Neil,

I have found what you describe to be more or less a universal truth. All too often people falsely believe because they can navigate a tool’s UI that they are proficient and effective in what ever task the tool was designed to support (in this case, data visualization).

You can go so far as to obtain a certification for a given tool (i.e. software) and still be lacking in the skills needed to apply those tools effectively.

To further Stephen’s point, it seems with increasing frequency that absent truly fresh ideas, many voices within the community have taken to revisiting practices that have already shown themselves lacking. Perhaps it’s a case of too many people wanting to be heard while have too little (if anything) to say.

Feel free to disagree and challenge. My frustration is that I still see to many representations of data without providing any real information – “surface skimming”. There’s no story. There’s nothing in the title that makes a statement to the viewer of what the information is. We place the effort on the viewer to determine what the visualization communicates. I hear managers say that they cannot see progress from our efforts through these visualizations and it’s primarily because they looking at representations of raw data. In some cases not only are they misinterpreting the visualization, but they are coming to wrong conclusions. In some cases there’s just too much of this “stuff”– again only representations of data. It as if we’re not purposeful when we create this flora of visualizations and the results are not meaningful. When you read Tuft, Cleveland and Few you get a sense that we’re trying to make connections between our actions and outcomes, between triggers and resulting events. Without that feedback – we will struggle to improve. Forget about the tool and first focus on what you want your audience to understand.

Alberto,

while I refrain from chiming in on the main topic, lacking sufficient depth and research in this, I often have this feeling, a recurring wave of personal conviction, that we’re still at an early stage of data visualization. Some random reasons:

1. Computer games: While in dataviz, we’ve been having fun making axes, and if we’re technically sophisticated, we even make them adapt to the data or even respond to it in real time (tweening with new data or filtering), in games, all these are a given, at high performance, and deep scenegraph hierarchies describing the game world. The same goes with styling. For example, consider Nadieh Bremer’s and others’ awesome posts on motion blur, point merging, or depth blurring on scatterplots. By contrast, the techniques for these have been around for ages in SVG – even accessible from D3 – and many outside the dataviz community have been using these with SVG, not to mention indie games and AAA games. Also, there’s so much to learn from games, in terms of control, exploration, immediate feedback, immersion, emotional connection, in comparison, incredibly advanced techniques, engagement and learning. Since gaming, education, news and data visualization overlap even now, and on fundamental grounds the overlap could justifiably be vastly greater, I can’t help but be convinced that there’ll be immense advances just due to learning from games. Techniques that were trivial to 80’s demoscene coders will continue to crop up and surprise over the next decade, yet the real promise is not technical catch-up as much as establishing the same strong connection between dataviz maker and user as currently seen in gaming – most importantly, for giving people a vastly deeper, connected and integrated immersion into the world of data than what the current, information poor, sterile barcharts can do.

2. Data and projection spaces: Besides gaming, there are adjacent areas such as mapping and scientific data visualization that deal with vast amounts of data, continuous streaming in/out of information as needed, continuous, adaptive resampling, and a way of helping make sense of vast amounts of data, by using our cognitive faculties (currently, mostly visual and spatiotemporal processing) as fully as possible. I suspect that current data visualization merely scratches the surface and current user engagement with dataviz is superficial, fleeting, often immemorable and isolated in comparison to what could, and I think will be achieved.

3. Interaction. There’s current debate about scrollytelling and user engagement. We read sound advice for using interactivity in moderation and after deliberation, still, it is true that most discussion and publications on dataviz either assume a paper presentation, or presentation on other media (screens) in a way that closely resemble paper. However with current computer screens we evolve toward “live paper”, and even though there are interactive experiences designed for computers, it’s rarely the focus of current datavis research and guidance. A great writing on this is Magic Ink from Bret Victor. In other words, I think that the current preference for scrollytelling merely highlights our present lack of experience and means for providing engaging, interactive content. Clearly, interaction is harmful if the story is single-dimensional enough to not warrant it, or if interactivity can be replaced by inference as described in Magic Ink. There’s some implicit linearity in most current storytelling, though I believe it’s linear mostly because we haven’t yet learnt to weave a rich, non-linear, navigable, open world of knowledge, which brings us to the next point.

4. Sea of knowledge. I used to be annoyed by economic news in the radio, where the reporter announced my country’s latest macroeconomic indicators, which were interesting, however they had not put it in context with other countries in the region, or longitudinally. So there that was, this sliver of knowledge, floating free of context in the air. Fundamentally, we still have this experience, no matter the medium. A thoughtful report on economic matters may of course include regional comparisons, offering e.g. a bar chart or small multiples chart per country in the region or continent. However it’s solely at the discretion of the author. If he thought of that, good. If not, then bummer. However I think that a lot of readers have a *distribution* of interests, and shooting for the average may be satisfying to few. As an example, economic databases include mountains of multidimensional data. However I can’t currently take a great NYT or FT article and just somehow ‘loop in’ data that the maker didn’t already include deliberately. It’s like a cage – the information is out there somewhere, and here we have a great presentation format, and we’re deeply interested in it *right now* but we can’t get the answers. The reader better becomes versed in the arcane knowledge of where the public databases reside, they can laboriously duplicate the data cleansing and consolidation exercise, probably leading to different results, and yes it’ll be necessary to learn R, python or at least a large data visualization tool. Currently we’re really far from freely navigating from a FT article on British demography to swapping data in the same article with analogous French or EU data, while the article and visualizations morph accordingly, transitioning toward some other article gradually which happened to be created by Washington Post, by OurWorldInData or by a research group at the World Bank. Why should we assume that dataviz stays roughly as it is, when it’s utterly deficient in ways that have conceivable remedy? Why expect that dataviz cannot somehow open up to immersion and free navigation?

5. Responsive dataviz. Current data visualization is one-size-fit-all. In the future, I expect that a casual, somewhat disinterested reader gets abbreviated, simplified sports analytics; if the medium detects increasing interest, then the dataviz morphs into something more detailed, possibly responding to the particular interest of the reader, without the current, rigid, mechanistic and preconceived controls of e.g. an “enlarge” button, a link or a crossfilter. A sparkline thumbnail on a home page may gradually morph into a detailed timeline chart, rich with annotations and contextual information, fading away into a sparkline again if the user broadens scope to comparables, making the sparkline now be a part of a small multiple grid.

6. Technology. I’m focusing on content and engagement, downplaying the technology aspect, but currently there’s no automated, transparent mechanism for very simple things like placing ticks on an axis such that the ticks are not too few or not too many, there’s no crowding or overlap, etc. (there are libraries and packages that have heuristics but it still needs to evolve). Publishing, design, typography are huge areas of pertinent knowledge, yet current tools only capture a sliver of that knowledge. Even doing something trivial has obstacles: SVG is a hierarchical representation for good reasons, yet the leading tools (Ai etc.) don’t support it, there are groups but no group transforms. There’s all kinds of siloing going on. I wrote elsewhere that we D3 users tend to forget D3 sits atop of SVG; we’re happy making hundreds of separate circles or axis ticks for a scatterplot where it could be done with a single DOM element and a marker. We’re struggling with making multicolored curves. There’s all kinds of technological, cultural and societal boundaries across tools and areas, and cross-pollination happens slowly. It is therefore expected that lots of advances will happen simply due to absorption of old ideas and knowledge – not to mention the still ongoing evolution of technology, with new classes of devices (smartphone, VR…) appearing every decade or so. For an inspiration of what it might be a few decades from now, Fast Times at Fairmont High by Vernor Vinge is a good read. But we don’t have to jump ahead decades, we can go back for profound inspiration. Much of what was conceptualized in the sixties is still waiting to be implemented. A great talk on this is, again, Bret Victor’s The Future of Programming, which has a great section that contrasts what Engelbart had in mind for collaboration and what trivial tools like Googe Docs or Skype do; Alan Kay’s talks are interesting too. I often find research papers from the 80s or earlier that are fascinating and talk to some fundamental problem or angle that feels sorely unaddressed by current means.

7. Understanding of ourselves. We’re engaging in data visualization for some purpose, and we’re humans. We know relatively little about how our mind and our society works, but this knowledge is increasing and more research may, and should target the processing, and even the creative process of data visualization. I would expect that data visualization will, or at least should, continuously be shaped by our understanding of ourselves and how we perceive, process and retain information and conclusions; what are the biases that can be misused; how can, in essence, numeracy and other abstract concepts be evolved into something like a new sensory organ.

8. Society. We currently think about dataviz in terms of news, public policy and human development analysis, sports and financial analysis done by newspapers, web sites, financial and business consultants and scientific publications. However data visualization is better perceived as something fitting in the grand scheme of sharing thoughts, conclusions, support and questions; influencing, convincing; teaching and helping grow; aiding research; entertaining; increasing the efficiency and relevance of the human creative process, be it about products, behaviors or policies. I believe that part of the public’s indifference toward important events such as global warming or ongoing wars is partly explained by the disparity between our technological means of impacting the planet and its inhabitants en masse, vs. the means of sharing thought, information, empathy, knowledge and values that would somehow make us behave like we care about the next person, our grandchildren or the other species. It is this aching gap that can and should eventually be filled by some future thing that has roots in a lot of current disciplines, including data visualization, games, simulations, massive online collaborations and opportunity of access by all of humanity. Also, there’s somewhat limited scope for visualization in an agricultural, preindustrial society, but we need to show galaxies and the universe, the Higgs boson and what went into finding it, planetary exploration, or how things like machine learning work.

These have been mere examples of the myriad of angles and directions that are bound to have an impact on data visualization (if not leading to its absorption or other reimagination), let alone combined. These are subjective thoughts and each of us hold different beliefs about the maturity and the various forces acting on a field. Therefore, again, not participating in the main discussion, I consider what we call data visualization something embryonic, fertile, unexplored and pluripotent, therefore anticipate that incremental advances will be accompanied that will, at least in retrospect, be considered fundamental changes.

@Robert Monfera

Robert, in response to points 2 & 3 I’ve had the experience of making a report in Tableau and seeing it utilized on an Epson short throw projector. This projector enables you to touch a point on the board like a mouse click. I have a simple interactive dual-trellis view with actions and filters (that took no more than a day to build really) and two managers are able to collaboratively interact with the data and find real value in the dataviz.

Kris,

it must be an interesting application. Multiple people molding aesthetic parameters or model parameters together, possibly via direct manipulation. aprt.us is an interesting example for building up something from geometric primitives via manipulation of the glyphs themselves. A modest, earlier example for morphing a dataviz (no direct manipulation ATM) is here, featuring Stephen Few’s bandlines (morphable to Tufte’s sparklines etc): http://bl.ocks.org/monfera/35be5626a47110312c35 and there’ll be more to come e.g. https://twitter.com/monfera/status/707276048413683712 and the caveat is, the concept of morphing dashboards is a very small and probably controversial aspect and my rough examples just allude to the concept.

Robert,

Electronic booklets or articles could serve as a useful way to publish short works on data visualization, except that, as you suggest, e-book platforms are currently lacking. I reviewed all of the popular e-book platforms, hoping to find one that would allow me to create electronic versions of my books that would provide the quality reading experience on which I insist, but I eventually concluded that none of the existing platforms are up to the task.

Even printed books fail, however, if the publisher insists on cutting costs. For example, Alberto Cairo’s important new book “The Truthful Art” contains a large number of figures that cannot be read without a magnifying glass. To display infographics properly, a book of much larger dimensions is required, which is why my books and Tufte’s are large. We self published so that we could control all aspects of our books’ designs, including the quality of printing and the size of the pages. A book about visual design that cannot be read because the figures are too small is in conflict with the very principles that it advocates. This problem can be seen in most of the books about data visualization that have been printed in the last decade or so. At least they’re printed in color, however. When the first edition of Naomi Robbins’ book was published, Wiley Press refused to print it in color and often printed it using high-speed laser printers, which severely undermined the quality of an otherwise excellent book.

Steve,

e-books lacking sorely means yet another area that’s ripe for disruptive changes. Collation, indexing, lateral navigation in a dense graph of content rather than in a serialized tree structure with a static index, adapt things in size, external links, interaction among readers (highlight, discuss), reflow for presentation, restructure by theme, demonstrate interactive features, multimedia, errata, new editions, feedback, maybe glean anonymized stats on popular or hard to understand parts of the text etc etc – it’s a question of time and willingness by someone to do it.

I am surprised Stephen did not mention Tamara Munzner’s book “Visualization Analysis and Design” https://www.cs.ubc.ca/~tmm/vadbook/
The book is full of guidelines and thoughtful analysis, e.g., “Get It Right in Black and White” or “Function First, Form Next” and offers a new perspective compared with “Show Me The Numbers”

Bilal,

I did not mention Tamara Munzer’s book because it belongs in a different category. It is a textbook that was written specifically for computer programmers who are studying information visualization at the college level. It is not a book for practitioners of data visualization.

Robert and Steve,

I’d like to see more books or articles on data-collaboration. Both real physical spaces and software that allows for simple non-linear ad-hoc interaction-flow between dashboards or data with many people.

Previously the model that I’ve seen is one in which an analyst (or journalist) would sit with the data for hours before presenting it. Looking for data, mucking with it, cleaning it, looking at different views and then come to a conclusion and dress it up and make it as simple as possible and present the findings in a linear fashion. Then I expect people to ‘get it’ like I do by only spending a few minutes looking at the information.

I’ve built an interactive YOY report I built (orange is this year’s trend, gray last year’s, redactions in gray boxes) and a simple image is located here: https://goo.gl/photos/B1fwcqeEgioeh5hJA And our team put this up on an interactive short-throw projector and we all stood around and interacted with it and collaboratively answered questions. Previously I could barely get one person in my tiny cube to look at my little screen. Publishing a report means we all sat in our small cubes and looked at it separately. Projecting it from one computer means one person was the driver and others may be reluctant to take or give control. By interacting with the data together we are all moving along together and gaining similar insights.

Alberto,

I agree with you that reading multiple books on a topic can be useful, but only if those books provide content that is different from other books or presents the same content in a way that promotes better understanding. This is true of few books that have been written about the practice of data visualization during the last decade. If you feel differently, feel free to provide examples of content that doesn’t appear in “Show Me the Numbers” or that does appear but is presented in a way that extends our understanding of that content. I’ve observed that when these books depart from the content that exists in “Show Me the Numbers,” they often introduce errors and provide bad advice.

Kris,

The topic of collaborative data sensemaking is indeed worthwhile and it hasn’t been addressed sufficiently. I’ll try to address it in a basic manner soon. I think of collaborative data sensemaking as falling into three categories that affect design: 1) asynchronous collaboration (people are not working with the data simultaneously and are not in the same location), 2) synchronous collaboration across multiple locations, and 3) synchronous collaboration in the same location. The nature of the displays and the interactions must differ to some degree to suit these situations. Stay tuned for more to come, either in this blog or in a newsletter article. Thanks for the suggestion.

Mark B,

It is definitely the case that inferential statistics require a great deal of statistical knowledge. Most visual analysis tools have incorporated some degree of support for inferential statistics, but none that I’m aware of are making it clear to their customers that inferential statistics requires a high level of skill. I don’t believe, however, that so-called “self-service” visualization tools have come anywhere close to supporting descriptive statics well. Even the best of these tools have far to go before they support descriptive statistics well. Even basic descriptive statistics requires a level of skill that few users of these tools possess. These basic skills are well within reach with proper training, but the self-service message of the vendors suggests that this training isn’t necessary.

Very much agree with Mark B.

Are they any definitions universal accepted for basic level of knowledge in any field? What is the basic level of statistics required for a practitioner in data visualizatin field? What is the minimal level of knowledge in data visualization field a good statistician should have? Are they required any minimal knowledge in data storage and interogation for a data analyst? What is the basic level of knowledge a decent data sensemaking specialist should have in: data storage and extraction, statistics, data visualization besides strong field related knowledge (economics, logistics, transport, warehousing, production, quality, …)?

As long as these criteria are minimized by vendors advertising to the trivial level of drag and drop within a tool, the business inteligence projects (otherwise very profitable) will continue to fail.

jbriggs,

When I said that these books contain nothing new that is accurate and useful and nothing old that’s expressed in a uniquely useful way, I was not saying that they’re all saying the same thing. They aren’t. Each of these books includes errors and varying degrees of bad advice. If they all said the same thing and did so equally well, then readers could choose any of these books and get the same value. If this scenario were true, however, it would still produce waste in the form of redundant publication efforts and also in the form of unwanted repetition foisted on those who purchased and read multiple books, resulting is lost money and time. Imagine how much more productive it would be if authors only wrote books if they have something new to offer.

Andy,

You do not understand me correctly. You rarely do. I have the impression that you are not interested in understanding.

We’ve learned quite a bit since Brinton, especially about the perceptual and cognitive aspects of data visualization. What hasn’t changed much since Brinton’s time is the insistance of unskilled practitioners to repeat the mistakes of the past.

Ben,

I would be surprised if you didn’t celebrate all of the new voices in data visualization. You have two strong incentives to do so: 1) you count yourself among them, and 2) you work for Tableau, a software vendor that derives benefit from any voice in the data visualization space that doesn’t make negative comments about Tableau.

You seem to be saying that you support all new voices in the data visualization space regardless of what they say. Does the quality of their work not matter? Does it not matter if they provide poor guidance? Should we not vet the work of so-called data visualization experts? Data literacy is not advanced by bad advice any more than English literacy is advanced by a teacher who speaks English poorly.

The “bar of entry” into the ranks of data visualization expertise ought to be high. By its very definition, expertise means a “high level of skill.” Data visualization will not benefit by allowing anyone to proclaim themselves an expert regardless of skill. Progress in a field is never achieved by setting the bar low.

Rather than making sweeping claims about the benefits of all new voices, it would be more helpful if you would identify specific examples of useful contributions that appear in books published in the last decade. We who work with data in support of decision making should be in the habit of supporting our positions with evidence.

Ben,

I did not claim to know your motives. I pointed out that you count yourself among the new data visualization voices and that you work for a software vendor in the space. These are both incentives to support the new voices of data visualization. Whether or not these incentives motivate you and to what degree, I cannot say, but I must consider the possibility. One of the essential practices of critical thinking is to identify the potential incentives and biases of people when evaluating their statements. If you don’t want your motives to be questioned, you shouldn’t be working for a data visualization software vendor. Would you question the motives of a commentator about gun control who works for the NRA? I would hope so.

Have you been publicly critical of Tableau? If so, I’m surprised and would appreciate it if you would direct me to this criticism. It will help me understand your perspective.

To correct a point that you made, we were not discussing your “motivations for encouraging people to enter the data visualization dialogue and share their thought and expertise.” In this blog article to which you responded I expressed my concern that few of the data visualization generalists who have published books in the last decade have added anything of value. I have never discouraged people from entering into dialog about data visualization. In fact, my work is dedicated to encouraging this. How you and I seem to differ is that I want the dialog to be productive, which requires guidance. I want people to learn from it. Not every dialog results in learning. In fact, many of the blogs, articles, books, and dialogs about data visualization of recent years have led to a regression rather than progression in the field. This should concern you, but the fact that you “celebrate all of the new voices in this budding and nascent field” indicates that error and poor advice does not concern you. You cannot encourage progress by celebrating mediocrity.

The purpose of this blog post was to encourage new voices to have something useful to say. I’m not sure what you mean when you say that we “need voices to reflect the spirit of our time.” What is this spirit to which you refer? Much of the spirit of our time does not deserve to be encouraged.

Like you, I learn a great deal from novice practitioners. My workshops are filled primarily with novices. I cherish the opportunity to help them get started on the right foot. Their questions and insights add great value to my workshops. I encourage them to learn the basics of data visualizaton well enough to think for themselves. As a teacher with expertise in data visualization, I provide feedback and direct my students and readers toward best practices. I don’t coddle them and give them awards for participation alone. I encourage them to pursue excellence in their work. I’m a teacher, not a marketer.

Ben,

I definitely wasn’t trying to bait you. You began this discussion by posting comments here in my blog. I have merely responded to your comments. You’re welcome to abstain from commenting in the future if you’d prefer that I refrain from responding.

Your agreement with Shawn Wallwork that double clicks on maps and chart in Tableau “can be ‘pretty disorienting'” is a rather timid criticims of the product that you’re paid to promote. Marketers and salespeople are trained to acknowledge a minor error in their products to give the appearance of objectivity. The rather tepid comments that you wrote about Atlas from the Quartz Team of GE (one of your large customers), a product that doesn’t compete with Tableau, does not demonstrate objectivity.

Regarding Cole Nussbaumer Knafic, it is absolutely true that Cole included a section about storytelling with data that applies lessons from theater. The content was largely derived from Nancy Duarte’s thoughtful work. The fact that Cole included this information in a book about data visualization is indeed potentially useful as an introduction to the importance of developing presentation skills if your job calls for them.

You have no choice but to defend yourself against reasonable suspicions that, as a member of Tableau’s marketing department, you are biased. While it is true that you could possibly, with heroic effort, play this role and yet remain completely objective about data visualization principles, practices, and tools, it is unlikely. You cannot be employed by a data visualization vendor and expect the world to leave your objectivity unquestioned. Rather than acting offended when your objectivity is questioned, why not simply embrace your role as a Tableau marketing professional and be upfront about your bias. If you were not biased in favor of Tableau, you would have been fired long ago.

Your final comment left me puzzled. I suspect others who read it will find it puzzling as well. You identify the “spirit of our time” as one that does not appreciate critical discourse. If this is true, I will resist the spirit of our time. I believe that my work is respected by readers and students because I approach data visualization critically, not in spite of this fact.

Steve,

I don’t feel like I am learning much from this debate.

More interesting to me would be to discuss what books we’d actually like to see happening.

I’ll start first with my partial list of ideas off the top of my head …

1) Text Visualization. Text is very poorly covered by existing books and it’s of hugely practical importance. There are lots of companies and organizations that are interested in looking into text data sets and the existing vis practices are incredibly poor (e.g., Wordle makes me itchy) and research is not exceptionally good either (with exceptions). I’d love to see a book focusing only on this aspect. I think there is a lot of space to instruct practitioners and inspire researchers in this area.

2) Designing Data Visualization User Interfaces (or Interactive Data Interfaces). Virtually all vis books (your “Now You See It” is an exception) focus very very heavily on static representation. But, what gets me excited by vis is it’s powerful analytical capabilities, that is, how people can make sense of complex realities by interacting with data. Tamara Munzner’s book covers a bit of that but I think it’s not enough. Helping people design effective interactive data interfaces is really hard and we need more guidance. One particularly deleterious trend, in this area, is the heavy focus on technology. Interaction is not about technology, it’s about how to translate questions, into actions, into reactions, into questions, etc.

3) Visual Machine Learning. Machine learning is behind tons of automated decisions and systems we use today and this trend is unstoppable. There will be way more of that in the future. There are two extremely interesting aspects of how vis should contribute here. First, ML models are terribly opaque and sensitive to choices and modelers need a) a methodology and b) effective tools to better understand what models do and what their implications are. As things look now, people are shooting in the sky. We also need better ways to incorporate domain knowledge into these systems. Second, there are many methods in ML that can make visualization way more powerful, but they are not well integrated. I’m always amazed how poorly integrated unsupervised/exploratory data mining methods (as well as statistical methods by the way) are with interactive visualization. They would be such a powerful combo together! I am thinking of methods such as data clustering, rules, etc. P.s. It’s unfortunate people are so excited about predictive modeling when exploratory methods look so promising.

4) Data Visualization Case Studies. Humans learn a lot from examples and have an amazing ability to extrapolate from specific cases. I am always frustrated to see how little there is to learn about how a team or a single expert went from ideation to deployment and what happened in between. We always see the end product, but very rarely the process and the struggle of creating excellent final results. Tamara Munzner has done some really good work with a couple of recent papers but I’d love to see a whole and more accessible book with carefully crafted stories of how some really good experts when from A to Z. That would also be very entertaining I guess.

Here is my quick list. I guess there is way way more to add (luckily for us!).

My hope is that we can move on with this conversation and focus on the positive side of it: we need to push this field forward.

Whether one agrees with you or not, there is no doubt we need more books and way more work. So, let’s do the work!

Take care.

Stephen,

I see the effect of “data visualization lite” scenarios being played out on a daily basis.

I see “good practices” (and I often hold up your examples from “Special Interview with Stephen Few and Data Visualization expert” by Alexander ‘Sandy’ Chiang, Research Director, Dashboard Insight, Common Pitfalls, Effectively Communicating numbers… ) frequently challenged by inappropriate design and chart type selection; here, I believe your frustration is that of a teacher, the children are not listening or comprehending your lessons/guidance; in fact, they may actually be rebelling in opposition of “rule-setting” or standards, although in my personal experience, it’s usually innocent ignorance.

Optimistically, I believe data visualization has grander scope and is maturing. Outputs in media such as data driven animation (complete with stories) such as 422 South’s “Data Visualization Reel” shine the light for me. However, As data visualization becomes ever more interactive, more tactile, more immersive; I’m unsure the written word (other than technical guidance) is the most effective educational medium anymore. I watch the most wonderful and engaging TED talks with data visualization practitioners who share their knowledge and wisdom.

Stephen,

I apologize, I should have put the 422 South piece in context for you, and your readers. The show-reel on its own, in my opinion, acts only as a technology showcase, giving me, a practitioner, an understanding of how to visualize and process large amounts of spatiotemporal data.

However, in context, and to re-frame, these outputs were part of my learning and understanding of flow, movement, and interplay as they were embedded into such documentaries as PBS “America Revealed”, where the accompanying audio commentary complements the visuals.

Yes, these are “just” a scatterplot with trails, but (I certainly find) visualizing x,y,z,t non-trivial and has been historically computationally expensive.

Aaron Koblin, whose TED talk I enjoyed, inspired me with his “flight patterns” visualization project, I see the 422 South output as an extension of this learning.

Jer Thorp, another TED “talker” is another data visualization practitioner whose work I admire, I find found his content useful.

I admire your ethic and stance with the TED platform, and understand your right to protect your intellectual property and “brand”.

I just discovered this thread. Since I was mentioned in the exchange between Stephen and Ben, I suppose I should comment. Ultimately I’m confused by the exchange. Ben works for a toolmaking company. Stephen your blog is about the evolution (or lack thereof) of an entire industry that produces data visualizations, and the quality of the ‘experts’ guiding this industry. How exactly is a toolmaker’s opinion in a discussion of the evolution of an industry disqualified because he is a toolmaker. Should we disqualify your opinion on this entire subject since you are a book writer, whose book sales will benefit if there have been no better ones written since you wrote yours?

I think there’s a pretty good case to be made that this entire thread should be ignored because you wrote “Show Me the Numbers”. Much more than disqualifying Ben just because he works for Tableau, a company I don’t work for. But I do love/hate their tool!

–Shawn

Many visual analysis tools have incorporated some degree of support for inferential stats, but none that I am just aware about are rendering it clear for their customers that inferential reports requires a high level of skill. I no longer believe, yet , that so called “self-service” visualization tools have come anywhere near helping descriptive statics well. Also the best of these tools have far to go before they support descriptive statistics well. Sometimes basic descriptive statistics requires a level of skill that few users of these tools possess.