Storytelling here and there

I came across 2 articles about storytelling on the same day this week, one by Jonathan Corum (1) related to the recent Tapestry conference (on Storytelling and Data Visualisation) and the other from Emma Coates (2) at Pixar, relating to 22 ‘rules’ of storytelling.

Though I can’t at all claim that comparing both articles is a fair thing to do, reading them adds to my hunch that story is thought of so differently in data visualisation than it is in most other places. And this is partly no surprise at all, because in data vis generally the idea of story with a clear beginning, middle and end, with characters and narrated voice and such is not the norm.

But what really got me thinking is that in data visualisation a lot more attention is put on visual methods for displaying data and their effects on users trying to ‘decode’ the visuals — than it is for say the ‘craft’ of telling a story and entertaining. Can I even expect to have favourite books, films AND visualisations?!  This is where it seems to me that data vis can learn from storytelling in other mediums. Sure most dataviz is not really intended for entertainment more so than it is for orientation and understanding complexity, but nevertheless people surely want to be entertained and engaged with a compelling story

I am exploring what relationships exist between data visualisation and storytelling in some current research. What I am finding is a curious kind of storytelling mode that happens through data visualisation: curious because such data-led, objective and fact based stories (not to mention the way narrative can emerge in them through interaction), are hard to reconcile with a traditional storytelling approach. Basic story elements like plot, character, narrator, suspense, conflict and closure can all and often be missing

Yet this doesn’t seem to make the work any less interesting and when I see a compelling visualisation it makes me wonder about cause and effect, and perhaps how people may have been involved or affected in the phenomena that are presented. A natural thing to do, to try and make sense of it by making it a complete story.

One impression is that a lot of Data Visualisations give the same effect of a vignette, more so than a full story.

Link to 2 storytelling articles:

Mirko Lorenz: from the practitioner perspective…

As some of you who’ve followed my blog may already know, I’m a part time PhD student at the London College of Communication. That’s what I do for some of my time, and the rest is taken up running the Graphic Design course and teaching here at the University of Lincoln. It’s been too quiet on this blog I’m aware, and long solitary periods sometimes need to happen when you’re wrestling with research, learning and keeping everything just pinned down. Anyway, to save extending such an apologetic opening, I want to share with you now some insight’s gained from talking to practitioners recently about their involvement in storytelling and Data Visualisation.

As my research goes further down the road of data visualisation design and what I call ‘invitational’ aspects of datavis, in the context of data journalism and storytelling, it’s been very enlightening to talk with several people about specific aspects of their work in this area.

This time I want to say a ginormous thanks to Mirko Lorenz for his insight and response to the following research interview questions. What he has to say here raises interesting points around telling stories through data, both from the theoretical and practical points of view.

I turn now to the unabridged text of this interview. Thanks again to Mirko and comments are welcomed.


Chris: Please describe your job position and role.

Mirko: I am a journalist, information architect and trainer. Started in print in 1985 as a free lancer, then moved to online in 1995. I write, develop concepts, including wireframes, conceive new software and manage the process to get them working.

For the last 17 years I am working on new ideas for journalism and media companies. For the last six years I am a member of the Innovation Team of Deutsche Welle. We participate in EU funded ICT research (e.g. semantics, cloud computing) and aim to extract new concepts that could help in the newsroom.

Since 2010 I am active in the space of data-driven journalism. I organized a conference in Amsterdam together with Liliana Bounegru from EJC, speak often at events about it, tweet and network.  With Nicolas Kayser-Bril and Gheoff McGee I wrote an article about a potential new position of media organizations in the future, which was published at and re-published by Nieman Lab.

Right now I am developing a curriculum, did trainings with journalists at organizations like Der Spiegel, Deutsche Welle, ABZV and recently for Mediacentar Sarajevo. One bigger current development is an open source data visualization tool called (with Nicolas Kayser-Bril and Gregor Aisch).

Chris: Please describe any challenges and opportunities data present to you in your daily work.

Mirko: Data, if understood, can add a layer of information that is deeper and more correct than other forms of journalism in certain cases and contexts. As a future perspective I would like to see techniques currently described as “Business Intelligence” into something new I would call “Public intelligence”: Media, institutions and individuals should be enabled to see and understand how an issue might affect them.
My favourite example here is the Rent or buy calculator, courtesy of the New York Times.

Challenges are primarily training – the technologies that can be used are there, much of it is open source and free. Another challenge is to avoid making mistakes that could turn the generally positive development of open data, open source into something harmful.

Chris: Is it important that your audience can interact with the data in your work? (this could be for example via inviting user input and manipulation, social commentary facilities, sharing source code and data). If yes, can you explain how you feel this is beneficial?

Mirko: This depends. The main goal of the data transformation is to move away from quantity to quality. The aim is to tell stories. This can in many cases be combined with data interaction, data download or crowdsourcing. But, there are other data stories where the audience or the user simply expects to “see” the picture. So, interaction and manipulation are additional options, but should be applied when appropriate and when they advance the story. A good example is what the Guardian did around the MP expense scandal in 2009.

Chris: In the context of communicating with data presentations, do you aim to capture and make use of possible audience/user contributions and interpretations? If yes, can you explain how and why? If not, is this because it is less applicable/appropriate to your communication intentions?

Mirko: Again, depends. Take Hackathons and other forms of one day creativity creation. If planned well, these can build new communities, new ideas, etc. If there is no concept, they just waste a lot of time.

So, user contributions are great, but they should advance the story. (I know, I am repeating myself).

Chris: Is part of your communication goal to help your audience to analyse data themselves (exploring), to provide a representation of the data for your audience (explaining), or both? If both, does this depend on any external factors such as source material and communication context?

Mirko: Both. There are three main categories of news from my perspective:

(1) Assumptions about the world: This is breaking news – people check to see if anything happened close to them. If not, they quickly forget, even with important issues. So here, explaining why a remote issue is relevant, should be a big goal.

(2) Opportunities. New jobs, new things, anything that can be viewed as a move forward. People around the world are searching through information to find something that benefits them. Here, exploration of data can add substantial new options for reporting and information offerings.

Of course, both forms can be mixed, depending on the subject

(3) The fate of the others: Although quality journalists, etc. don’t like this too much, there is a third category, consisting of people and home stories as well as gossip. This is essentially “yellow press”. We are interested in anecdotal reports about celebrities, etc. though, it’s quite human. The reason why (from my point of view) is that we are enabled to compare whether someone richer/poorer might lead a better/worse life.  Driven by social media this is actually gaining more attention. Put positively this can work as “social glue”, more negatively it is just diversion.

Chris: Is storytelling an important part of what you do? If yes, can you briefly explain how you see the role of data in storytelling?

Mirko: Story is key. To transform dry statistical material into something that provides new knowledge and might even change how people think, is the goal. Prime example is how Florence Nightingale reported about “Diagram of the causes of mortality of the army in the East” in 1854. This is a big story, on one sheet of paper. Transformed healthcare.

Chris: Do you anticipate that your audience will have little or lots of topical subject specialist knowledge and in what way might the form and scope of your data presentation be contingent on this?

Mirko: This is why story as structure and convention is so important. You can introduce almost anything to any audience, but to succeed something that is hidden must be transformed into scenes, that follow a certain structure (Beginning, middle, end). The simplification that is done by developing a story helps to make the content understandable.

Good recent examples are the reports about Olympic disciplines like swimming and 100 Meter Sprint by the New York Times.

Even further advanced are pieces about top athletes, again from the New York Times. Here, data is used to create scene after scene – after 1:30 you know more about baseball or tennis than ever before.

The stories below are built along classical narrative structure: A protagonist is introduced, then explored step by step. Both stories have a culmination point (e.g. when they freeze 1.200 throws of the baseball player in mid-air). Data and visualization techniques are use to provide new perspectives and insights. Watching both there is a certain pattern (e.g. when they compare the number of spins of the balls for both sports).

NYT: Mariano Rivera

NYT: Speed and Spin – – Nadals Lethal Forehand

Chris: Where you are communicating through data, how do you/your team typically make decisions about what and how much source data is necessary or appropriate to include within any single article/communication?

Mirko: Depends. Nightingale transformed healthcare with a small datasheet. Other instances, e.g. Arab Spring might have millions of data points. Again, not quantity, but quality and the potential story coming out of it are interesting.

Development is done by storyboarding, based on traditional questions like who, what, when and how….

Chris: In cases where data visualisations feature in your work, can you describe whether they are normally positioned along with accompanying text/images/visualisations or just used on their own? If either one or both, can you explain any decisions behind such placement?

Mirko: A picture as well as a graphic should always have one byline and tell a brief introduction story to enable the connection to the reader. These texts, if good, can be very, very short.

Chris: How do you evaluate or judge the success of the data presentation format that you create or use in your work?

Mirko: I would focus on getting quality feedback – like: I never thought about this like that. Quantity builds over time if you achieve this point of information and understanding.

Chris: Can you describe how practical limitations such as available time, skills and resources impact on your work?

Mirko: There are just too many offerings currently, plus too many areas of knowledge from code to software to techniques. So, one way is to enable both “quick data visualization” for some stories as well as really working a week or longer on bigger interactives.



Thanks once again to Mirko Lorenz for his insight and generous contribution to this research.

A hybrid of part to whole and nominal comparison graphs

After an intensely busy period, it’s nice to be back and looking at developments in the field. I came across some work from an ex student of mine Ben Willers earlier.

For some time I’ve been thinking about visualising part to whole relationships whilst effectively allowing for a means to draw comparisons between those parts. My earlier work in progress had such a part to whole chart in which I was always mindful about the problem of only one of the category measures starting at zero, this despite other aesthetic decisions that needed to be taken in balance.

Ben’s work looks at this problem in an interesting way, in a kind of hybrid part to whole / nominal comparison chart setup (extract pic below). However I do think that this method has some pitfalls too, as I mention over on Ben’s blog.


Moving on from the style vs utility debate in visualisation design

One of the biggest differences of opinion in the data/information visualisation design community arises from the form versus function debate – specifically what a visualisation should look like (its style) versus what it should facilitate (its utility). This pre-occupation seems to have formed two ‘sides’ in the visualisation community. It tends to yield healthy and intense debate (e.g. here, here, here and here) whilst serving as fertile ground for creative practitioners (e.g. here, here and here). But the general contention seems polarised and a little tired – where concerns to either reconcile or celebrate differences between aesthetic and utility centric visualisation design leads to a sense of paralysis preventing design and research from moving forwards. At its root – is the comparability of practices invoked in the argument (namely between ‘infographics’ and data visualisation) justifiable?

It’s a very neat summary about differences between infographics and visualisations by Robert Kasara over at Eager Eyes:

‘Visualization is general, infographics are specific. Visualization is context-free, infographics are context-sensitive. Visualization is (largely) automatic, infographics are hand-crafted.’ Robert Kasara

And there are two even more fundamental distinctions to be made. Infographics are primarily for pointed communication around a small amount of data to non expert audiences and, generally, are manually labour intensive to produce. Whereas data visualisations are primarily for an exploration of large amounts of unstructured data, more often for analysts and subject experts, and tend to be produced by machine automation. So when both infographics and data visualisation are invoked in an argument about their style vs their utility value, it’s more realistic to acknowledge that infographics and data visualisations have different purposes, users and use contexts that makes a universal set of design principles hard if not impossible to define here. What this really suggests is a clear difference in the ‘needs’ that visualisations are filling and ultimately to a need for differing and more nuanced design principles.

Coming from very different ends of visualisation design practice, these quotes from Stephen Few (a proponent of functionality and clarity in design for Business Intelligence visualization) and Alberto Cairo (an information graphic designer and journalist) are perhaps good examples where reflective practitioners are considering tensions of style vs utility in their design work:

‘I am making a distinction between visual beauty as a goal in and of itself, which is not useful in a visualisation, and pleasing aesthetic qualities that contribute to a visualisation’s ability to communicate, which is what I teach and attempt to inspire.’ Stephen Few

‘Calling infographics just art makes people think that the priority when creating an infographic is to make it look beautiful. This is not a priority, but it’s important. Infographics have to be beautiful and have to be attractive, but that’s not a priority. The priority is to make them functional, to communicate something. I tried to fight that idea for a while, but I gave up, because this idea is rooted in the culture. Then I agreed to call infographics art, but proposed that it should be called “functional art”. First of all, it needs to be functional, and then it can be art.’ Alberto Cairo

For the sake of developing design practice it seems that (between the ‘two sides’ of visualisation design) there is a commonality in trying to reconcile form and functionality with regard to specific audiences and contexts of visualisation deployment. But all of this seems to be very designer centric. We should also be asking what people themselves bring into and do whilst they are decoding visual representations of data; whether that be in infographic or data visualisation form. What design principles are informing visualisation work that account for specifics of audience, motivation and context of use – and what knowledge and competences do designers have in curating and visualising data given such variables? The answer would lead to a greater understanding of the variety of visualisation use and lead to developing design methods accordingly.

If this could mark a point of departure in what seems to be too binary a debate – then it points to richer and more interesting ground in which we can hope to extend knowledge of design practice for info/data visualisation. For whilst accuracy and clarity are likely to be crucial in visualisations that are efficiency critical and for use by experts, equally important may be aesthetic appeal, play and the ‘guided’ experience that are the hallmark of casual and non specialist visualisation. Both are different, with different design principles and methods.