Avoid the dead-end dashboards: further reading


Today I delivered the final webinar in our series, “Dashboards to inform and inspire”. The topic was about avoiding dead end dashboards. You can watch it, along with the rest of the series, here. The series has been inspired by The Big Book of Dashboards: do go check it out, if you fancy.

Go check out the Fitbit dashboard. It will change your life, initially. And if you get more life out of one than I did, let me know.

  • “Springboard” not dashboard
    • Governed data sources (which are useful for enabling self-service while retaining control. We use them to encourage account managers to build personalized dashboards

If you watched one or all of the webinars, do let me know what you thought.

The Data Debate, 2017: Further reading

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.

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My book is The Big Book of Dashboards and Andy Kirk’s is: Data Visualization: A Handbook for Data-Driven Design. Jade is yet to write a book, but I’m sure it’ll be amazing if she does.

Data has lost its way in the public sphere

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
  • Tristan Guillevan won Iron Viz at least in part because of his use of animation to tell a compelling story about US house prices.
  • Bloomberg’s climate warming chart is, I believe, one of the best animations in dataviz. It reveals the creeping, then accelerating, trends in temperature in a way that stops and makes you think.
  • Finally, I ran a terribly weak poll on Twitter. The poll is bogus, of course, but the thread of conversation was excellent.

Which comes first: the chart or the question?

I was hugely influenced by the Chart Chooser from Extreme Presentations; this was the first time I saw that charts had such a powerful underlying structure. There are other excellent chart choosers, such as the FT’s Visual Vocabulary, Jon Schwabish’s Graphic Continuum, and Andy Kirks’s encyclopaedic, tool-focussed, Chart Directory.

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.

Steve Wexler considered the BAN issue in his recent blog on iteration and collaboration.

Defend the Indefensible

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.

Stacked bars

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.


Source: BBC

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.

Design Tricks for Great Dashboards: Resources


As part of the dashboarding series of webinars for Tableau, I presented “Design Tricks for Great Dashboards“. I hope you enjoyed it. Let me know your thoughts below or on twitter (@acotgreave)

Here is the list of resources and ideas I mentioned in the talk.


The books I used for inspiration are:


Further reading on the design principles

Dashboards and visualizations

Mike Cisneros’ Oil v Gold masterpiece

Subscriber Churn from Big Book of Dashboards

Jacob Olsufka’s Energy in America dashboard

Elena Hristozova’s National Student Survey analysis

Accenture’s RBS 6 Nations score trackers

Michael “Framing Master” Mixon

Transportation Reports by David Krupp

What can we learn from eye-tracking? (resources list)

Amy Alberts and I hope you enjoyed our talk at #data17 about eye-tracking and what it can teach you about building better dashboards.

This post is a list of links and resources we talked about in the talk:



Affordances and Signifiers: applying design theory to your dashboards

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?

Click here for the interactive version. Creidt: Eric Brown

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:

8 affordances (there are further affordances in the tooltips)
  • 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:

All the affordances!

Here are the main changes I made:

  1. Move all drop-down filters into one place
  2. Added instructional text to the scatterplot and colour legend
  3. 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.

Although this example isn’t in my upcoming book, The Big Book of Dashboards is pakced full of successful dashboard designs and tips. Sign up for details here.

Note: Eric’s dashboard is excellent, it looks super and is a pleasure to explore. He’s graciously given me permission to use in this post, and I thank him for that. 

Visual Design Tricks Behind Great Dashboards

At the Tableau Customer Conference this week, I did a session entitled “The Visual Design Tricks Behind Great Dashboards”.

You can watch the recording here. (registration required)

Here are the resources I shared.

Design Tricks


For all the design tricks, including the impact/difficulty breakdown, check out my Design Month posts.


Visualizations referred to in the talk


I hope you enjoyed the session! What other design tricks would you add?


Do you have a world class dashboard? Want to share it?

BBD image
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.

Your dashboards don’t have to be in Tableau!