Why I haven’t blogged or done Makeover Monday recently

This could be the least asked question since “How can I hire Lynton Crosby?” but the stats show this blog still gets a few views each day.  So why the radio silence?

The short answer is that after a lot of lobbying I finally secured Tableau for my team at work, and my development time since has been spent putting together a training plan and creating dashboards for real-life work scenarios, none of which I can share.

Which got me thinking: how much of what people do in Tableau is hidden behind company firewalls?

I did get a peek behind the curtain at the Midlands Tableau User Group last Tuesday with a demo on how Parexel use Tableau (as a portal for multiple external users: an interesting contrast with how we use it as an inward-facing tool to answer business questions using data).

I also created one viz I can share, inspired by an example in the excellent Big Book of Dashboards.  There is a screenprint below, and the interactive jitterplot can be accessed here.

Like everyone in the field I get inspiration from other people’s work, and used Steve Wexler’s post I’ve Got the Jitters (and I Like it!) to create the main chart (albeit the data points are ordered by club name, rather than randomized).  Another reason to keep Tableau Public!

Are home attendance and success related.

The Friday project – week 19

A late – and short – blog post this week as I write from my sickbed, stricken by a bout of man flu…

This week’s lesson: find your angle

My thoughts this week harked back to Andy Kirk’s original advice to think like a Data Journalist: the importance of finding an angle.

Makeover Monday has some excellent examples of this every week, and it is amazing to see just what completely different tacks people take using the same data.

Week 16 had some particular fine examples (take a look at Adam Crahen’s below or read Mike Cisneros’ blog post Going a Different Way: The NHS Dataset).  And if you don’t already follow Mike’s blog, you should – it is a must read.

adam crahen

So I started week 19 looking for an angle, but with limited time to grapple with what seemed a complex and incomplete dataset I landed on a fairly superficial subject for my viz: car price by colour.

I was reasonably happy with my final, simple, colourful design (a lollipop chart using custom marks), and finished it within my hour timeframe, so: a partial success.

Car colour and price

My second viz was made the day after Chelsea won the Premier League, and was triggered by a curious stat I saw that morning: that my team Ipswich Town had been Champions of England more recently than Spurs.

My first step was to find the most recent season each English club has been crowned Champions and show it as a bar chart, but it felt….boring, so with a bit of googling I found the exact date that each team had last won the league and cross-referenced against a list of UK number ones.

It is barely Data Visualisation at all but I had found my angle (look how young Elvis looks, and Madonna!) and I think the songs are more redolent of times past than a number on a bar chart:

Funny how time slips away

The Friday project – week 18

This week’s lesson: be timely

Last week I was experimenting with an infographic to celebrate Lionel Messi’s 500th goal for Barcelona. I was reasonably happy with the results as it is not my normal style, but like La Liga defences, I was playing catch-up.

Messi scored his 500th goal in the game again Real on Sunday 23rd April so I had earmarked the following Thursday to put together my viz (I know – it is called the Friday project – but I had a window in my schedule).

The problem was that Barcelona had a game against lowly Osasuna on the Wednesday night, and Messi scored another two!

messi tweet

With this in mind I was interested to hear earlier in the week that Roma’s new Sporting Director had revealed that Francesco Totti will retire at the end of the season.

Like Messi, Totti is a one-club man and a legendary figure in European football, although unlike Messi he is under appreciated in the UK (check out Hoddle and Souness on Totti if you don’t believe me).

So this seemed like an opportunity to develop the infographic with a new subject, and be ahead of the game when the man himself confirms his retirement, or plays his last game for the club.

As with Andy Kirk’s original advice, although this was far from a scoop, I was beginning to think more like a Data Journalist.

Here is the infographic below, or take a look at the interactive version on Tableau public to use the hover options and play the YouTube video.totti

And, talking of timeliness, here is the Makeover Monday remake I did on Saturday…just the five days late!

Sydney Ferries Network


The Friday project – week 16

This week’s lesson: always be accurate

This week I managed to squeeze in two quick vizzes, and made mistakes with both of them.

In my work life I know how inaccuracy (or even perceived inaccuracy) can erode confidence in an individual, team or department if it is not addressed.

The diagram below illustrates this well for me, although it can be flawed analysis or processes, rather than data, that erodes confidence.

data wheel

Mistake #1 – Makeover Monday

So here is mistake number 1 – see if you can spot the difference:

Here are the top skills - original with mistake

Here are the top skills

Got it? For some reason I misread the first country as Austria, not Australia, and edited the alias and added a flag symbol for the wrong country. Total brain freeze!

Fellow Makeover Monday enthusiast Athan noticed my mistake and let me know (thanks Athan!). I am not up on my emojis but I like to think this one means “you total bellend”.


So what did I do wrong? I rushed, I didn’t compare my finished viz to the original, and I didn’t ask anyone to check it for me. Note to self: be more patient at work when my team cock up.

Mistake #2 – Messi infographic

One of the objectives of the Friday project is for me to broaden my approach and experiment with different styles, so for my other viz I attempted a more infographic-style approach to mark Messi’s 500th goal for Barcelona.

So it is spot the difference time again…

Lionel Messi - original with mistake

Lionel Messi

Got it? The number of FIFA Balon d’Or awards.

So this one was a little more understandable. In the list on Messi’s wikipedia page there are four years where he won the Balon d’Or.

wiki awards

The difference this time was that I double checked against the text of the article, which says he won five Balon d’Ors:

wiki text

So what is going on? Long story short, the Balon d’Or had a different name in 2009 (FIFA World Player of the Year), so both were correct. As with the article, I retained the current and most widely known name in my viz.

The other big difference – I checked before I published my viz so it was right first time. Maybe I am learning something, after all?

The Friday project – week 15

This week’s lesson: they can’t all be winners

bad santa

This week’s lesson comes from Bad Santa, as once again I was playing catch up with Makeover Monday, looking at how Gold and Crude Oil prices change over time.

Normally I try and avoid #MakeoverMonday on twitter but this week I had already seen several entries, including this fantastic effort from Pooja Gandhi:


My problem? I couldn’t think of any other way of showing the data…area charts seemed like the obvious choice, and it seemed perverse not to use gold and black as colours.

Given my limitations (technical and self-imposed – I try and complete every Makeover Monday in an hour) I ended up creating the candy corn in the calendar, a kind of Pooja-lite:

Gold and Oil prices

Acceptable on its own terms, but lacking in comparison with the superior original, I had made my own Bad Santa 2.

The Friday project – week 14

This week’s lesson: the power of learning by doing

This week I passed the half way mark of my six month Friday project and two pieces of good news made me review my approach: I found out that my secondment at work is being made permanent, and I that I had secured Tableau Desktop licences for my team.

So why does this change things?

Firstly, developing a portfolio was fundamentally a form of insurance for the job not being made permanent, a way to showcase myself to potential employers. Secondly, I have taken a fairly punk approach to getting up to speed with Tableau, learning by doing: is this the best foundation for training other people?

Take my Makeover Monday on automation from earlier this week (I am playing catch-up after a week off). I came up with the robot arm / lollipop chart approach more or less immediately, sketched it out, and found an appropriate icon from the brilliant Noun Project, ending up with the viz below:

Will a robot take your job.

But you know what was the best news of all? I had played around with making lollipop charts before, using Andy Kriebel’s excellent step-by-step instructions. And I had remembered how to do it. By doing, I had learned.

So instead of changing things completely, I am just going to tweak my approach. I am going to close a few technical gaps by completing the excellent Tableau 10 for Data Scientists course by Matt Francis, I am going to carry on with Makeover Monday, I am going to carry on developing a portfolio, and I am going to continue with the blog.

After all, when a robot takes my job, I might be looking again…

The Friday project – week 13

This week’s lesson: persevere!

A counter-intuitive lesson from the first Friday where I did absolutely no vizzing whatsoever (although I didn’t spend all day in my pants).

This week I received confirmation that I have finally secured Tableau Desktop licences for my team at work, only four years after my first attempt!

So will this change the focus of my Fridays? Check the blog next week to find out!


The Friday project – week 12

This week’s lesson: steal like an artist

This week’s Makeover Monday subject was basketball’s March Madness, and it didn’t take me long to sketch out my design: small multiple slope charts showing the results for each year’s winner, with colour denoting the difference in seeding between the two teams.

However Ann Jackson’s entry from last week had lodged in my mind. Would area charts look better, with the horizontal line aiding comparison of the margins of victory?


In short, yes. My final version is a blatant steal – and I credited Ann in my tweet. The overall viz needs some work (I want to revisit to add conditional text showing the overall winner, loser and score), but I feel the area charts work reasonably well, and highlight those rare years where the underdog triumphed.

March madness tweet

So why the blog title “Steal like an artist”? Well I stole it from Alberto Cairo, who stole is from Austin Kleon. And the blog subject? Stolen from Neil Richard’s Is it OK to steal?, and Ryan Sleeper’s Data Visualization: The Stolen Art.

So in that spirit I will steal Neil’s last sentence: “if anyone ever wants to steal from me, then I know, unlikely as it seems, that I will have made it!”.


The Friday project – week 11

This week’s lesson: use the right data!

Before I began the Friday project at the start of the year I asked Andy Kirk and Andy Cotgreave’s advice on where to start. Andy C’s suggestion was to participate in Makeover Monday and Andy K’s was to be disciplined, to think like a Data Journalist, and to add constraints.

As my Tableau skills have improved I have sought to add constraints by completing Makeover Monday within an hour on Sunday, leaving Fridays to work on thinking like a Data journalist: finding an interesting story, sourcing appropriate data, and creating a visualisation.

This week it was this article that caught my eye: Man found guilty of killing one of Britain’s rarest butterflies.

large blue

Now butterflies are something I know almost nothing about but the article triggered a few questions for me, e.g.how many Large Blues are there in the UK, how have numbers changed over the years, and how do they compare to the other 25 protected species?

And here is the kicker: I couldn’t find the information I wanted. The best data I could find was these Occurrence (Distribution) and Abundance (Population) Trends for 1976-2014 and 2015-2014, for 60 species of UK butterflies, including the 25 protected ones (top 5 only shown):

Butterfly data

For the Large Blue there was insufficient data for Occurrence change for both periods, a 1,440% increase in Abundance change for 1976-2014, and a 20% decrease for 2005-2014. So why the fuss if numbers had increased so much since 1976?

Without the underlying numbers behind the increase, I had no coherent story. But instead of looking for another topic and wasting a morning, I pressed ahead and wasted the day, creating, then deleting, a dashboard which was devoid of interest. I had less to show from the day than I had from spending an hour on my Makeover Monday viz:

Joy of sex

So what were those key lessons?

  1. Add time constraints to data gathering as well as to data visualisation
  2. Be wary of the sunk cost fallacy: know when to move on
  3. Be thankful for the work done to create those Makeover Monday datasets – it is harder than it looks!

The Friday project – week 10

This week’s lessons: embrace serendipity, and use the right tools!

While checking twitter this morning, I came across the following tweet from my ex-colleague Ferg:

ferg tweet

Lucky Ferg, I thought! (Or something like that). Then I checked out Raw Graphs.

Last week I blogged about how complex it was to create a Sankey diagram in Tableau, and how the process felt more like a test of how well I could follow instructions than a creative exercise.

So could I create a Sankey using Raw Graphs? Two minutes later I had my answer: you bet!

The Sankey on the left took me about two hours to do in Tableau (about the same as a flight from the UK to Milan), the Sankey on the right took me two minutes with Raw Graphs:

All I had to do was paste in the data and choose my graph type. Another lesson learned!

Using the right tool also meant that I could revisit my original intention from last Friday to redesign the diagram below:


Here is a screenshot of my first attempt at a redesign, Visualising UK Labour Market Flows. Although I lost some interactivity by pasting the Raw Graphs Sankey as an image, I think it works as part of a larger dashboard:

Labour Market Flows


The other visualisation I created this week was about the Top 500 YouTube Games Channels for Makeover Monday. For this one I wanted to embed videos of the two highest performing channels within the dashboard.

Sounds like it could be tricky, right? Not at all. I just had to chose Web Page for the dashboard object, drag, enter the URL, and resize. Another example of using the right tools for the job!

YouTube Games Channels