#AskAndy webinars: the director’s cut

Hello again. We didn’t manage to answer all the questions that were submitted in yesterday’s #AskAndy webinar. But don’t worry, we collected them all, and I’ve written some brief answers to each one.

Apurvaa Vijay Sharada: Do you think at some point the beauty of dataviz overtakes the actual intent behind it? If yes, how do we avoid that pitfall?

Can you have functionality and beauty in dataviz?
Can you have functionality and beauty in dataviz?

Yes – this can happen. You need to remember the purpose of your visualization. Always take a step back and ask if it’s actually possible to discern the meaning from the visualisation. Ask other people if they understand your viz. Adding beauty will make it more engaging, but unless you’re actually making data art, keep checking that the view actually works.

Melissa Black: What do you think are the most common mistakes made with visualisations?

Thinking of beauty before functionality! Many people get enticed by whizzy effects BEFORE they’ve learnt the basics about effective visual communication. On a practical note, I see many visualizations with disappointing titles. The title and caption are a chance to set out the exact purpose of the visualization. It takes moments to think about something appropriate and it can make a big difference to how it is interpreted.

Paul Banoub/Nicholas Bignell: Have you noticed any variations in dataviz trends / styles / usage across different global regions?

Great question. You know what, I don’t think I have. However, I do remember seeing a post somewhere on the blogosphere about this recently, but cannot find it. If anyone has that link, it was a useful read.

Imogen Robinson: Hi all, my question is: how can data visualisations be made more effective in informing decision-making?

The titles tell the storyThe titles tell the story

  1. Make the titles of your views questions. Eg “Should we invest in Singapore?” The question reflects the decision you’re trying to reach. It also forces you to look at your view and ask yourself – does the view help me decide. If not, you have the wrong view.
  2. Use reference lines to show where thresholds are being beaten – anything above a line, for example, needs attention. You can also use simple colour schemes for this. eg grey for most things, and red for those that need action.
  3. Use scatterplots as a way of comparing measures. This helps you determine if one thing causes another.

Peter Wallis: Could you point us towards some other good blogs to help newbies?

I use and recommend Feedly for blog reading. You can import this OPML file into feedly. It’s a list of all the Tableau and BI blogs I read.

Nicholas Bignell: Who are your favourite bloggers?

The Tableau blogging scene is huge (just check any month’s Best of Tableau Web roundups)

I’m more enthused by some of the amazing dataviz podcasts at the moment. Go check them out.

Suzanne Wilson: How do you define the relationship btw data, info, knowledge & intelligence?

I think there’s value in thinking of a wisdom funnel (this isn’t anything new – check out Google) but I have an aversion to funnels as metaphors: they suggest a linear process and a single direction. I’m more keen to think of data, info, knowledge and intelligence as part of a cycle of visual analysis. A bit of knowledge might make you seek out more data. More data gives you info, but only by sharing and acting with others do you get the intelligence

Anton Lokov: What is your favourite tool for rapid dataviz prototyping?

Why, Tableau Desktop, of course! But seriously, I don’t think anything is as good for exploring data quickly to prototype views.

Anil Mistry: Following on from Biel’s question, I am new to Tableau, are there any specific beginner books you’d reccomend?

A great new book is Storyelling With Data by Cole Nussbaumer. It covers the basics. I really enjoyed Information Dashboard Design by Stephen Few, it’s a fantastic introduction. For starting with Tableau, try Communicating Data With Tableau by Ben Jones.

Auzema Qureshi: What was/is the biggest challenge you have faced when using Tableau for data visulisation?

I remember doing a live “Analysts in the Hot Seat” at a Tableau conference in 2011. We were given a dataset, which we’d not seen before, and asked to explore it and build a dashboard in front of an audience. It wasn’t a great conference session. I spent 10 minutes semi-randomly dragging and dropping things around some worksheets, and duplicating lots of things. Then I realised that, uninspiring as it was to watch, this was actually EXACTLY why Tableau’s so amazing – I was throwing things around to see what stuck. What had so far felt embarrassing (“Really? This is how Andy Cotgreave plays with data?”) was actually the power of Tableau – drag, drop, fail fast, move on. After the 11th minute, all that exploration began to gel into something really powerful and I build a cool dashboard.

What charts would make your 5-a-side squad?

I just got off the fun webinar with Andy Kirk: AskAndy Anything About Data. It was fun to field questions from social media. I hope you all enjoyed it.

One of the questions was from Andy Kriebel:

We decided to take Tableau’s Show Me charts and do a squad selection 5-a-side. Andy picked one, then I picked from the remainder. Here’s the “squads” we ended up with, in order we picked them (ie Andy K picked line chart, I picked bar chart, he picked treemap, etc)

Which 5 would you choose?
Which 5 would you choose?

I was surprised Andy K took the treemap as second choice. Andy suggested that maps are overrated (shock!). We both agreed that boxplots and bubbles could be happily left on the bench.

What do you think? If you could only use 5 chart types ever again, what would they be?

The webinar recording is available here.

Functionality or Beauty in #dataviz? Books to help you choose.

Can you have functionality and beauty in dataviz?
Can you have functionality and beauty in dataviz?

One of the great challenges in data visualisation is balancing functionality and beauty.

On the one hand, you want to make your data viz as understandable as possible. On the other hand, it has to be engaging enough for people to want to use it.

Different dashboards and visualisations need to be at different places on the functionality/beauty spectrum. For example, if you use a dashboard every day for monitoring processes in your business, you need something highly functional which allows you to see outliers as quickly as possible.

I get asked a lot about this balance. My answer is a recommendation to read 3 books.


At the functionality end of the spectrum, you have the books of Stephen Few. I highly recommend Information Dashboard Design. His books give you the foundational knowledge you need. If you are designing dashboards for operational monitoring, you cannot go wrong with this approach.


At this end of the spectrum, I’d put David McCandless. Get Information is Beautiful or Knowledge is Beautiful (I prefer the latter). He makes very popular, very beautiful data visualisations. Almost all are impractical for business situations. If you are trying to communicate anything accurately, I don’t recommend adopting his approach at all. I do not however dismiss what he does.

A sweet spot?

Is there a sweet spot which balances both functionality and beauty? The book which gets closest to acknowledging the tension between function/beauty and helping your deal with it is The Functional Art by Alberto Cairo.

But I can only afford one book…

If you’re starting out in data visualisation, definitely read something by Stephen Few. This is vital to understanding the foundations of data visualisation. Once you’re familiar with those, you can start to add more aesthetic aspects of your visualisations.

Your second book should be Alberto Cairo’s. Finally, get the McCandless book for some inspiration.

Whatever visualisations you build, test them on other people for effectiveness.

Let me know what you think.  If you’re going to the Tableau Conference in Las Vegas in a couple of weeks, I’ll be talking about this in my “Visual Design Tricks” sessions.

Breaking the Real Chart Rules To Follow

Stephanie’s original chart.

I inadvertently sparked a debate on Stephanie Evergreen’s blog, “How to show down is good” (go read it – it’s great). In the post, she showed a bar chart whose axis didn’t start at zero (above). The horror!

Not only is this a flagrant breaking of all The Laws Of Dataviz, it came the very week Nathan Yau published an excellent post “Real Chart Rules to Follow”. How dare she!

I commented on Stephanie’s blog that her bar chart was a valid example showing that you could break one of Nathan’s rules, because there’s no such thing as zero weight for an adult human.

Stephen Few made a great point that for Stephanie’s chart, a dot plot would probably be a better option. I agree. Jeffrey made a great point that half the bar length suggests that the person is half as heavy. Fair enough: I agree with that too.

I also agree with my original comment. I’ll defend my comment, but not to the death. Maybe to first scratch but not beyond.

Let’s move on. And anyway, I’ve also written before about zeroes on axes (skilfully avoiding the bar chart pitfall, you’ll notice). And I fundamentally believe there is always an exception to every dataviz rule. We should educate people about guidelines and learning when they can be broken.

Finally, let me pose a question, to which I am genuinely intrigued to know the answer. Is the chart below valid? All I did was change the title and y-axis of Stephanie’s original chart. Now I am showing zero. Is this ok? 0lbs to target is still 150lbs in real weight.

Is this valid? It uses zero, but that zero still represents 150lbs?
Is this valid? It uses zero, but that zero still represents 150lbs?

Which chart should you use to show this data?


What's the best way to show this data?
What’s the best way to show this data?

I’ve blogged before about there being no “correct” way to visualize a dataset. The video below shows how this is the case. Even when data is extremely simple, there are many ways to view it, each being better at answering a different question.

Conclusion? The trick isn’t to think “a line is the best way to show time data.” It’s to consider the question you want to answer. Manipulate and play with the data until the answer is clear.

How to drive the message home with the right dashboard

Today (Thu 16 April) I did a Webinar for Tableau, “How to drive the message home with the right dashboard.” (the webinar recording will be available on that page very soon).

The slides are available here.

And here are the links to the resources I shared:

Design books and projects

Ranking UK political parties according to mentions on twitter by the media
Ranking UK political parties according to mentions on twitter by the media

Tableau Dashboards

How come we see bigfoot fewer times, despite us all now having smartphones?
How come we see bigfoot fewer times, despite us all now having smartphones?

Inspiration and further use cases

Viz of the Day: great messages, every day
Viz of the Day: great messages, every day

How many data points are too many? In praise of the small multiple.

My latest Huffington Post article (published Wed 28 Jan) discusses how amazing our visual system is at seeing very granular levels of detail. Here’s a rather shaky GIF of the different views going from 1 data point to over 10,000:

howmanymarks narrow
Click image to see a bigger version

The inspiration for the column and this post was Ann K Emery’s 2015 data resolutions. I’ve always been a big fan of small multiples, but her specific statement to “do more small multiples” triggered my efforts to break the data out of the charts I’d been making with the Citibike data.

There have been lots of posts celebrating small multiples recently. My favourite is “A Big Article About Wee Things” by Propublica. Go read it! Go on.

What I really need to emphasise is that no single view is the “right” one. Theere’s no such thing as the “right” view. Being able to cycle through these very quickly in Tableau is immensely powerful – each view teases something else out of the data as you feel your way to insight. Each view shows something different and if you can see 30 views in 5 minutes, who knows what insights your data will reveal? What’s certain is that we can reflect on just how complex and yet clear 10,000+ marks appear:

All 10,246 marks in one place!
All 10,246 marks in one place!

3 ages of data viz?

I’ve got this idea for a future theme looking at “3 ages of data viz”. I want your thoughts. Is there something in this idea? Am I right? Are there more? What’s the NEXT age going to bring? What does this teach us about dataviz?

Age 1: The Excel disaster (pre 2000)

(image from http://peltiertech.com/)

The early spreadsheet designers got excited about graphics and gave us 3-d exploded pie charts. If only they’d read some theory about effective dataviz maybe we’d not have had 35+ years of fighting back against dataviz disasters. To be fair to Excel, as you can see above, the defaults weren’t really that bad, given the limits of graphics cards in the day. Unfortunately, people got too excited about the 3d options.

Age 2: the Stephen Few fightback (2000-2010)

(from http://www.perceptualedge.com/)

Stephen Few took on the spreadsheet behemoths in the first decade of this century. He made us all see sense and put science-backed best practice on the pedestal. People saw the light and visual tools began to ditch the dross in favour of charts that actually work.

Age 3: the creative years (2010-present)

What’s the top data dog (http://tabsoft.co/1CF8TAr)

The problem with Stephen Few’s approach is that people found his approach, well, boring. Unarguably his approach was functionally correct and just right for operational business dashboards. But many people were left unmoved. They found that following his approach didn’t engage people. As data journalism flourished and infographics exploded, there was a realization that a balance needed to be struck.

At the extreme end we found that people like David McCandless found success with their design-trumps-function approach  but others, such as Alberto Cairo (see his Tapestry Conference slides) and Andy Kirk (8 hats) pushed the need to ENGAGE as well as INFORM.

Tell me your thoughts

My ideas are fluid around this. I’m trying to make the point that we’re in a great place with the combining fields of creative power and effective design. What else do I need to know?