One of the hardest parts to write about this particular blog post was the title. In the end, I’ve gone with my chosen title to make it clear that this is a book review of “Data Visualization in Society” – a book formed of a collection of data visualisation essays and edited by Martin Engebretsen and Helen Kennedy. The book is a whopping 440 pages and is expensive but undoubtedly worth it if purchased through usual channels. However, it is available electronically through JSTOR at the following open access link free of charge. It was this availability which made it an excellent book for Datawrapper’s Data Vis Book Club earlier this month. With that in mind, I haven’t read the full collection (it remains a physical book I would love to read and devour in full!) but have read the essays/chapters necessary for the book review.
In many ways the chapter that resonated most with me was the foreword from Alberto Cairo which was entitled “The Dawn of a Philosophy of Visualization”. In introducing the book, he describes it as a “collection of chapters by scholars and professionals who don’t call themselves philosophers of visualization but who, in practice, operate as such.” This got me thinking – surely this is going to be a collection of data visualisation papers, what has philosophy got to do with it? However, he then posts a long list of questions posed by the chapters:
These are just a small selection of the questions asked – we are then asked to reason, to reply, to argue, to write and publish. Every question leads to knowledge and every refinement leads to your own philosophy. From my own point of view, It certainly gives me validation and makes me rather pleased to run a website where every post is a question! One of my favourite data visualisation speakers, authors and, let’s face it, one of my favourite people, Steve Wexler, often starts his talks with the following:
I would say the same thing: open discussion, healthy respect, constructive disagreement and thoughtful dialogue on all sides are what makes data visualisation a unique field. I don’t know that I can claim to be established enough to have my own philosophy on data visualisation. But I do know that if you were somehow able to mould the (currently 88) questions I have posed from each of my blog posts into some over-arching data visualisation philosophy, that it would not be the same as Edward Tufte. Or Alberto Cairo. Or Steve Wexler, Florence Nightingale, Giorgia Lupi … or anyone else I could name (of course, it would but closer to some than to others). For those of you reading this – I encourage you to enjoy and be inspired by the content of any one of my pages or creations. But I also encourage you to disagree. Let’s be honest, I’d be suspicious if you didn’t, somewhere along the line.
That mindset was such a good inspiration for two things – to read the chapters of this excellent collection of Data Visualisation essays, and to discuss them via the above mentioned online book club. So much to learn, so much to understand and agree with, and so many points posted in a different way: not to argue or disagree with per se, but to complement my thoughts and understandings on what I had read and inferred myself. Taking the four chapters/essays read and reviewed one at a time:
Chapter 2: Ways of Knowing with Data Visualisations – by Jill Walker Rentberg
This did a great job of covering objectiveness in data visualisation. Two concepts were really key: first, the overview effect, (term described by Lisa Charlotte Rost in our book review – also used in the article was the term “God’s eye view“) essentially having the comfort of having the “true” data systemised in front of you in a “fantasy of knowing” or of “total knowledge”. The second concept was that of dataism – defined by José van Dijck and described as the ideology of big data, characterised by ‘a widespread belief in the objective quantification and potential tracking of all kinds of human behaviour and sociality through online media technologies’ with the idea that data visualisation really relies on this trust. A third concept that is really relevant is that of the “Average as norm“: the idea that data and concepts can evolve into a a “belief” of how things are over time. All these things represent real challenges when working with data and determining whether our data is objective. This was such a thoughtful chapter, off the back of Alberto Cairo’s introduction, to pose more questions about how we work with data, and how we determine what we think can know from how the data is presented.
Chapter 11: Data Visualisation and transparency in the News – by Helen Kennedy, Wipke Weber and Martin Engebretsen
This chapter was certainly relevant to the news today given the proliferation of data visualisation in the election and COVID era. It used a number of sources and interviews to give a broad range of answers an opinions on transparency and trustworthiness in data journalism in particular. My own most interesting quote was that “one respondent (Developer) described it as lazy not to provide and angle onto data, because doing so is the essence of journalistic work” – when approaching data visualisation from a journalistic perspective it’s important to tell the story and explain the angle. This is very much against my own style of visualisation (particularly for personal non-business visualisations) but that only serves to show the difference of goals between data journalism and some of the many other facets of visualisation. The skepticism of audiences to data visualisations in the news, perhaps due to the proliferation of misinformation, only adds to the challenge of data visualisation practitioners in their decision of what/how much data to show and how much to guide audiences on how to understand and interpret things.
Chapter 16: What we talk about when we talk about beautiful data visualizations – by Sara Brinch
This is a topic I was amazed I hadn’t covered in my blog before. Aesthetics and beauty in data visualisation, as in anything, can be such a personal choice, and it’s certainly one I have my opinions on. I guess they only ever made it to my drafts! In essence, this was a the lightest of reads of each of the chapters for me, mostly because I was aware of most of the content discussed and agreed wholeheartedly with it. The chapter acted as a teaser for many of my favourite textbooks and visualisation books that adorn my bookshelves and was a reminder of why I love the field that I participate in (and blog in). You don’t need me to add my own thoughts on what constitutes beauty in data visualisation, as I’ve got dozens of other posts that refer to my influences and my own attempts. But as mentioned above, I recommend anyone to agree, disagree, or have their own interpretation on beauty and aesthetics in data visualisation.
Chapter 18: Exploring narrativity in data visualization in journalism – by Wipke Weber
When we discuss narrativity we are always concerned with the concept of data storytelling – possibly one of the most contentious topics in data visualisation. Indeed, the chapter asserts that “A lively discourse about storytelling in journalism has developed in recent years”. The mantra in journalism is often “Show, don’t tell”, and a focus on storytelling in data might well be “Tell, don’t show”. This chapter explains the key differences in both aspects of data visualisation and introduces the concept of narrativity: a “narrator” may be present in the form of micro text elements such as titles/captions/tooltips, sequentiality might be present in scrolling and animations, temporal dimensions can tell the story of data change over time. In particular, fellability addresses the question of what makes a story worth telling. To summarise, both “showing” and “telling” can be present in narrative data visualisations, with the conclusion that “Show and tell” should perhaps be the mantra that best covers data visualisation in general.
These are just snapshots from four chapters which in turn are just snapshots of the full collection of essays and articles. I thoroughly recommend reading them, discussing them and using the information within them to greatly help your understanding of data visualisation in society. In doing so, you’re building up your own data visualisation philosophy. Pretty cool, right?!