Today Andy Kirk and I duel it out in the annual Data Debate. We tackled big and small issues in the field of data. The recording will be available here. This post contains links and further reading on the topics covered.
Are politicians emboldened to say whatever they want? If they are, is that changing society’s attitude to data? Is social polarization accelerating this trend? I fear it is.
Sir David Norgrove, Chair of the UK Statistics Authority, was driven to write to Boris Johnson in September warning him of his “clear misuse of national statistics.” William Davies writes on this in the Guardian, using GDP as an example of the end of statistics. Simon Kuper offers a glimmer of hope with advice on how to take on the populists in the FT. However, his main argument is to ditch the facts and lead with emotion and the story. Sure, that might win people over, but if there’s no role for information and data, have we lost the fight?
The case for animation
My main argument is that animation is exceptional for presenting data in a dramatic way. It enables storytelling and creates tension, surprise, and drama.
The master was Hans Rosling, who astounded us in 2006 with his first TED talk on health data, and later with his Joy of Stats
But I see a lot of people fixate on the chart they want to build rather than focus on the question or the data itself. Watch this video. The end result is a chart you’d never find this in a chart directory. But, if your question is: In which years does B outsell A, it’s a wonderful way to represent it (it’s not the only way, but it does work).
Also consider the two charts below. One shows drought index in the US and the other shows US Road Fatalities. The structure of the data is the same (month, year and state). Only one is a successful chart (the drought chart on the right). Consulting a chart directory, there’s a risk you’ll pick the small multiple and publish. But of course, the data itself drives the appropriate chart type.
Big Ass Numbers: awesome!
Which of the above dashboards conveys a headline better? The one with the BANs or without the BANs?
I came to appreciate BANs through the writing phase of The Big Book of Dashboards. I consider them the Headlines for your dashboard. A well defined set of BANs will capture your KPIs in a way you can interpret instantly. Clever use of colour or other visual indicators can show, straight away, whether they are above or below target. Once the headline is digested, then you can decide if you need to devote time to interpreting the charts in the rest of the dashboard.
This was fun! What I like about defending “bad” charts is that it emphasizes the lack of “rules” in dataviz. There are only guidelines and any binary argument of right or wrong is a failed endeavor.
My defense is simple. If your primary goal is to show the total of all categories, then a stack is fine. Yes, that comes at the expense of being able to accurately see the changes over time of the individual categories. If the primary goal is to see the individual categories, a stack is a poor choice. I believe you should consider primary/secondary goals in all assessments of charts. Remember: All charts are a compromise.
The same defense applies here. If the goal is to give the gist of the data, as they tried to in the BBC article on Apple’s Tax Bolthole, then you’re ok with bubbles. A bar chart will provide more accuracy, sure. In the case of the BBC article, I’m not convinced readers miss out by their inability to accurately see if Apple is precisely 30% bigger or smaller than another bubble.
When designing objects, be they hotel room taps/faucets, iPhones, or cars, the creators grapple with the concepts of affordances and signifiers. These terms were introduced into design by Don Norman, author of The Design of Everyday Things, based on earlier work by JJ Gibson.
What are these and how can we apply them to our dashboard design?
An affordance is something an object (or dashboard) can do. A tap/faucet can run hot or cold water, for example.
A signifier is an indicator of some sort. In our tap example, this might be red/blue dots signifying which way to turn the tap to get hot or cold water.
How many times have you tried to use a tap in a bathroom and not known which way to turn it for hot or cold? This is a frustration of modern life: apparently a sleek design is more important than signifying (red/blue) the affordance (hot/cold).
Dashboards have affordances and signifiers. How you implement them will influence their success. Let’s use an example. I’m going to use an excellent dashboard by Eric Brown. It allows you to compare gestation periods of different animals.
Let’s play a game. Here are the rules: take a look at Eric’s dashboard and, without using your mouse, identify all the ways in which you can interact with the dashboard?
How many did you count?
There are eight intentional affordances Eric built into this dashboard. Did you spot them all?
How many of those eight affordances have a signifier?
Here are the affordances:
1, 5 and 6 are drop down filters. Drop-downs are a staple of interacting with dashboards, and web pages. But why put the three filters in different places?
2, the light bulb, is a hover-help tooltip. Hover over the light bulb and you see a description explaining how the compatibility score is calculated. That’s great if you’re familiar with the “hover-for-help” trope, but if you’re not, then, well, it’s just a light-bulb. How would you know it contains an explanation?
3 and 4 allow you to click on the animal to highlight it in the scatterplot and see more details.
7 is the colour legend. If you click on one of the colours, it highlights all animals in the scatterplot in that category. Does the dashboard tell you you can click on the legend?
8 allows you to click and see the datasources.
Note – did some of y ou think you could click on the silhouettes of the whales in the top right? If you’re a Tableau expert, you might have thought you could. But, no, that is just a legend. It has no interactivity.
That’s a lot of stuff you can do with this dashboard. But only some have signifiers.
We could solve the problem by ensuring every affordance has a signifier. Here’s what that could look like:
Here are the main changes I made:
Move all drop-down filters into one place
Added instructional text to the scatterplot and colour legend
Removed the hover-help tooltip and placed the calculation explanation in the bottom right, above the data source links
I could now claim to have fixed the “problems” with Eric’s dashboard. Anyone now coming to the dashboard with no prior training in it, or dashboards in general, now has a signifier for every affordance. If they invest the time in reading the dashboard, they will be able to interact fully.
Should I always create a visible signifier for every affordance?
No. Sometimes you are designing dashboards for an internal audience. They may be familiar with dashboard interaction, or you can train them. In which case you could remove the signifiers.
The thing you need to do when designing a dashboard is consider your audience, and how you can communicate to them that they can interact with the dashboard. Skilled users know to click and experiment, or can be trained to do so. New users don’t have that knowledge or confidence. Your job is to make these decisions consciously, not by accident.
I’m delighted to announce I am working on a book: The Big Book of Dashboards will be published by Wiley later this year. I’m co-authoring with Steve Wexler and Jeff Shaffer.
The book will be a compendium of real-world dashboards, each a valuable solution to real-world problems. There will be sections on the real world challenges faced when building and maintaining dashboards.
So where do we get these amazing examples from? Why, the community, of course! Do YOU have an amazing, world-class, beautiful, high impact, dashboard? If so, we want to hear from you.
Please use the form below to tell us about it. If we like what we see, we’ll get in touch to discuss the dashboard in more details with you. If it has sensitive data, you will need to be prepared to share it with anonymised data.