I was going to let this one slide, but people kept commenting, essentially trashing FlowingData, and that’s just not cool. As you might recall, I put in my picks for the best data visualization projects of 2008 a while back. They were the fine work of statisticians, designers, and computer scientists, all of them beautiful, and all of them built to tell an interesting story with the dataset at hand. None of them were traditional graphs or charts.
Design Sucks
In a post titled Better late than never earlier this week, a friend of Andrew Gelman’s responded to my picks: “Does this stuff suck? Or am I missing something?”
Andrew replied, “Yes, I agree. They all suck (for the purpose of data display).”
I didn’t think all that much of it. I knew what Andrew meant (after I got over my initial shock). My project picks don’t work as analysis tools, and that’s true, because they were made for presentation more than anything else. The comments that followed, however, were what got me going. They serve as yet another reason for me to believe that statisticians (and a lot of the analytically-minded), in general, are clueless about (or unjustifiably against) visualization outside their own field of expertise. It’s this sort of narrow-mindedness that has kept statistical visualization looking pretty much the same for the past few decades. Sure, add some interaction here, change a color there, but take that away and you’ve got the same stuff John Tukey was writing about in Exploratory Data Analysis (1970). Don’t get me wrong. Tukey is great. This was before computers though. I just got done reading about how to draw my graphs with a pencil and to use a pen for extra emphasis.
Boo Technology
Data has changed since then my fellow statisticians. We don’t draw our graphs with pencils and use a pen to make things bold anymore. We have computers, and believe it or not there is software and programming languages that work better than R in some situations. Again, don’t get me wrong. R is excellent. I’m just saying you don’t have to do everything with it. Like data parsing. Python is pretty good at that last time I checked.
Look at the cool stuff coming out of the MIT Media Lab or HCIL at Maryland. We should be doing that type of stuff. Experimenting. Playing. Trying new things. Computer scientists and designers are doing it every day. Some of the stuff might be a bit clunky or useless, yes, but at least they’re thinking about (and implementing) new ways to explore. That’s how we learn. Why not statisticians? But no. It’s all about bar graphs, scatterplots, time series charts and ways to combine methods we already know. Everything else is labeled chart junk, which seems to have developed in to the visualization equivalent of meh and epic fail.
Maybe this is because of a lack of technological know-how. After all, statistical computing is still a relatively new idea, but still, open up your mind to the possibility of something new. Traditional statistical visualization will only take you so far, and sooner more than later, the statistical tools that you are so accustomed to won’t be enough. You shouldn’t have to fetch a computer scientist to set things in motion. Don’t knock something just because it takes more than a moment to understand.
Garbage
There was even one commenter who went so far as to call most of what appears on FlowingData garbage, which is absolutely unacceptable. Andrew, who I feel is more on my side on this one, disagreed. I, of course, completely disagree. This isn’t just an insult to me and what I do, but to every person and group I’ve ever posted about. Stamen Design. Jonathan Harris. Martin Wattenberg. The New York Times. Brad Paley. Google. Seriously? Garbage? No way. I don’t need to explain why.
As for one of those ending comments that said yesterday’s guest post was “possibly the worst, least informative, most gimmicky presentation of data that I’ve ever seen,” well, what can I say? That commenter hasn’t seen much.
Statisticians, of all people, you should understand there’s more to data than just the numbers. I know I do, but if it’s sparklines that you want, go ahead and stick to those, and I’ll continue with my garbage. Let’s compare notes in 10 years.
End rant.
Nathan,
I agree with you. While I don’t agree on the top 5 for the year I do think they are not crap.
One of the things I think that is being missed by the comments is that data viz as you, and I, appreciate it offers a new way of understanding and importantly engaging with data.
While there has historically been a push to measure “effectiveness” of data viz or charts, we have, with new technology and more importantly borrowing from stats, comp sci AND design, the ability to democratize data and take it out of the ivory towers that the people commenting on the other blog seem to reside in.
There should be an obligation not just to make effective data displays BUT also to make engaging visualisations. If people can engage more with the story of the data through the use of impressive visuals and storytelling/narrative techniques all the better.
I wonder if there was such a discussion when stories became silent films became block busters such as Star Wars….the story underlying it is as old as time but the WAY it is told beats anything that has come before.
I’d say that the statisticians _are_ willing to play with the new technology, but they are not willing to make the leap you refer to – “the story of the data through the use of impressive visuals and storytelling/narrative techniques all the better”.
A statistician, at heart, is trying to convey information through data visualization to show relationships, but the difference is they are trying to tell the story of the relationships of the data, not the story of the world. Purist statisticians (of which I have been trained by my share) will contend that the data is not supposed to tell a story, the reader should be able to see an objective set of data. Visualization is a tool for analysis and showing a density of numbers. The numerical relationship is the story, not as you and I believe the story beyond the relationship seen between the numbers.
In other words, everything you said.
Thanks for this, Nathan. I appreciate that you’re taking the long-view on data visualization. I think it’s true with any highly specialized field — most people in the field are operating on the parameters that are already defined, trying to string together sentences by putting together words in an optimal arrangement. When a new word comes along, the gut reaction is to try to fit it into what we already know, when in reality, it’s could be a part of a new language.
The first films weren’t films; they were moving pictures. They merely visualized the data of still photographs laid in rapid succession.
Hi Nathan
I like your site. Some of the stuff you find is great, some less so I would never trash it as ‘all garbage’. However the critics though overstated have a point. You say:
‘It’s this sort of narrow-mindedness that has kept statistical visualization looking pretty much the same for the past few decades’
Yes the desire to most concisely represent or summarise the data with the least distortion does hold us back. As it should.
Computers are a huge advance in the ease of production of graphics– but not their actual form. A screen is still 2-D and the same rules apply. The one great breakthrough in form is the interactivity and animation that computers allow.
So I like it when visualisations represent the data with simplicity and panache and use the strength of computers (Britain form Above)– but sometimes flashy graphics obfuscate the data (cough cough …Circos).
Nathan, I really understand your standpoint. Some of the comments really were unreflected and seemed driven by the mood set by the post itself.
When it comes to data display some of the projects from 2008 were more focused on telling a story than analyzing the data to it’s details. I think we’re on the verge to have a lot more data openly available (governmental, ecological, etc.). Thus a much larger audience that isn’t necessarily trained in data analysis but is eager to learn about the data must be addressed. We have to learn how to make this data digestable to this kind of viewers. Finding ways to communicate cause and effect in new ways may include some missteps but will ultimately make the data readable to a broader readership.
I think we should embrace new technologies to discover better methods to communicate the ever changing datasets. Furthermore these methods should address the reading habits that have massively changed over the past decades.
All I can say is: keep up the good work.
Nathan, I love your burst of emotion. I do recognize myself in your frustration, even more than you can imagine.
However, I think that almost of your commenters truly stated “their” truth, from their (“narrow-minded”) point of view, expertise and experience. Realize that you are “preaching” to people who might shift, but will never convert to your view. This is because really, the examples you show here (and those on “information aesthetics” probably even more so) are not examples of data visualization or statistical analysis. Yes, a Tufte sparkline will always win, even against a Jonathan Harris.
Instead, your projects point to a new approach, a new field, one that still needs to grow its own definition, values and critique. Trying to push them as things they are not, will always provoke such harsh reactions from those protecting their turf.
I also know these types of discussions, and totally agree with Andrew: These critics are comparing pears with peaches, so we should not make the same mistake. I do not think people in general are not open for the playful/artistic, but apparently many judge an artistic/experiential/metaphoric project with the same set of rules as a scatterplot or the Tableau software. Which, obviously, begs the question – what ARE established quality criteria for information-aesthetic works? Maybe we should work on that :)
Well, I am a psychologist and I think data visuals you exhibit go a long way to drawing people into discussions where they can developed a shared sense of a situation and find ways to act collectively.
We still need back room statisticians and indeed the more people play with statistical displays, the more call there will be for statistical work and the more funding will become available.
Trashing ain’t cool at all. There is a well known anecdote of about a young don who made some cutting comments in a seminar at Oxford. An older don drew his aside afterwards and said, “Everyone here is clever. The trick is being nice.”
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Part of the problem is that the “visualization” label is slapped on so many different things that have different goals, approaches, audiences, etc. Everything from statistical graphics and visual analytics to info graphics and data art is called “visualization.” So it’s not surprising that different people with different goals will apply their criteria to what they see and come to different conclusions.
We need to figure out what different kinds of work there are, what their goals are, etc., and what to call them. Even within the academic field of visualization, people talk about exploration, analysis, and presentation – but the criteria for each are far from clear, and work still tends to focus on the first two.
To an extent, I can also understand the frustration of people on the data analysis/statistical graphics side. The ugly but useful stuff doesn’t get much attention, it’s the pretty stuff that gets on Digg, etc. That ends up distorting what many people get to see when they look for visualization. If anybody is interested in another rant, I’ve got one on exactly this topic from the academic point of view.
Nathan: I’ll reply at greater length on my blog (with an anecdote or two) but in the interest of avoiding a flame war between the commenters on my blog and the (far more numerous) commenters on yours, let me emphasize that I thought the graphics you displayed “look cool but I don’t think they do a particularly good job at conveying information.” I also strongly disagreed with the person who referred to the stuff as garbage.
Beyond that, your comments above have caused me to shift my position somewhat. here’s why.
When I posted the blog entry the other day, my thought was, first, that it’s great for people to be experimenting with these sorts of displays, and second, it’s great that these sorts of graphs advertise the concept of large datasets; for similar reasons of promoting numeracy, I enjoy the pictures posted here:
http://www.stat.columbia.edu/~cook/movabletype/archives/2008/02/plotting_a_mill.html
But I was, on the whole, unhappy with the graphics that you posted because (a) I felt that they attracted attention to themselves more than they displayed the data, and so (b) I wasn’t so happy with them being labeled “best of the year.”
To take a specific example–and recognizing that we may just disagree about what we find beautiful or interesting or helpful–I found the Baby Name Wizard to be far superior, as a tool for conveying information, than any of the examples you gave. If, for a moment, you accept this judgment, it leads to the surprising conclusion that the 5 best developments from 2008 were lower in quality than something that was done in 2005–which is a bit of a disappointment given the improvements in technology.
To get to specifics: I think Tukey (and, for that matter, Tufte) are great, but I completely agree that the work of these pioneers shouldn’t limit what we do today. I’m sorry I was critical of your blog entry, but I never used the phrase “chart junk,” and I think you’re being unfair in lumping all statisticians together. My criticisms of the graphs you posted were more specific and, despite what one of your commenters wrote, criticism can be helpful too. For example:
– Wordle conveys a small but important amount of information (the most common words in a document and their relative frequencies) in what I see as a confusing way. Again, it it’s eye-catching, I can see that it is doing a service, is making the world a better place–after all, the alternative to people using Wordle is not, in general, people using something better, but rather people not using Wordle and thus not learning what the most common words (other than “the” etc, are in their documents). But I don’t have to like it!
– I dislike the Decision Tree because I dislike the model it is based on, and I think it leads people to a confused understanding of voting. Here, I think the world would be better if nobody were to see this graph. I’m not really complaining about the display, more about what it’s displaying.
That said, I agree with you that we only will learn by trying new things, and that, for graphics, it’s good to have new tools. Who knows if the eye-catching graphics you display in I Want You to Want Me might be altered to display data in some informative way? So I don’t want to discourage experimentation. To be fair, all of your examples show that “data is more than a bucket of numbers,” as you put it.
The perils come when a snazzy display is used to obscure information. This has happened many times with pie charts–the high-tech, snazzy, attention-grabbing, beautiful graphical tool of the 1970s–and they can happen with the best data visualization ideas of today.
Statisticians should have a way of pointing this out–of connecting the visualization to the ultimate goals of subject-matter understanding–without alienating people and thus obscuring our own message, which is apparently what I did in my own blog entry. I apologize for that.
One more thing I should add: if we can figure out what the differences are between the different approaches, we can actually learn a great deal about what they really are. So these differences can drive us forward, if we’re willing and able to figure them out (and not just insist that our respective approaches are The One Way). I have work under review at the moment that discusses several criteria that help differentiate within InfoVis (the academic field) and also to separate it from different neighboring fields.
Richard Hamming’s dictum comes to mind: The purpose of computing is insight, not numbers.
The more that can be done to raise level of the journeyman’s use of formal visualizations in public settings and their comprehension by the public at large the better off we all will be. The level of functional illiteracy and innumeracy in the USA is too high to offer anything more than the most prescriptive of rules and examples — ie, “use visualization X when you need to convey data Y”.
Nathan et al,
The site content and your “garbage” are terrific. This right-brain, visual learner has grasped concepts here that would have required hours of reading and re-reading to understand. Thank you for providing explanation and identifying the key features of these amazing projects. As ‘Ben’ and ‘Jo’ commented (earlier posts), these tools give guys like me cause to rethink my own understanding of the facts.
While I don’t understand some of your graphs at times and others will never serve any purpose for me in my business or education, I still love the fact that they ARE unique and different and use something beyond the same slides of data that we have seen for years.
Keep up the great work.
Wonder if they, they ranting statisticians, have even had to sell an idea (or product), convince the public, or persuaded anyone of their story. Or if they’ve read “Made to Stick” or “Freakonomics” to broaden their myopia about how people learn, remember, and form their beliefs.
This emotionless view of the world is what we (everyone) continually underestimate. I think Alan Greenspan’s mea culpa last fall underlines the point that no matter how smart, or amount of resources, statistics, ideology and theory still never truly predicts human nature.
‘Wonder if they, they ranting statisticians, have even had to sell an idea (or product), convince the public, or persuaded anyone of their story.’
No we are all unemployed, illiterate, and play no part in public policy.
Nathan,
Great posts and great blog. I enjoy reading your take on visualization and data presentation. Keep the great posts coming. If everyone is happy with what you’re doing, then you’re doing something wrong.
Thanks,
Nathan W.
I do not follow any stats blogs except yours. ‘Great stuff, cool links, insightful commentary and neat catches. Thanks for all you do. “Non illegitimae ex carboratum”!
Even when I am underwhelmed by a particular technique or example I see here, I am glad to see it here, and get a change to judge it myself.
Keep up the great work, Nathan, this site is the opposite of garbage.
Nathan, wonderful blog. I’m not involved in data viz at all but developed an interest after Tufte’s 1-day seminar; your blog satisfies my appetite. Everyone is (or should be) wrestling with how to best display content to satisfy the needs of the intended audience — a principle embedded in the minds of user experience practitioners everywhere. Anyone who isn’t oriented that way isn’t creating useful (for the intended audience) visualizations.
Nathan,
I stumbled on FlowingData at the end of 2008. I started showing it to student friends and then professors who had net yet heard of your site. What you post is PERFECT fodder for imagining how to present data and information. Not all info is quantified, but is, as you suggest, visual. Thanks for the inspiration in condensed form. It’s rare that anyone actually finds something inspirational. That fact that you have managed to compel people to do more and think more about these things, means that you’ve accomplished a great deal. As with all things good, keep it up!
Nathan, I love seeins some of the ideas for data visualization here. You are an innovator in data vis., and as you have seen, innovation doesn’t always go as smoothly as we might like. As a statistician myself, let me suggest two fairly straightforward takeaways.
The first is on static data displays. They should be chosen to tell a story that would be confusing in words alone. Thus, a static plot ought not use more ink than needed to clearly tell the story. Some extra ink can be useful. But too frequently (although NOT always), extra ink or so-called “chart junk” can bias the interpretation of the graph. In short, a good chart should follow the classic journalism ideals of brevity, impartiality, and “multi-facetedness” should be followed here.
The second is on dynamic data displays. These are desperately needed. However, the software to create these is not yet as widely available as it needs to be. Further, too many dynamic displays are too complicated to use to the fullest of their abilities. Sure, there are some wonderfully complicated interactions out there. We haven’t found a way to communicate many of these in ways that most average people (or even most Ph. D. statisticians and computer scientists) can understand without significant training and explanation. The lack of simplicity is a turnoff.
I hope these points make sense. And please don’t interpret these comments as calling your work “garbage”. It is not. You are encouraging the development that NEEDS to happen in this field.
Nathan … I think the fact that people are having such a passionate debate about this stuff is a value. There will always be jerks who get petty and personal in lively debates, and it can be difficult to deal with them on the internet. But there is also some substantive critical commentary as well.
I don’t often comment here, but I personally like hearing people disagree on what’s effective and what isn’t … and provide reasons. But, yeah, those just trashin’ it for the sake of it are a waste of space.
I don’t ever post here, but I was amazed to see such a heated post and wanted to add my two cents.
First of all, I think you should always realize that there will be people who don’t appreciate what you do. I really enjoy this blog, but for those who have a problem with it, I would just forget about it. Although some of the people’s comments may have been over-the-top, I would call this response an overreaction.
For example, I went and looked at Gelman’s post, and to me it appears that you took the “garbage” quote a little out of context. The comment says “…for an analyst who is trying to model the data, most of this stuff is garbage.” I would somewhat agree with this – not to the point of calling it garbage though. I think it all depends on the situation. I think the stuff on this site is great, especially for exploring ways to effectively communicate data. But in the analysis stage where I’m the only audience for my visualizations, I really don’t care about fancy animations and such. Maybe once I have adequately modeled the data, I would look to examples such as those seen on this blog to better communicate the results. Hopefully this point makes sense, and maybe makes some sense out of why some people may not appreciate what you have here.
Nathan, thanks for the wonderful blog. I don’t agree with all your rankings, but I do visit every day to judge for myself what I can use in my work. You provide a valuable service.
Just yesterday I was showing some new Flex tools to a senior manager at a billion dollar company and he asked if I read your blog! That’s mind share in the market!
I don’t have the skills yet to reproduce many of the types of vizualization you show, but they all have value – even ducks have value in duck recognition!
Are we sheep or are we men? It feels good to do something different, and we might even learn something new.
To me “data visualization” is one part graph and one part marketing, and too much of either is a bad thing. Too much graph, and you have just that–a boring graph. Too much marketing, and it’s all bullshit pretty pictures. So let the experiments continue…
Many years ago i went to a visualization conference, and one of the presenters gave a talk on how sophisticated rendering techniques had resulted in visualizations that were sexy-looking but uninformative. As an example of a “bad” visualization he used a picture of a protein surface that, incidentally, had been made by a friend of mine. To me it was very informative– i could even identify the specific protein and the active site. He said it looked like a “handful of popcorn”.
I think people have certain expectations of a visualization, and when it doesn’t immediately meet those expectations few are willing to look harder.
As someone who creates a lot of the ‘pretty stuff that does well on digg’ I certainly get my fair share of hate mongering from the statisticians and neo-Tuftists. But it really comes down to communication at the intended audience. You can’t please everyone and there will always be spiteful, jealous, or other wise inarticulate critics.
This blog and your work are great though. If you highlight some chart that isn’t up to snuff, then it will likely get savaged in the comments, that’s the purpose of discussion.
People also need to chill out. Sure wordles are lame and should not be used in your PhD dissertation, but that’s not their intent. Wordles are fun and interesting for 15 seconds.
You should not get upset by people who do not understand data visualization. These people will be left behind while you will be having a blast making sense of our data-driven world ;).
Every site has it’s set of trolls. If they don’t like the content, then they should go to another site. Personally, this is one of the few sites that I really enjoy reading just about every post.
I just recently found this website by chance, and I must say, it has opened my eyes to something I hadn’t thought of before. All through my scientific life I’d been taught about how best to put forth data, whether they be graphs or tables. Usually this consisted of just making things readable and whatever conventions the professor at the time subscribed to. This rather dry approach to data has made me loathe the lab sections of my courses but this website has given me a new appreciation for conveying data in an interesting way. I love what I see here, and I hope I can take away something and bring it into my academic life and make things that much more fun and interesting. Keep up the good work!
Nathan, I am also contributing to Andrew’s Statistical Modeling, Causal Inference, and Social Science blog – and I found one of your top 5 examples fascinating enough that I’ve twittered about it. Moreover, your blog is on our blog roll. We code in Python, and parse our data. The cultures aren’t at all that different. The visualizations were strong on data, strong on visuals, but there are elements where statistical modeling could make them even better. Let’s join forces!
Of the 140+ blogs I follow (I might be a blog junkie), yours is the first one that I read every day. Not only is it my favorite, but it has impacted my work and the way that I have led the companies I have worked at to try something different. That should be encouraging!
I love your site. Wanted you to know.
Nathan,
Today’s rant most definitely sprouted into a lively and productive debate. I discovered your blog shortly after you started it and always enjoy your discovers and insights. I can’t say I like the examples in every post, but they do demonstrate your intent {link to ABOUT}. It seems you know your audience, and I would say you are true with your voice.
But your audience doesn’t know you and our intention, and criticize as if they expect to find an orange in an apple orchard.
As you state, FlowingData is about the understanding how the data flows. Jack’s example that films are actually moving pictures is a good analogy. Data points are static. They are points in time. Group together, and laid out in succession they weave time and place together, exhibiting movement and progression. It doesn’t matter whether the deliverable is bar chart, box office receipts, or the Britain from Above clip, data tells a story that makes things more evident.
I feel the important question — the one that any statistician or data-jockey needs to pay attention to — is What story is being told?
When I see an image of one million plastic cups, should I try to interpret the patterns in the image, our simply take away “Damn! That’s a lot of cups.” The image draws us to the data point and creates, as Chris Jordan clearly intends, to “have different effect than the raw numbers alone.” It creates an emotional reaction. As such, do we argue that his images aren’t statistically accurate patterns. If that’s the expectation, then we as the audience don’t understand the medium, let alone the message.
But this brings me a general criticism. First, let me be clear that I find Chris Jordan’s work compelling, thought provoking, and thoroughly enjoyable. But the pieces are an obvious manipulation of data in order to promote the messages. Again, that is Jordan’s intent. It just reminds me remain objective when working with data; to let the information reveal itself and not let the tools shape results. The power of computing makes my job easier, and the new tools (Tableau) offer new perspectives, but I still keep the quote attributed to Benjamin Disraeli in mind; “There are liars, dam liars, and then there Statisticians.” Again, I admire Jordan’s art and it’s intent, but I’m not about to confuse the context of the data for the images.
As an explorer, Nathan, you’re moving away from edge, from traditional paths, and discovering the shape of the world. There will always be some who maintain the world is flat, despite evidence to the contrary. The challenge is to make the discoveries evident and not obscure the message with the medium, or for the sake of the medium.
Keep up the great work.
“The evolution of culture marches with the elimination of ornament from useful objects.”
Adolf Loos, Ornament und Verbrechen (1908)
I know what I like to look at and don’t need anyone to tell me.
Hi there –
I work in market research. I’m not a statistician, but have to make choices everyday on how I present findings (qual and quant) to clients that will engage them.
I LOVE this site – I love seeing how others faced with the same challenge come up with creative ways to present facts that makes them come to life. This site challenges me and inspires me!
Thanks for taking the time to pull together so much interesting data visualsations
Kelly
Michael, you’re quoting Adolf Loos in here? I didn’t see the sign saying we were allowed to quote irrelevant, historical relics. Maybe I should rev up an aesthetics bolstered by Taylorism? Or not.
Nathan, trolls be damned! If you are pissing people off that means you are (still) expanding your writing project out of your niche. You will *inevitably* annoy purists while (hopefully) blowing the minds of folks that aren’t so acquainted with these means of graphic communication.
I’m not aligned with any particular camp, but the type of stuff that I write about probably puts me a little more on the arty/aesthetics side of the fence. I’m actually tired of Tufte drum beating and purist preaching and welcome fun and informal representations of information. That said, there is too much mindless consumption of data visualization on the net—what makes FlowingData so useful is that visualization project profiles and features are contrasted with technical posts and industry/tool talk. We need more people writing about visualization & data practice and coming at it from diverse, informed angles. Keep at it Nathan. :)
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Graphical display is key in my opinion for any statistical analysis of data. If you can’t visualize and display graphically a statistical conclusion then you are really limiting the understanding you have. And if you want to share a result with others who are not as deeply imbedded in the data and analysis as you, a graphical display is critical.
The key question for me is whether a graphical display conveys the right intuitive message. When someone looks at it, do they come away with the right intuitive conclusion. If a graphical display misleads or overstates or misrepresents the real relationships in the data then you have a problem. There are certainly cases where a graphic misleads, and often the popular press is the culprit.
I think Flowing Data is excellent, it explores different ways to present data so those graphics can be discussed, improved upon, etc. There is nothing wrong with interesting and cool graphical representations… as long as they don’t mislead the viewers. I am okay with a bit of “chart junk” if it makes it more pleasant to look at and interests new readers as long as it then leads them to an appropriate conclusion.
Love the site. Don’t mind the haters… there’s always haters
My experience in working with research scientists is that they generally have absolutely no artistic, design, or aesthetic sense what so ever. A clear example of this is the (huge) number of talks that I had to endure from outstanding scientists that were presented in Comic MS.
Given that prototype data visualizations and frequently driven by not only the need to communicate but also to communicate beautifully, one half of the reason for many visualizations falls on unseeing eyes.
The reality is that most people, although they may be fundamentally quantitative and capable of understanding numbers, algorithms and patterns, do not have commensurately developed ability in visual interpretation. I hate to use the cliched left/right brain metaphor, but I think in this case it applies.
One thing is certain, for readers of visualizations that are non-specialists, an attractive figure is much more appealing than a cold-hearted scatter plot. They are more likely to be drawn in, engaged and stimulated by a well designed visualization. The specialists sometimes forget that they are a minority and that it is as important to communicate their findings to the general public, who may be stimulated by an entirely different visual.
It’s the internet.
I find myself, as a designer, fascinated by web apps like wefeelfine and twistori that have the ability to aggregate collective biographical data and communicate “barometric” readings of humans themselves… Maybe some things aren’t classic statistics but that doesn’t make them less beautiful.
There’s something for everyone in the world of data viz. Keep it up and don’t let internet folk get you down :)
Hi Nathan. I’m sure you know where I stand already since I produce my own share of ‘garbage’ but I wanted to add my support here as well.
The creative playful exploration of ideas related to data visualization is my passion. I think your site, together with infosthetics, are the most stimulating places on the web. Keep up the excellent work !
Thank you everyone for the support and input. This ended up as probably the best discussion ever on FD.
I just came across FlowingData about two weeks ago, and now it’s my second favorite RSS feed to check every morning (sorry, xkcd still comes first… but it’s a comic!). I am pretty stingy with the time I spend surfing the net, and only forward webby things when I feel they are truly exceptional. There is a very great deal of “wow that changes the way I see things” awesomeness to be found on your site. (Even as I write that I am thinking of how I might visualize the relationship between the web-things I share with my friends and family, awesomeness and FD…. see?!)
Just wanted to add my two cents in the chorus of “keep up the good work; I love it!”
I have a degree in biology, and worked in the sciences for years. After a while, I decided to make a change and enrolled in law school. Despite the fact that the school I attend (Northwestern) is known for hiring empiricists on the faculty and enrolling primarily students with work experience, only an extremely limited number of my classmates have a good grasp on statistics.
I’ve showed several of the visualizations to classmates and they’ve always been met with a positive reaction. A good visualization is an excellent tool for expressing data to people outside of the field. Well illustrated data can tell the story teased out from the numbers in a far, far more accessible manner than a table or scatter plot could ever hope to.
The need for well visualized data is great; there is a cottage industry of designers working to illustrate complex science and medicine in the courtroom. The average jury (and judge) has even less experience with science than the attorneys. Major decisions are made on a daily basis for which well visualized data is invaluable. Despite the (apparent) tendency of statisticians to poo-poo such work, people outside the field applaud it.
Keep up the good work!
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Nathan,
In my opinion, FD is one of the best resources on the internet for anyone with an interest in data. One of the great challenges I see in the near future is the concept of data literacy. Now that so much of the world has more data to available to it than ever before, and that public store of data continues to grow at an ever increasing rate, the ability to reason and communicate about data is getting more and more important.
I love the way FD is unafraid to champion interesting ways to do exactly that, and by doing so, creates a truly interesting public conversation. I think the criticisms are inevitable, and I wish all were as reasoned as Andrew’s, but hey, this is the internet.
Thank you!
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