How are your interactive data visualisations working for you or your users? I ask as there have been a number of useful posts published by Data Viz experts recently that suggest all is not well in the world of interactivity.
So, building on our recent themes of Data Visualisation and Data Science, I’ll spend this post exploring how well interactive data visualisations work. If hardly anyone is using them, is it worth bothering?
Given the amount of media coverage and interest in interactive data visualisation, it’s important to consider if they are actually working for their intended audience.
As Customer Insight Leaders we should not be using a technique because it is fashionable. We should be concerned with both the clarity of insight & whether it communicates insights effectively for your audience.
Identifying the problem – even for the New York Times
It is a long read, but with plenty to benefit data artists, so I encourage you to give time to digest this. By sharing the experience of Gregor Aisch, data visualizer at the New York Times – with only 10-15% of people ever clicking on their (often brilliant) interactive data visualisations – he nicely highlights that there is a problem despite the hype.
I particularly liked Dominikus’ summary of a core challenger for data visualisers or data scientists:
“...we data vis people spend too much time thinking about the interactions themselves and less about the audience who is supposed to be using them. And then, well, they might end up NOT using them. We might be super excited about some clever interaction trick, but maybe we’ve already lost our audience before they even saw the graphic. So, as always in design, be aware of your assumptions and your personal bias.”
Following that, he shares three key considerations to help improve the suitability of your data visualisations (including where appropriate still using interactive data visualisations). Those are considering, for your audience: Time; Goals; OnBoarding; Care. In so doing he shares some great positive examples, so it’s also an encouraging read. Here’s that full post:
(edit: Gregor released a new blog post, clarifying some of the aspects and made some great points on the benefits of interactivity) Last year I was lucky enough to go to the Information+ conference in Vancouver where Gregor Aisch, who works at the New York Times, gave a talk about the publication’s graphics and their impact.
A recent positive example of an Interactive Data Visualisation
Continuing that theme of responding positively to Dominikus’ challenge, I recently experienced a simple interactive data visualisation that I think acts as a positive case study.
As the above post encourages, it is quick to use, has a clear goal and prompts users to interact with it to gain the information that matters to them. I share it because it is such a simple example.
To overcome the self-indulgence the industry has demonstrated (focussing on what can be done in D3 etc, rather than their audience), I think it helps to learn from such simplicity. Perhaps surprisingly, this visualisation comes from a government agency, The UK’s Office for National Statistics (ONS).
In this visualisation they are performing a useful social purpose and demonstrating they understand what citizens will be concerned to know. It deals with the controversial topic of migration & in my view does a good (simple) job of prompting users to guess local levels & then see personalised graphs that may well challenge their perceptions.
See what you think:
Are your interactive data visualisations working for you or your users?
I hope that expert advice and positive example helped you. Rather than share more curated content at this stage, I’d be interested to hear your experience.
Are you or your team currently using interactive data visualisations? Do you know how many times they are clicked upon when viewed? Are their interactive functions actually being used?
Please share your experience and any recommended tips to improve their relevance and use. I will then share your wisdom as an ‘interactive’ addition to this post.
Keep up the good work! Data Visualisation does still matter, we just need to think about our audience too.