and format of the event is constantly evolving in support of this aim based on what is working and not working (we collect surveys from the students, for example) and so we’re most grateful for any and all feedback!

To your specific points:

1. Judging: All Marathons (and challenges) are judged based on three criteria, which we’ve previously shared on Visualizing:

  • Understanding: How effectively does the visualization communicate? How well does it help you make sense of this issue? (out of 10 points – we agree with you this is most important and that’s why it gets the most weight)
  • Originality: Are the approach and design innovative? (out of 5 points)
  • Style: Is the visualization aesthetically compelling? (out of 5 points)

Our global jury selected 1 Winner and 2 Honorable Mentions in each of the 5 cities. These are the top 15 projects based on these metrics.

Importantly, however, the Grand Prize was selected by us out of the top 15 based on a different metric: how well does the project help illuminate new insights to the complex problem the students were given (in this case, sustainable development). Because data visualization is not only a tool for communication but also a tool for exploration, we sought to highlight and amplify the latter with this particular prize. It’s why it comes with a $10,000 grant to support further research and education. We felt the winning project best delivered on this metric (the approach and analysis detailed in their accompanying essay is particularly noteworthy). And as we noted in the prize announcement, we hope very much that the students use the additional time and resources they have been granted to take the visualization further (including putting their 3D shape to work as outlined, and perhaps evolving a simpler overall design). Also, we’re sure they would enjoy hearing suggestions directly.

We have been exploring how we might incorporate a “People’s Choice” aspect into the program, though there are some potential complications with this format that we are trying to be mindful of.

2. Time: There is no question that time (usually) improves quality – and our Visualizing Challenges, for example, typically run 4-6 weeks based on that logic. With our Visualizing Events, like Visualizing Europe and the Visualizing Marathons, we want to create the space and opportunity for people to come together in a shared and collaborative environment where they can meet, learn from one another, and develop new partnerships/relationships. We hope that after each event, the conversation continues in a way that can push forward the field of data visualization. We know from direct feedback from students and their professors that there is a tremendous didactic, creative, and inspirational value in working together with 2-3 of your friends in a common space with other students for 24 hours towards a common goal (we are also mindful that there is a real limitation to the amount of time students can commit to an extra-curricular activity). As you rightly pointed out, there are high quality projects among the entries, so clearly it is possible to produce something of quality in the allotted time. We also believe that overall quality from students will improve year over year as the professional field and its accompanying science mature and codify what works. That said, we are in fact experimenting with time this year to help improve overall quality and welcome any suggestions.

3. Training: We agree that training is important. In the spirit of openness, we allow students of all levels and disciplines to participate in the Marathons and learn by doing. As you mentioned, data visualization has become mainstream only recently, especially in some of the cities where the marathons have taken place. To provide greater training before the Marathon, this year we just have started providing registered students with various resources and helpful links (including this one) well in advance to encourage them to learn more about data visualization. And since the beginning of the program, we have been running data visualization lectures and workshops hosted by design professionals during the Marathons to teach best practices (based on feedback, we recently moved these lectures and workshops to the start of the marathon program so lessons can be incorporated from the outset).

Again, thanks Enrico for all your support. As we are ever committed to developing the best possible Marathon program, we’re very much open to ideas.

The Visualizing team

Thanks Visualizing for accepting openly my criticism. I think this is simply great!


How Do We Achieve the Right “E-Cube-Librium” in Visualization Marathons?

“Huston we have a problem …”

I just received this in my inbox:

We want to again express our sincerest gratitude for your help in making the Visualizing Marathon 2011 such a resounding success. Your participation was instrumental and the 376 students who competed in Sydney, São Paulo, New York, London, and Berlin told us how excited they were to meet you and have their work reviewed by such an esteemed global jury.

We just announced that the winner of the $10,000 “Imagination at Work” Grand Prize is Columbia University for E-Cube-Librium […] Out of 15 finalists, the Grand Prize was awarded to the project that “best illuminates a new insight or solution to a complex problem through data visualization. [bold is mine]

I receive this because I was part of the jury for the Marathon in Berlin. Visualizing Marathon is a series of events (inspired by the more famous hackatons) organized by around the world to promote visualization. Groups of students develop a visualization for a given dataset/problem in 24 hours and gives awards to the best entries.

Being a juror was fun and and an honor for me, as well as being one of the speakers at Visualizing Europe last year. I am grateful to Visualizing for the great work they are doing in terms of promotion, and also for their commitment to building a solid platform for visualization designers. Nonetheless, I think we have a problem.

I look at E-Cube-Librium  and I cannot help but think: “Is this the best 376 students from all over the world can produce?” It just doesn’t match with my definition visualizations that “best illuminates a new insight or solution to a complex problem“.

I am really sorry I have to say that, especially because I am sure the students did their best and are probably proud of their work, and also because I am sure the guys at have the best intensions in their mind. But I am also concerned that people around the world would look at the best prize winner and think this is the gold standard of visualization. We have to be careful, especially now that visualization is in the mainstream, about what message we give. I have seen too many times visualization dismissed altogether because people think it’s only pretty picture. Our reputation and future is at stake here.

Now, we have a nice series of events organized around the world and I am all in favor of data visualization evangelism, but why are the results disappointing? Is it an intrinsic problem of marathons and contests or maybe we can engineer the whole thing to make it more effective? Here are some potential explanations I can tell from my experience:

  1. Time is too short to produce quality results. Every time I complain about the quality of the results there is someone who points out that time is too short. I am not fully convinced time is the main problem however, even though I do think time is too short. Basic design choices do not depend on the amount of time. It doesn’t take time to know a 3D visualization of numeric data should not be your first choice when designing a visualization, it takes knowledge.
  2. Students are not well prepared. That students are not knowledgeable enough to produce quality results is not surprising. Visualization has become mainstream very recently and there is not a clear path to follow if one wants to become an expert. Nonetheless, some of the entries I personally reviewed as a juror were more than reasonable, especially given the 24hrs constraints! Also, giving a look to the page with the whole set of winners and honorable mentions it’s surprising to notice how neat solutions coexist together with very questionable ones.
  3. Jurors select the wrong entries. Another possibility is that jurors just pick the wrong entries. I don’t know who selected the grand prize winner, I was not involved in the process, but my feeling is that here we might have the biggest mismatch. When I participated as a juror it became clear to me how things can go wrong. Some people put clarity and information throughput before everything else (guess who?), others judge things from their coolness factor. I know, it’s sad but that’s the way it is.
How can we make better marathons? A few modest suggestions.
Without pretending to provide all encompassing or particularly clever solutions here are few things that come into my mind:
  • Give more time. If time is too short why not giving more time? The marathon format does not lend itself to data visualization. Visualization is a process, a tortuous process actually, with lots of dead ends along the road. Pretending to visualize data effectively in 24 hours might be an unrealistic goal.
  • Train students before the marathon takes place. If students are not good enough why not giving them some training before running the marathon? There are many professionals out there who are able to explain in a concise manner what are the no nos and the good practices of visualization.
  • Run marathons without prizes. Maybe a marathon could be held without giving a prize? I don’t know … is the perspective of receiving a prize that motivate students to do their best? Maybe not. Maybe just knowing that they will have the opportunity to get trained by a professional and to have a certain level of exposure will motivate them enough. I think competition is totally overrated.
  • Let people judge in place of jurors. One option could be to have “better” jurors but then we would have to discuss what we mean by “better”. As an alternative, why not letting people judge? I am not sure the result would be better but at least we could claim it is a democratic process and it wouldn’t embarrass any jurors.

And you? What do you think? Do you have any concerns with contests and marathons? How would you shape your own marathon event? Do you have any suggestion on how to improve the situation? I’d love to hear your voice.

IMPORTANT NOTE: I had the chance to discuss with some people at Visualizing before publishing this post. Since I totally respect their work and wanted to avoid slashing them with an overly unfavorable post, I decided to let them read it before publishing it. Apart from a few sentences here and there the post is still the same as the original draft.

Charlene Manuel was also so kind to send me a long reply to this post which I decided to publish soon as a post rather than a comment so that everyone will get the feeling of how Visualizing is handling this criticism.

I am very satisfied with this process. I think we all have to be happy to see that it is possible to have constructive criticism and make the whole field thrive without unnecessary battles.

UPDATE: here is the answer from the Visualizing team.

Dammit, I want more kick-ass data visualization blogs folks!

successful-blogsYesterday I wrote this on twitter: “I must confess I very rarely read data visualization blogs, most are depressingly predictable and shallow.” Yes, it’s not the nicest sentence I could write, but it’s true: most data visualization blogs suck. They do not inform, they do not entertain.

At VisWeek, last month, we organized a pretty successful Birds-of-Feathers (BoF) titled “Blogging about Visualization”. I and Robert advertised the thing a bit and we managed to gather a pretty cool bunch of people around a table. We spent at least a couple of hours all together and then we enjoyed a wonderful dinner at a Greek restaurant.

During the BoF we discussed several aspects related to blogging (check the nice summary wrote by Dominikus to know more) but what struck me the most is the following: (1) people desperately want to know how to succeed with blogs; (2) people think it is a sort of black art when in fact it’s only a matter of mindset and hard work; (3) there are endless possibilities to open new blogs.

Yet, the decent blogs around can be counted with the fingers of one hand. And I want to see more great stuff, because either we grow as a community or nobody will grow. Here are some personal thoughts about blogging and a number of tips I want to share with you, hoping they will convince some among you to open the best data visualization blog ever.

The Data Visualization Showcase is Dead

When I think about why many data visualization blogs are so useless, the reason number one that comes into my mind is that they try to replicate a dead model: the data visualization showcase (I fell into this trap twice before creating FILWD, so I know what I am talking about). The showcase model is this: “Hey folks, look how cool this is“. Stop. Iterated x-times per week.

You don’t need a blog for this. It was maybe true 5 years ago but with the advent of Facebook and Twitter it’s totally useless. Also, and even more important, there’s no way for you (and for me) to compete with Infosthetics and Flowing Data (@Andrew: I know you don’t agree with me on the death of the data visualization showcase, but what can I do? This is what I think Smile).

Let me clarify. I don’t think these two blogs are useless. Andrew and Nathan did an enormous service to our field and we all have to thank them from the bottom of our heart, but it’s foolish to believe we need more of that.

Three key reasons why (vis) blogs suck

I could name a hundred, and by the way if you buy a book on blogging (like the classic mainstream ProBlogger) you will find millions, but here I’ll focus on those I believe are especially troublesome for vis blogs (apart from the data visualization showcase which is the most troublesome).

Trouble #1 – Taking it as a hobby. This is the most problematic. People write blogs casually, once in a while, when they have nothing special to do or when they feel something is so cool they have the urge to share it with the world, that is, three friends. Amateur blogs are everywhere and pollute the whole web. If you want to succeed with your blog it’s important for you to realize that you have to sweat your damn shirt. On the contrary, if you don’t want to succeed, why polluting the web with your blog? Think about it, it’s an ecology thing: every piece of information you put on the web may decrease the already feeble signal to noise ratio we have. Do you want to contribute to the noise?

There’s no other way to succeed than taking it as a serious endeavor, believe me. Blogging takes a lot of planning and work. Every single post may take many hours distributed across days, weeks, or even months. And that’s just the effort needed to create content, without counting administration and marketing. You might not see it, but behind every single post here there is a huge amount of work, and I know it’s the same for other fellow bloggers.

Being serious about your blog then it’s not only a matter of content but also of being committed to have a somewhat regular schedule, especially at the beginning. People hate dead trees and for a good reason. Please do me a favor: if you are considering opening a blog, take the whole thing very seriously. You need a good reason for opening a blog and if you don’t have one, sooner or later you will give up. I don’t want to discourage anyone, to the contrary, I want to see more great blogs! But I am also tired of shallow blogs and dead trees.

Trouble #2 – Providing limited value. What is *special* about your blog? I know, it’s a tough question. But, if you are not totally honest with yourself about that, you will have problems. People stop by and read your blog only because you are able to deliver some kind of value. What value? I don’t know … you name it. As a general rule people read for two main broad reasons: to learn or to be entertained (or both). Are you able to deliver unique knowledge that other people cannot deliver? Or do you have a special irresistible style that people love so much they are eager to see what’s next? That’s the trick, that’s the obsession you have to have to succeed.

Many, many, many vis blogs are shallow just because they do not give in, they do not have anything special to offer. They don’t even try to differentiate themselves from the rest. It’s a game in which you lift the bar 1 inch higher every single time you write. The web is a jungle, people jump from one web page to another in a matter of seconds, how do you plan to let someone stop and read through what you write? Let’s take the data visualization showcase mentioned above: do you think you can attract people by showing new visualizations every day? Do you think you are more skilled than the current main players in finding new stuff? I have several doubts.

When I opened FILWD it was clear to me I could not compete with the big guys (and I didn’t want to anyway) so I asked myself: “what skills or knowledge do I have that I can use to gain a competitive advantage?” And my answer was that I have direct access to vis research and researchers and that I know vis theory better than the average geek. I am sure you have your own uniqueness so try to think hard how to use it.

Trouble #3 – Forgetting to show a real face.People are too busy to absorb the bare information, and information by the way is not a scarce resource anyway. Many blogs are plain dry, it looks like the writer does not exist or hides behind the curtains. Where are the emotions, opinions, and fun? Writing about scientific stuff does not imply being serious, objective or dry. The best bloggers show their face and risk their reputation every single post. Sometimes I feel a pain in the stomach before hitting “publish”. I happened to think: “people will kill me for this one “.

Similarly, many bloggers don’t spend any time thinking whether they have a style or not. But *your* style matters a lot and you’d better know what it is. There are a million styles and be careful not to fake it. Your style has to be natural but it also has to shine through your words and visual design. Take for instance Stephen Few: Oh boy … I hate the way he expresses his opinions, he makes me cling my teethes at times, but you rest assured I read every single line of what he writes. What is your style then?

How do you create (or revamp) a successful vis blog?

Hey this is slippery terrain: every single blogger has his own formula and you can find a million sources on the web on how to make your blog successful. I don’t pretend to be a blog guru, but I can share with you the things that really worked for me, with the hope they will assist you in case you want to open your blog.

Tip #1 – Find your final cause. How do you plan to change the world? Why do you want to open a blog? Once you put aside all the legitimate ego trip we all make what is left for the others? Successful blogs are centered around the readers, they want to make the world better. They strive to provoke shifts in people’s mind. How do you plan to be ridiculously helpful for people? With FILWD I planned from the very beginning to help people become visualization experts, then I discovered I could sometimes help them think in unconventional ways. What’s your cause? I’ll give you an example: do you know anything about Data without Borders? That’s a cause folks!

Tip #2 – Study a lot. Before starting FILWD I read an endless amount of material about blogging, I trashed many and kept some. I studied the strategies of many many successful bloggers in many other areas out of visualization. I could name hundreds of sources but you have to do your own research. Among the thousands things I read, there are two gems that really shine: Trust Agents, a must read even if not an easy read, and Think Traffic, the best blog about blogging ever.

Tip #3 – Plan ahead and find your style. Before starting FILWD I wrote down a thousand plans and eventually came up with two key pieces of information: (1) my target posting schedule; (2) a very few number of post categories. The posting schedule does not have to be very tight but it has to be somewhat regular, especially before your blog is established; people hate guessing when you are going to post the next article (and of course I am still struggling with it). Having a number of predefined post categories is the best piece of advice I can give, it helped me being totally clear about what I wanted to write and especially what I did not want to write. For instance, I very rarely write about other people’s work unless it is an inspiration for a broader argument. You can check my categories on the blogs and you will see they are very few. When I write a new post I think: “what category do I want to write in today?

Tip #4 – Be ready to walk through the dark and deep valley of loneliness. Blogging reminds me when I started learning how to play guitar many years ago. At the beginning it’s so frustrating, it looks like you will never be able to play two chords one after another. With blogs the problem is that at the beginning you have zero readers and you have to spend a lot of time preparing these stupid posts nobody will ever read. Very painful. But it’s totally transitory: if you keep doing the good work, people will come and will love your post written to nobody in the past. That’s a very key element of blogging: being able to go through the deep valley and wait until it blossoms. You have to have faith: it will be great.

Tip #5 – Find your own buddies. What is life without friends? I don’t have to tell you how to use twitter, Facebook, or Google plus right? Plus I don’t think there is a unique formula. But hey, make sure to build a thriving environment around your blogs and your ideas. Somebody said “No man is an island”, well this is especially true in this business. Find some buddies, share your ideas with them, test your ideas before writing a post, be exceedingly generous and genuine and people will gather around you.

Tip #6 – Experiment. Blogging is a constant experiment. You write a very successful post with a given strategy, you try to replicate it and it doesn’t work. I like to think about blogging as a radio knob you have to manipulate to find the right frequency to tune with your audience. The frequency is always shifting and your work is to be able to seek the right spot all the time. Sometimes it works, sometimes it doesn’t. But it’s not a big deal as long as you keep trying. Blogs, for instance can accommodate very different media and it’s a good idea to experiment with them. I experimented a few times with video and I was scared shitless because of it.

There are of course many other things you can do to make your blog successful, many of which I don’t know yet. Everyone has his own path, you have to find yours. I know one thing for sure: hard work always pays off. Always.

Need a good reason for opening a blog?

Hey, I hope I did not scare you too much up to this point. There is one thing I want to make sure you get out of this blog post: opening a blog may be one of the smartest choices you can make in your life. Again I could name hundreds of reasons why blogging is great but for me the most important one is that it feeds my mind in a way I could not get with other means. Blogging so far helped me, at least, in these many ways:

  • I became a much better writer
  • I became a sharper thinker (thanks to having to write what I think)
  • I know much better how the web works
  • I know many more great thinkers … and they know me
  • My ideas are debugged by a large crowd of people
  • If I have a burning question I have lots of people to whom I can ask
  • I get invited for talks
  • It feeds my research and my research feeds it
  • I might write a book one day thanks to it

I can testify that all the effort is definitely repaid by the myriad of benefits you can get. Some people do blogging for the money, and some are pretty successful, and some other for the glory. But whether you do it for the bling bling or not, the formula is always the same: you have to write epic shit. There are altruistic and egoistic benefits from blogging and they are all fine as long as you have a good balance. Blogging makes you grow internally, you find yourself improving in many ways, and it helps you having a powerful interface with the world. But it also helps people thrive thanks to your work, and that’s absolutely priceless.

Start a kick-ass visualization blog today!

Let me add one final remark. If you are thinking of opening a data visualization blog, a good one, please do it! We have a desperate need for quality content and I want to have my inbox filled up with exciting ideas. If you need more help send me a line or ask to professional bloggers. I do think there is a huge space for new blogs in this area, you just need to find your niche. For instance, I am looking forward to data visualization blogs related to one specific application area. Or, another great one I’d love to see is a blog with a frequent posting of interesting little visualization experiments. It’s up to you now, let’s make data visualization better together!

Shaking our heads won’t make visualization any better

I wanted to title this post “giving constructive feedback about visualization and its long-lasting effect” but it didn’t sound as good as this one.

The Story

I was about to write my next long post (don’t worry, almost done) when I received an email from a guy working for

Hope you’re well. I’ve seen you’ve covered infographics in the past and thought you might be interested in a new one from that looks at how people from around the world eat and sleep when staying in hotels. The research was conducted among 3,339 people in 20 countries. You can view, download and embed the infographic at:

Here is the infographic (click on it to see the details): Infographics

I gave a quick look to the image, read the findings, and just discarded it as crap. I said to myself: “Here we go again … another email with crappy infographics”, pushed delete, and moved on to the next task. After a while, my sadistic brain could not resist and I wrote a quite cryptic message on twitter trying to see if I could catch some fish:

From “I’ve seen you’ve covered infographics in the past and thought you might be interested in a new one”

A few people replied and again I moved on to the next task.

Some other people like Stephen Few would have maybe started a long rant about all the reasons why this was crap, while some others, maybe, would have taken it seriously and tried to analyze it in details. Me, I just shook my head a never replied to the guy.

Here is where the true story begins: after a few minutes I receive an email from Andy Kirk of (bold is mine):

“Enrico – I received the same email today and had a good exchange of emails with the guy promoting them.  

To be fair after our conversation he was really appreciative of the advice and said he will do his best to try and affect a change in approach. Really interesting how this particular market has erupted though isn’t it – the fact has a dedicated Infographics section under its PR pages…”

And later on:

“… it is becoming more and more difficult to stay on top of these type of requests but I’m taking the longer view that if I can offer constructive feedback it might in the smallest way have an impact on improving practice

Let me repeat this sentence from Andy:

“If I can offer constructive feedback it might in the smallest way have an impact on improving practice”.

The Lesson

What a lesson have I learned! It was like a diamond in my head. Thanks Andy.

If you have been reading this blog for a while, you might have noticed I tend to be quite pacific, but at the same time when it’s time to say crap, I say crap.

We in the community have learned to have an automatic reflex: we look at some crappy visualization and in the best case we shake our head, in the worst, we write long rants a la Stephen Few.

I must confess I use to shake my head more often than writing long rants, also because otherwise FILWD would only host such type of big-ego content which I don’t like.

After reading Andy’s email I completely changed my mind. It’s way too easy to look at some stuff and think  “oh yes, the usual crap”. I did it so many times! And it’s even funnier when you share the “crappyness”  with some friends or tweet about it “hey look … how could they be so idiotic to draw this and that in this and that way”. And we fill our mouth with words of wisdom.

Question: Do we make visualization any better by ignoring or, worse, mocking people who design bad visualization?

I know some of you might say that publicly criticizing bad stuff with big words will make people notice and be more cautious about what they publish. True. But will this strategy pay off in the long-term? I am not sure.

What do you think? Is it more beneficial a loud voice or a humble and cheerful suggestion? Especially, when people ask for an opinion. Do we need both? Do we have to treat different people with different strategies? Or should we just ignore everybody and do our work the best we can?

A few months ago I wrote in my post on Visualization Consumerism:

I think we have to acknowledge the problem and do our best to educate people. But wait a moment …. educating people is a dangerous idea! I agree. But let me explain what I mean. When I say educating people I mean doing it bottom-up; by giving the right examples and striving for creating a thriving environment

It looks to me like if these words had been written by someone else! The words are good, my behavior just does not match. We will build this thriving environment only if we learn to shake our head less and learn to help people in every possible way to make great visualization.

Sorry, now I have to go … I have to write a reply to the guy from


Happy Birthday Fell in Love with Data!

Oh gosh, I was almost going to miss it: Fell in Love with Data turns one! One year has passed and so many things happened in the meantime. Where do I start? Well, let me start with the obvious but important:

Thanks to all of you guys who are reading, commenting, re-tweeting, sending messages, etc. You gave to me much more than what I gave to you. I owe you something.

Numbers have been growing fast during this year but FILWD is not my personal toy to boost my ego (even though it helps in these regards), it’s a tool to advance data visualization. It’s for you, it’s for me, it’s a work in progress made to help us making this whole business damn better … with some fun in between if possible.

Lessons Learned.

There are many things I learned during this year, far more than I am able to write in this blog post. Here are a few that come to my mind right now as I am writing:

  • Blogging is a fantastic platform and I cannot think of doing without it anymore. Now, after one year, I cannot think of how one can pretend to be influential without a blog (especially in academia my dear fellows).
  • Writing blog posts helped me far beyond my expectations towards clarifying ideas to me and taking all these vague concepts I had in my brain and transform them into something concrete. What is really surprising to me is to notice how not only my research work helped me writing blog posts, but also writing blog posts has had a strong positive influence on my own research (one more reason for blogging my dear friends). Far more than I expected.
  • Blogging is at the same time much harder and much easier than people think. It’s harder because you have to spend a lot of energy and thoughts to make a blog successful. It’s a damn serious job, it doesn’t happen by chance. For me everything changed when I realized that it was totally nonsense trying to compete with Infostethics and Flowingdata and that I had to offer something different. But blogging is also much easier than people think because it just takes you to come up with a solid concept, set up a blogging account anywhere, and write, write, write an let it flow. I am surprised by how many people are scared by it.
  • What matters is not success in terms of numbers (even though numbers count), but influence. At the end of the day if you have only 100 readers but you are blowing their mind it’s a lot better than having 100.000 casual readers passing by and say: “hey cool” and then they go back to their own stuff. It reminds me a notable statement from Tufte: “differences that make a difference”.
  • No matter how much planning you put on your blog and how many blog posts you have in the pipeline, a blog is a living entity with its own dynamics and you cannot anticipate how people will react. This means being always ready to adapt and write about what matters now. What people need to read in this very moment, not the idea you had three months ago.
  • The space for the data visualization showcase is shrinking (and thanks god!) The only way to be successful in data visualization is to do solid stuff that people need. Yes, there are still a couple of consumerist visualization readers out there but who cares? Do they make any difference at all?
  • It doesn’t matter how clever and innovative the things I write are, the biggest value of the blog is YOU. In innumerable instances the real value of my posts came from my readers and their comments. I especially enjoyed those with opinions alternative to mine. They helped me re-think my ideas and make them more solid. Thanks a looooot!
  • The best posts I wrote are those that scared my butt off, those where I felt I was stretching my intellectual capabilities. Often with this kind of posts I experienced the tension between trying to be as accurate and informed as possible, with the realization that I just don’t have enough time and means to study everything in every single detail. That’s hard but it’s also very very rewarding.
  • Data visualization is a huge trend, far beyond the close knit of academics I was used to deal with. Plus, people out there are in desperate need of solid information because the Internet is a chaos and the field is not mature enough. Also, we people in academia have the responsibility to lead the way (did you hear that guys?)


In retrospective what could I have done differently? I don’t know … maybe you can tell me what you think. I don’t think I could have done anything too differently, I am pretty satisfied of how things evolved.

If I have to mention one single thing I would like to do better, it’s to achieve a much more regular posting rate. But in the end it’s a compromise, what is better: to write more often but more crappy stuff or write only when I have something to say? Dilemma.

I am proud of the following posts:

Things that blew my mind.

  • That my post on the 7 classic foundational vis papers had almost 4000 visits on the date of publication. People are thirtsty for knowledge!
  • That some renown researchers in the field are reading my blog and contact me for the things I write.
  • That some people invited me to talk for my blog and not for my research work (though a bit disappointing! :-))

Special thanks to …

  • Robert Kosara: for showing me  with his blog that it was possible to write a blog like FILWD.
  • Andrew Vande Moere: for instilling in me some doubts before I started.
  • Prof. Tamara Munzner and Prof. George Grinstein for giving me so much fuel.
  • The Data Visualization Cartel: you guys know why.

Plans for the future?

I always have plans for the future which I regularly abandon and the list is so full of stuff that I know I will never do it all. So what can I say? Maybe you have something to suggest:

  • How do you see FILWD evolving in the future?
  • What are the posts you liked the most and would like to see replicated in the future?
  • Is there anything useless in FILWD that I should definitely stop doing?

I can anticipate a few things I’d like to do:

  • Do more videos, especially if they are fun.
  • Create the FILWD Newsletter to have a more intimate communication channel with some of you.
  • Create an e-book out of the Beginners Toolkit.

What do you think? Do you like these ideas. Do you have anything to suggest to make them better? Thanks.

With Love,

Visualization Consumerism

consumerismA few days after my post on indispensable visualization I received an email from Prof. Georges Grinstein (what an honor! he is one of the fathers of visualization) asking for explanations about my use of the word “visualization consumerism”. I defined visualization consumerism as visualizations found on the web, used solely for communication purposes: mostly static, with little interaction and digested information.

Georges rapped a bit over my knuckles, and I understand why: “The largest applications involves mapping. Can you drive without some form of a map? Yes, but even GPS dependent individuals look at the images most often. That’s great consumerism.

Sure I see the point. I was a little too hasty in defining the whole set of visualization for communication “visualization consumerism” and I am ready to amend and apologize. But  “visualization consumerism” does exist and in this post I intend to explain what I mean.

Defining visualization consumerism

I define visualization consumerism the careless production and shallow consumption of visualization. Consumerist visualizations are pre-digested depictions of data with the only effect of generating an “how cool!” effect. They do not inform, they do not let you think. They do not even pretend you to spend some time thinking. At best they entertain, but only for a few seconds: the time to click, give a look, say “cool”, and leave.

Visualization consumerism is an interplay between the consumer and the producer. It’s an attitude. It’s a feedback loop between the two, like consumerism in general. Eventually, you don’t know whether companies produce crappy products because consumers want them or the other way around.

Consumerist designers don’t spend too much time thinking or simply lack the knowledge, culture and attitude necessary to build “sophisticated” artifacts.

Consumerist consumers are people surfing the web, maybe with a slight interest in visual things, who stumble across a visualization and just say “cool!”. Again, they lack the knowledge, culture and attitude necessary to appreciate good design and to distinguish it from crap.

The origin of visualization consumerism

Am I a dogmatist, fanatic, orthodox, conservative visualization theorist? No, because I don’t blame anyone for visualization consumerism. I just think we have to recognize it and act in a way to let a larger portion of our society be able to make beautiful stuff and appreciate it.

Visualization consumerism is not different from many other effects we observe today, especially in the way we consume information on the web. Reading full articles has become a luxury, we just skim over everything, and of course we do the same with visualization. Finding good articles has become a luxury (by the way, this is why I strive to write long quality articles instead of a kaleidoscope) and again visualization is no exception.

Who’s to blame for that then? No one. Really, I firmly believe the large majority of people are honest and well-intentioned. It’s only the system we live in that produces these effects. And by the way I have no intention to write a long rant against modern society because I totally love the 21st century.

It’s also not my intention to point my finger to this and that designer so that I can generate a lot of voracious comments between two gangs: the purists and the creatives. I am personally annoyed by Stephen Few’s crusade against David McCandless, I think it’s detrimental, narrow-minded and, more importantly, disrespectful.

What is to be done?

Said that, what can be done? Should we just accept it and passively complaint about how mistreated “pure” visualization is? No. I think we have to acknowledge the problem and do our best to educate people. But wait a moment …. educating people is a dangerous idea! I agree. But let me explain what I mean. When I say educating people I mean doing it bottom-up; by giving the right examples and striving for creating a thriving environment:

  1. Providing people with excellent study material
  2. Showing people sophisticated and enlightening visualizations
  3. Providing people with professional (and gentle) criticism
  4. Showing our radical passion for excellence

I know. It’s tough. But that’s the deal guys if we want to do something. I know very well from my own experience how many moments in life lead us to produce and diffuse sub-optimal stuff. I don’t deceive myself with impractical ideals, and I am not ingenuous, but I am convinced we have to lean towards doing great stuff. Always. And this makes a difference in the long run, I am sure.

Is visualization for presentation consumerism?

Going back to the initial question and Geoges’ email, a clarification is due. No, I don’t think using visualization for communication purposes is consumerism. My initial reaction is due to the fact that I see a disproportion between the use of visualization as an exploratory tool versus a presentation tool. Up to the point that people might get an impression that communication is the only purpose of visualization. This is what Georges had to say:

Visualization really fits into 3 classes which are exploratory, confirmatory, and presentation (see data vis book). Now exploratory visualization is only as good as the tool AND the analyst. A great analyst will be able to use visualization to generate many hypotheses. Once these are there then the next step of course is confirmatory and most often stats is the only too used. However combining stats and visualization provides a more grounded explanation of the variations in the numbers, more context, more connection with other hypothesis. Finally the presentation is what I would call your consumerism and we’ve not had many tools to make that fluid (from the exploration onto the presentation).

Totally agree. What is missing is the connection between communication and exploration.

We all encounter daily, daily, presentation visualizations. Most encounter confirmatory ones rarely as that’s the realm of researchers and statisticians. Very few encounter exploratory ones. But most often the presentation ones are the result of a great deal of exploration.

So true! I’ve heard so many visualization designers talking about the painful exploratory process needed to produce a visualization for communication. And I can testify myself that this is always the case.

But again, what kind of impression do we want to give to people? Do we want to send the message that the main purpose of visualization is communication? Do we want people to only consume visualizations made by others? Or do we rather want to empower people with powerful “tools for thoughts“? I still believe we have to help people discover how visualization can be an indispensable tool for them. I want to realize the big vision of augmenting the human intellect not to making it flatter.

Is visualization for the masses consumerism?

In his email Goerges mentions “visualization for the masses” as one of the main challenges for visualization. It’s a buzzword that started circulating a few years ago when ManyEyes was launched and, as far as I understand, it means giving people easy access to visualization and enabling collaboration through visualization.

I must confess “visualization for the masses” does not make my heart beat faster (I am sorry Georges). I think the word “masses” is really unfortunate and resonates too well with consumerism. Who are these masses? Do we really want to give visualization to the masses? No. I personally want to give visualization to Paul, Cindy, Frank, and Anne … people with a real face and a specific need. I think it’s very dangerous to think of a generic audience in need of visualization, because there’s nothing like visualization for everyone.

My opinion is supported by the large failures we have seen in developing general purpose visualization services on the web like ManyEyes and Swivel. Even big and successful product like Spotfire and Tableau, which seem to provide general purpose solutions, started with a very clear target population in mind. The way I see visualization used most successfully is when it is designed and targeted to a specific population (often a small one) not for a general audience. Even less for the masses!

But if visualization for the masses means trying to push for the diffusion of tools that can support the full data visualization process and allow some people learn to use visualization and not only consume it, that’s definitely great! I am not against it. But I just won’t call it “for the masses”!

Data Visualization is NOT Useful. It’s Indispensable.

(This blog post is the result of a talk a gave a few days ago at Visualizing Europe. You can give a look to the program, you can give a look to my slides directly, or you can see a preview of the pictures taken during the event. Also, this is a quite long post, I am sorry. But I really needed this space to turn my slides into a blog post. If you don’t have time to read it now keep it for later.)


I just came back from Visualizing Europe, a great event organized by in Brussels to gather several key data visualization actors in the European scene and discuss the state of the art and potential future of visualization.

I had the honor to be invited in the panel session organized by Andrew Vande Moere to discuss the power and potential of visualization together with a bunch or great data visualization designers like Santiago Ortiz, Moritz Stefaner and David McCandless.

To tell you the truth I felt a bit like being the black sheep; but not with a bad feeling! I was the only one in the panel with a core CS visualization research background and the audience too was not to “researchy” as the usual audience I am used to talk to. What a wonderful occasion I thought!

So, when I got the invitation I started thinking about a talk that could send a strong message and stick into people’s mind. And suddenly I recalled I blog post I wanted to write since a long time about the questioned usefulness of information visualization; a complex that afflicts many of us.

I was originally inspired by a message I received from Jorge Camoes (ExcelCharts) in which he pointed me to a couple of interesting blog posts on visualization usefulness.

The first one is from Jorge and is titled: “Is Data Visualization Useful? You’ll Have to Prove It“. In this post he argues that believing that data visualization is useful is an act of faith and that we have no scientific evidence of its usefulness. Along similar lines the second post is from Stephen Few who in his blog post “True Stories about the Benefits of Data Visualization” confesses he has a hard time convincing people of visualization’s usefulness.

After reflecting a little bit about their content and their frustration I recognized the same frustration in me and decided it was time to react. And my reaction, I decided, had to be a strong one. I realized in fact that the whole problem of visualization usefulness can be removed if we change our mindset. I realized that one of the reason why we ask ourselves whether visualization is useful or not is because we don’t have a clear focus on those problems in which visualization is not just useful, but plainly indispensable.

In the rest of the post I’ll try to articulate this idea further, with the hope I will convince you it is important to change our mindset and focus more on problems where the net gain of introducing visualization is extremely high. But let me start by providing a few examples of problems I have personally experienced where I think visualization is clearly indispensable.

Examples of indispensable visualizations

In the following I provide three example of indispensable visualizations. I hope this will convince you that when visualization is indispensable it is evident.

Example #1: Bart and his moving animals
Bart is a biologist and he studies animals. More precisely he wants to understand how animals move and how contextual factors influence their behavior. Think birds migrating from one place to another exploiting the right wind at the right moment or lions searching for a prey and trying to optimize their energy expenditure.

Problem is that animal movement analysis today is a very technological endeavor. Animals have GPS and other sensors attached and can be tracked continuously around the world and generate enormous amounts of data. Plus, this data can be enhanced with contextual factors like weather conditions, making the whole thing more difficult. How do you deal with that? Can you do it without visualization? The problem is that not only they want to test some hypotheses and assumptions they have, but they also want to be able to come up with new ideas.

My colleague and dear friend Florian Mansmann (together with his valuable students) helps Bart and his colleagues understanding how these animals move, and developed a nice tool to visualize traces in time and to correlate them with contextual factors.

animal movement visualization

Again my question is: do you think it is possible to do that without visualization? I think this is another case of indispensable visualization.

Example #2: Joachim and his hidden molecules
Joachim is a chemist at the University of Konstanz, where I work, and his job is to study small molecules that inhibit a fundamental process in cellular division called “cytokinesis”. I am not going to give any biology lesson here, I am totally inadequate, but it suffices to know that  Joachim studies this stuff because it has implications on understanding cancer. Yes, cancer. Serious stuff.

Problem is that modern biology is done with computational tools and robots and he has to deal with tens of thousands of chemical reactions at once. He uses a technology called High-Throughput Screening by which a biological target can be tested with thousands of molecules in parallel in a matter of few hours. Isn’t it crazy?

But these data must be analyzed then and again there are no clear-cut preconceived hypotheses. Joachim has to delve through long excel spreadsheets and make sense of them. How primitive is this? And how indispensable is visualization here?

So we developed a simple tool to let him explore the association between the activity level of these molecules (how much they react to the given target) and their molecular structure. Nothing too complicated from the visualization point of view: a very interactive and flexible scatter plot with well-designed mappings.


When I showed the capabilities of visualization to him he was blown away, he couldn’t believe this was possible. Won’t you call this indispensable?

Example #3: Security officers keeping us safe
Even if we don’t notice it in our daily life, there are a number of people around us whose job is to monitor our infrastructures to keep us safe and to ensure that we have a steady delivery of fundamental services. We just started a new project here at the University of Konstanz to develop visual analytics tools to help this people monitor big amounts of information in real-time. These are for instance the guys who monitor the power grid infrastructure, a monster with millions of nodes spitting data 24hrs a day, and ensure that you can always recharge your PCs, switch your TV on, or heat up your food with your microwave. These guys do their job in control rooms like those depicted in this picture.

control room

Of course, this data monster is by no means tractable by visualization alone, data analysis and reduction algorithm, as well as complex simulations are needed, but do you think it is possible to do without visualization here? Do you have the feeling it is conceivable to make sense of what is going on there without visualization? I don’t think so.

But that’s a handful of highly skilled people!

A common objection I get when I explain this theory is something along these lines: “ok Enrico this is true, but how many people are in the condition of desperately needing interactive visualization up to the point it is indispensable? The people you describe here are a few very skilled knowledge workers which represent a tiny proportion of the overall population! Visualization has the power to affect a much much larger group”


I agree. It’s actually true that these are a few skilled workers if we reason in terms of proportions. Nonetheless, we have to realize two things:

  • The “tiny” proportion is millions of people. It’s not small! Jerome Cukier from OECD approached me after my talk and suggested: “You know what? I think you underestimated these numbers. There are 500 millions Excel users around the world!” Good point.
  • The “tiny” proportion are those who work to make our life better. There is a whole army of skilled professionals who study to:
    • find solutions to cure our diseases
    • find solutions to reduce poverty
    • find solutions to make our planet cleaner and safer
    • find solutions to provide innovative and pleasurable products
    • find solutions to keep our cities safe
    • … add another thousand rows

Shouldn’t we spend some time to help these people? I think we certainly have to. If we have the tools with the potential to expand their mind and do great things we have the responsibility to do it!

Visualization use and the cartography cube

Finally, I think it’s necessary to reflect on how we are using visualization today, how we could use it in the future, and how this is related to indispensable visualization.

Fortunately, while thinking about the idea for this talk I stumbled upon a very useful diagram of visualization use. I found it in Alan MacEachren’s “How Maps Work” (by the way, this book is blowing my mind, it’s not at all only about maps and if you are very serious about visualization you should read it).

The model describes visualization use according to three main dimensions:

1) Private vs. Public: Is the visualization targeted to one person or a restricted group of people to solve a specific problem or it is more intended to address public audience?

2) Static vs. Interactive: Is the visualization a static representation or an interactive tool that people can use to explore alternative views?

3) Revealing Knowns vs. Revealing Unknowns: Is the goal to communicate a number of findings or messages to an audience or to provide people ways to explore the data and make sense of it?

My feeling is that visualization today, especially if seen under the lens of what the web and similar media offer to the average reader, is largely used as a communication tool, that is we are at the upper corned of the cube: largely public, mostly static or with little interaction, and mostly to reveal information that has been digested by someone else.

cartography cube

I call this use of visualization “visualization consumerism” (I hope nobody is offended by this name, it’s not my intent), that is, the visualization is prepackaged by someone in a format that can be easily and quickly digested by a large number of people. To be clear, I am not against it, as long as it is done well. Visualization Europe was full of fantastic designers and I love their work. But I think we should do more. Visualization is not only something to consume, we need to look at it in a different way.

What I propose is that we take more care of the knowledge workers I mentioned above and provide them with the indispensable tools they need. I call this: cognitive cyborgs. I think we have the great chance here to help people become cognitive cyborgs. Visualization, as I explained in an older post, has the power to make us more intelligent and we should strive to do that.

And looking at the cube I think we have to shift our attention to the opposite corner of the cube if we want to help people become cognitive cyborgs.

cartography cube

We have to build visualization for private use, highly interactive, to allow easy exploration and focus on revealing unknown, that is, help people generate new knowledge.


I will be totally candid with your here. It’s evidently a bold statement to claim that visualization is not useful but indispensable. To some extent it is provocative on purpose. And it is also dangerous to claim that visualization is indispensable, because some problems might benefit from it but still be solved without it. But I really think it is important to change our mindset and focus more on the impact we might have.

I confess I believe it’s not a totally black and white here, there are several shades of gray in between. But I see a huge opportunity cost here: the more we spend our energies on communication issues, the less we can spend on putting these tools directly in the hands of the people who need them. I see an army of incredibly skilled visualization designers that could do a lot more if only they had the opportunity to work on projects where visualization is really indispensable.

The problem is that this opportunity is not easily visible because many of the people we could empower with our tools don’t know it! That’s the real challenge.

I know, this is a very personal point of view and I am ready to accept some criticism, but that’s it. That’s the way I think and I would love to hear your opinion if you do not agree with me.

I personally don’t want to see visualization trivialized to a mere communication tool of digested data. I want to see cognitive cyborgs all over the place. They are everywhere guys! They are waiting for us but we have to chase them. Let’s make this happen, there are several opportunities at stakes here! Good luck.