This post continues my debrief of a great Data Viz event I attended in London, this is #datavizlive part 2.

Building on all I learnt in part 1, we pick up the story with me still attending a variety of sessions across the three tracks. To start I continued attending the Technology track.

On my own data viz training, we work on embedding suitable preparation & quality assurance (QA) into your analysis workflow. Thinking of data prep for Data Viz. Taking this aspect as seriously as the data wrangling & QA needed for your analysis.

So, I was pleased to see a session focussed on testing your Data Visualisations. Off I went to learn lessons from France…

Why & how to conduct Data Viz user testing

The french connection, excuse the pun, came from our speaker Caroline Goulard. Caroline is the CEO & co-founder of DatavEyes, based in Paris.

She made a persuasive case for why you need to test the effectiveness of your data visualisations. To learn what others see in them & how easily they can use them.

The latter functionality point is important, as Caroline stresses behavioural testing. Like many others, I encourage analysts to make use of peer review of their visualisations. Akin to the “what’s going on in this chart?” series run in the New York Times. Using unprompted review to help you learn what others see first & later (without being prompted).

Caroline makes the argument for not focussing so much on first impressions or whether your customers/peers like your data visualisation. Rather, she encourages the use of behavioural experiments and provided useful guidance on how to design & run these. Moving beyond subjective preferences to learn what testers were able to do with your Data Viz (tasks completed).

For more on this approach to Data Viz testing, you can enjoy Caroline’s visual story on User Testing from her blog:

Dataveyes – Dataveyes Stories

Read writing from Dataveyes in Dataveyes Stories. Human Data Interactions. Every day, Dataveyes and thousands of other voices read, write, and share important stories on Dataveyes Stories.

Design thinking puts the data viz User front and centre

I was excited to hear from my next speaker. I’d enjoyed her data visualisations when shortlisted for the Info Beauty awards.

Emma Cosh is a freelance Analytics & Data Viz UX consultant, so she gets to work on an interesting variety of data. In this talk, she brought us back to the core design responsibility of putting the user at the centre & making things easier.

Through numerous interesting & often overlooked examples of effective design, she shared lessons for simplifying our Data Viz. Focussing on what the user wants to get done. For instance, I’d never previously stopped to ponder on the beautiful simplicity & effectiveness of our UK motorway signs. But, Emma knows the couple who designed them.

She also prompted us to think about behavioural psychology as well, a subject dear to my heart. In fact, she made the third reference of the day to Daniel Kahnemann’s work & the need to design for System 1 usage.

One of the tips I really liked was Emma often asking her clients: “What are you afraid of finding in the data?” Her design was then focussed on demonstrating that ‘stories of consequences’. Very much in line with Cole’s training & sparing use of accent colours to draw the focus to what needs attention & action.

You can enjoy more of Emma’s beautiful art on her visual stories blog:

E.G. Cosh

Comic maker and illustrator

Time to bring the emotion back into your Data Viz stories

We’ve talked about the importance of emotion for a few different roles on this blog. For instance, why leaders should show their emotions & the need for marketers to measure the emotional impact of their marketing.

In the next session that I attended, Xaquin Gonzalez Viera helped bring to life the power of considering emotional impact when designing Data Viz. He is well qualified to narrate examples of this in data journalism, given his previous editorial roles at the Guardian, National Geographic & New York Times.

Through a variety of impactful data journalism examples (and plenty of humour), Xaquin talked us through the importance of Metaphor. This included thinking about ‘emotional maths‘, for instance personalising the impact of rising house prices through an interactive map. In this and other examples, the work had been done to think about consequences. So, what could just have been a map of house prices became an interactive experience of seeing how much of the UK you can’t afford to live in.

Addressing important and serious subjects, like gun violence & plastics pollution. Xaquin shares example after example of how the use of tangible imagery & animation can bring to life the human impact of statistics. An important technique in these post-truth times when numbers alone fail to cut through.

As I often say about Analytics, he made the case that “it’s all about people“. We neglect the human and emotional impact of our analysis at our peril. Rather, time reflecting on this can help creative data visualisers create more impactful & engaging visualisations. Don’t just be led by the data.

To see more of Xaquin’s Data Viz work & engaging humour, check out his website:

xocas /SHAWK-us/is a hypocoristic form of Xaquín /shah-KEEN/

I recently led the Visuals desk at The Guardian – a cross-disciplinary experiment of graphics, interactive, multimedia and picture editors. Before that I worked at National Geographic, The New York Times, Newsweek and El Mundo. My most recent obsession are emotional connections in data-driven visual storytelling. I moonlight as a chef.

Finally, #datavizlive part 2 culminates in beauty & truth

A fitting close to this visually engaging day was to hear from the pioneering David McCandless. David has really helped create the Data Viz community in the UK. As well as his own work at the Guardian & as a freelance visualiser, he created the Information is Beautiful agency & the Info Beauty awards.

Followers of this blog will know that I recommend following those Info Beauty awards, to inspire your own Data Viz practice. In fact, we have shared a subjective selection of those winners on this blog in 2015, 2016, 2017, 2018 & last year.

Anyway, in this closing talk, David shared updated versions of some of his famous data visualisations (including the billion-pound-o-gram, infographic on Left compared to Right politics & chart of regular news inflated disease stories). It was fascinating to see how things have developed over the years, especially in terms of scale.

David is a master communicator & I encourage you to hear him live. Beyond just the beauty of his coffee table classic “Information is Beautiful“, he brings to life comparisons of numbers & statistics in a new way (even for listeners to More or Less). I can’t do justice to the many examples he shared but suffice to say for now that he also explained Brexit & which supplements are worth it.

So, I’d recommend grabbing your next chance to hear David live. In the meantime, he has also launched an initiative that I really applaud. Given so much depressing news these days, in true Hans Rosling style, David’s agency is publishing daily positive news. Plus, they are doing it through the medium of data visualisations. So, you get a daily diet of both good news & a new data viz to inspire your creations.

Here’s the site to check for these daily pick me ups:

Beautiful News

Unseen trends, uplifting stats, creative solutions – a new chart every day. From Information is Beautiful.

After #datavizlive part 2, which data viz events will you attend?

I hope you’ve found this #datavizlive part 2 debrief to be useful. It certainly both encouraged and informed my practice.

But beyond this, I’d be interested to know which other data viz events you recommend. Chatting to a few of my contacts, I’ve so far been pointed towards:

Any others you are looking forward to? If you do attend them & write a debrief, please get in touch to see if we can publish that.

More importantly, what are you now going to do differently? I’ve shared eight different talks & topics across these two posts. Out of all those ideas, what one thing could you change to improve your Data Visualisation?