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.

Review: The Two Types of Data Visualisation?

‘Premise: The Two Types of Data Visualisation, and Why it Matters to Understand the Difference.‘ — Tom Steinberg

It’s a bold claim on Tom Steinberg’s blog, but nonetheless thought provoking – that there are only two types of data visualisation: ‘Story Visualisations’ and ‘Answer Visualisations’. In Tom’s words ‘Story Visualizations are those produced by one set of people with the goal of telling a story to an audience,’ whereas ‘Answer Visualizations are produced to supply an answer to a single question posed by a particular person.’ A pretty neat and handy framework for talking and thinking about visualisations. But one that’s also problematic.

There’s no doubt that the kind of ‘Answer’ visualisations Tom refers to (for example the ‘What Size Am I?‘ visualisation) have a purpose and scope quite distinct from, lets say, more complex and onerous forms of data visualisation. But at the same time the divide he sets up doesn’t account for the kind of visualisation where ‘answers’ (or maybe at least signposts to them) appear out of a story told through data. What I think Tom is referring to is more of a distinction of functionality that the ‘Answer’ class of visualisations have which is akin to ‘apps’ – in as much as they are visualisations more clearly targeted toward a specific user need, well defined in purpose and being most efficient for carrying out a limited range of tasks. Being Tom’s case in point, the ‘What Size Am I?‘ ‘app’ is a great example of this:



But offloading the then non ‘Answer’ types of visualisation into the ‘Story’ camp sets up a binary of ‘Story’ and ‘Answer’ which is less convincing and perhaps needs to be nuanced, or better explained. We might turn to data visualisation for Business Intelligence as an example of combined ‘Story’ and ‘Answer’ visualisation, where data is visualised and can be explored, say to diagnose a business problem or to help spot a business opportunity. This example from Spotfire shows a data visualisation that allows for selecting data sets and manipulating data views so that the data ‘story’ and ‘answers’ might be found:



Examples such as this might not have clearly defined questions or even stories, at least not before the user works with the visualisation to define what they might be. We might also turn to data-led journalism – where an insight gained through visualising the data can form a story, a question and/or an answer. We need to accept that examples such as this involve data having been curated in some kind of way, so of course the extent of possible ‘Stories’ and ‘Answers’ ensuing from the data will by default have been governed. Their functionality and purpose may be less clearly defined for their ‘users’ too. But is does hold that visualising data to tell ‘Stories’ can lead to ‘Answers’ too. This comes across as a barred combination in Tom’s present framework.

In my own humble work I’ve found that both approaching data visualisation with and without a question in mind yields answers (and often even more questions), both for me and the people having seen or ‘used’ my work. However I’m a practitioner (albeit of modest skills and means) and Tom is talking about data visualisation from a user-cum-creator point of view. Tom is concerned that ‘lowering the barriers to Answer Visualisations seems almost unimaginably hard and distant’ – concluding with an air of inevitability that it won’t be until the right software or system becomes available that ‘everyday people’ will be able to do their own bespoke forms of data visualisation. This may hold true – although freely available programmes such as Tableau Public offer powerful exploratory visualisation now (and are realistically within the grasp of most computer savvy people). At the same time open source languages for coding of data visualisations are reaching maturity (and, aren’t kids being taught to code at school, anyway?!). But perhaps this is all too technologically deterministic – surely it’s just as important that the ‘everyday user’ develops the right capability to be able to deploy and perceive data so that they can recognise the answers (and questions?) that the data is telling too?

I thank Tom for his excellent blog post and insight because it really got me thinking. As always, comments are welcome.