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?

14 thoughts on “Telling a story doesn’t tell the whole story

  1. Jorge Camoes

    I strongly agree with you Enrico. I suspect that this whole “use visualizations to tell a story” thing is clearly a conspiracy by graphic designers :) Data visualization is a language with multiple accents. The graphic designer’s accent is just one of them. More visible, but not the “right” one (there is no “right” accent).

    I accept “story” it if we understand “story” in a broader sense, as “variation”. Variation that must be seen, shown and, if possible, explained. In that sense, story includes exploratory analysis, monitoring, etc.

    Like all other languages, visualization is not objective. Yes, you can use a chart to persuade, because a chart is an interpretation of reality, a visual argument that supports your own world view. There are no objective charts. A story is always an interpretation.

  2. Gregor Aisch

    Good point, Enrico. Honestly, I begin to hate all that talking about stories here and there. My non-datavis colleges already make jokes like “once upon a time, there was a little data..”.

    Instead of “telling stories with data” it definitely should be “supporting stories with data”. There is and always will be some kind of (textual) narrative needed to actually *tell* the story, like the speakers voice in many of the NYT pieces.

    Big +1 for pointing out the role of visual data exploration, but that’s a different story. I prefer to talk about finding *insights* in data, instead of promising fascinating stories inside every dataset.

    I’m glad you’re back into writing. Looking forward for more stuff..

  3. Ben

    The best visualisations tell a story you an interact with. Much like a fantastic game of dungeons and dragons. The data represents the possibility space and the visualisation can be the guide to explore the data possibility space for you to find “stories”, where stories can be insights, exceptions, etc. but ALL should be conveying something you can act upon, be that an action you need to take as part of your job or a behavioral change that the visualisation has compelled you to make e.g. Reducing energy use due to the highlighting of the story in your regular daily shower usage.

  4. cscheid

    Hear, hear!

    Have you read Taleb’s Black Swan, or Kahnemann’s Thinking, Fast and Slow? Both of them spend many pages talking about the “narrative fallacy”: the misguided human urge to assign a consistent story to a sequence of events which might not have causal relationships. This is a very strong urge, and leads to all kinds of nonsense (“Stock markets fall after Saddam Hussein captured, because on future uncertainty in Iraq” was the headline. Half an hour later, same tv channel: “Stock markets rise after Saddam Hussein capture because of future oil prospects in region”. This happens all the time.)

    I fear that encouraging story-telling in vis without being super extra careful leads us to be sloppy about whether there is, in fact, anything real in the data being analyzed.

  5. OOM Creative

    agree, great insights, however it’s a good way to sell data visualization – as it humanizes a process – that from the outsiders point of view appears very dry, technical and abstract. So provides an entry point for new audiences.

  6. Jan Willem Tulp

    Great post, and I totally agree. Especially the point that the data makes the story, not the visualization is something I totally agree with. If you don’t have an interesting dataset or the data doesn’t contain the story, you can do what you want, but there will never be strong story. Your McCandless example is a good one!

  7. FILWD

    Folks, I am sincerely surprised by such a positive response! Honestly, I was ready to receive rotten tomatoes :-)

    It looks like I hit a nerve here in many respects and I think it’s really important we know we kind of feel the same about this issue.

    @Gregor: I prefer to find insights in data as well but I think it’s important we start discerning between who is finding the insights and who receives the summary of these insights (and in what format). I am concerned, now that I think about it, that we visualization people are the ones who search for the insights and this, at many level, is a the ley problem here. We are not expert in everything, especially in all the domains our data describe. As long as we do that by ourselves, for the fun factor and the thrill it gives (I like it at least as much as you guys do), the scope of visualization will be limited.

    @Jan: I am very glad to hear other people experienced the same about data with no stories to tell. I suffered a lot of frustration in the past by seeing this happening to me over and over. In a way it’s comforting to hear I am not the only one!

    @Greg (OOM Creative): I see your point and I fully agree. Especially, I understand the need for companies in the field to find the right niche and the right way to sell it. All these companies and freelancers did a wonderful job in making visualization popular and wanted. Also, having a gentle entry point is an important issue to investigate more. Research included.

    @Carlos: Very very very good point! I’ve heard of these books but never managed to read them. Did you by any chance also read “Fooled by Randomness”? I think it’s along the same lines. I agree with you when you say we have to be super extra careful, if the data is sloppy the visualization can only be sloppier, not to talk about its interpretation!

    @Ben: Your comment reminded me of Segel and Heer’s paper on “Narrative Visualization” ( There is a section about “Balancing Author-Driven and Reader-Driven Stories” and then they offer a series of patterns to allow some (controlled) level of exploration in the data. I think this is very interesting and desirable.

    @Jorge: I like your your idea of story as a “variation”, I think I know what you mean by that. Yes, data visualizations don’t necessarily tell the truth but I suspect they are often used to this purpose. Do you think people, at large, get that? I think Tufte was concerned about it many years ago already. And many other statisticians before him too.

  8. Jakob Jochmann

    Yeah, one of the problems lurking behind the whole marketeer talk about stories shebang is the lack of agreeable definitions about what a “story” is. You know, some hypothesis for scientists to test against.

    I actually believe, as cscheid pointed out in his examples of humans making sense of world events, that “story” is an extremely salient schema, a complex knowledge structure of nodes and links. This schema just so happens to be culturally propped up in a way that makes it one of the go-to-devices when it comes to putting information into a cause-effect frame.

    Incidentally I was just yesterday discussing how disappointing it is that some of the best communication frameworks based on ideas that Gumbertz proposed 40 years ago have never been developed into full fledged theories. The only thing really elaborating how to put schemas into communicative use is work about contextualization by Peter Auer, which was originally in German unfortunately and never made it into the anglophone scientific mainstream.

    Still, “Kontextualisierung” in “Studienbuch Linguistik” is the one resource I’d recommend going for to get acquainted with a framework how information is contextualized. If you don’t know German, there are a few articles he published in English as well over the years.

  9. Jérôme Cukier

    I’ll have to disagree out of friendship because you were looking for an argument and nobody gave it to you.

    on no story telling without data exploration, there is a caveat emptor.
    for explanatory visualizations, the exploration can (and probably should) be done entirely by the designer, leaving the user/viewer with no more than an illusion of interaction. The designer already reached a conclusion (or possibly based their work on an opinion) and only gives the viewer the possibility to check its validity.

    on data making story rather than visualization, you will agree that the same data under two different forms can be interpreted very differently. on DmC mistakes, they are of the same nature as picasso’s supposed inability to achieve photorealism, ie: fully intentional and with a purpose. Though I can’t let you say that the story simply lies within the data. Many possible interpretations are there, some interesting, some less so, some prominent, some effectively hidden, some naive, some biased… and the job of the designer is to prioritise the ones that they want to convey while downplaying, or muting altogether, the rest. This prioritisation, which adds a layer of subjectivity, is what makes the story, not the data proper.

    I mostly agree with the 3 other points, but if you need a contradictor I can happily pretend to disagree. Just don’t get me started on contest entries.

    just in passing: obviously explanatory visualizations are given much prominence on the web which is a space for communication. it’s the nature of such viz to convey meaning to a larger audience so they fit on the web more naturally than analytical tools which are arguably more important, because as you point out no data story could be told without proper analysis first.

  10. mirkolorenz

    I am probably guilty of promoting that connection between data and story quite a bit. As you say, the connection itself is not the problem. The general public often is unaware of really big, really important trends for a time, while the pioneers toil on solving the hard nuts. Then, once someone finds a way to “package” complexity into a nice, short line, interest picks up. This wave of enthusiasm is often misguided, inflated and not really creating advances. Instead, this popularity often waters the original effort to something that is sellable.

    I am very active promoter of “data to story”. As simplified as this is, the idea is to tell the right story based on understood data. But dressing up commercial/PR stories and claiming to “have the data” is not the same thing, this is what we have already.

    So, I absolutely agree that there is a growing danger of over-simplification here. If the people hopping on the bandwagon of data analysis just want to pick the low hanging fruits and just want a “story” the whole concept of mindful investigation, questioning, probing, failing and insightful visualization of data can be broken pretty quickly.

    There is a reason why out of millions of “stories” only some really are worth telling. It is like digging for gold – you need to have a high frustration level. My feeling is that given the growing popularity of data science, data journalism and so on we should be well aware that in the wrong hands even a good thing can be turned to bad quite easily.

    I have no answer how to manage the wave of enthusiasm and still keep the core of the concept intact. Your post rightly warns about that danger.


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