During week 14 we looked at job automation and the potential impact of robots and AI on the UK employment market. The original article was sent to us by Nicholas Bignell and provided a lot of background information. I personally thought the topic was quite fascinating and was looking forward to the data stories the Makeover Monday community would create. And you guys didn’t disappoint me! There have been a number of outstanding vizzes this week. Before I get to those, I want to call out different observations we’ve made this week and there have been lots of positives.

The Good:



People paid attention to filter out the ‘total industry’ dimension member. The data contained one row with totals in it. I could have filtered it out but chose not to. You get the data as it is because it will help you pay attention to these things. With the exception of a couple of vizzes, there were no totals included among other industries. So well done for picking up on that!


Many of you aligned your design to the topic. This week’s challenge was a good opportunity to practice those design skills and I was very impressed by a number of those ‘robot vizzes’. I think the black backgrounds really worked in a lot of the designs, but also ‘standard’ white vizzes with other colour schemes brought the automation and AI theme to live. Simplicity and spaces dominated in a number of vizzes and design elements such as images, lines and call-outs were used very effectively. If you want to learn more about this data artistry then definitely have a close look at the work that came out of this week. While I can’t pick them all in the favourites section, here are some really good examples for dashboard design that you can draw inspiration from:

Adam Crahen’s ‘Human Cost of AI’


Pooja Gandhi’s ‘Workers at Risk’


Pablo Gomez’ ‘Risk or Opportunity challenge’


The dataset provided percentages but some of you very effectively translated this into measures that related the data to a population of 1000 people to provide context and make the numbers easier to comprehend. I really like that approach, see it as being mindful of the needs of your potential audience and appreciate you considering the end-user in your design.

Many submissions this week included explanations in call-outs and text objects and people are much more consistent in stating their data sources. The dataset this week was simple, but the topic was complex and background information provided in the article was rather comprehensive. Simple charts combined with effective annotations and explanations worked really well to bring out key insights and get the message across.

What was really cool this week was to see vizzes in other languages. There were a couple produced in Spanish and I think that’s a great way not just to spread the dataviz and Tableau message to new audiences but also to engage with a topic in a different way. For the authors it means extra work, of course, but also provides an opportunity to rethink their approach and design because different languages require different layout, tone, colours to address the target audience and cultures. And a lot of attention to detail is required to ensure that the language carries through in every small aspect of the viz. So hats off to you multilingual viz creators, well done!

Rosario Gauna’s Spanish Robot Viz


Another point I want to make is that we appreciate it greatly that community members are getting involved in providing feedback on each others vizzes. Even if we wanted to, we cannot comment on every viz and suggest improvements and point out what’s good or to be changed. It would simply take too much time and a lot of vizzes are published while we’re working/sleeping/doing other stuff, so we can’t respond in time. Please continue to help each other get better and give that constructive feedback so everyone can learn.


And lastly, it’s encouraging to see good work from the same people every week. These are the authors who inspire newbies across the community and your regular involvement in the project helps keep us pushing our own skills and learning new techniques. Seeing the improvements over time from so many authors across the community has been great over the last 3 months and while we sometimes wonder if anyone reads these weekly summaries, people are taking feedback on board and implementing it for their furture work. I’m not sure if we can take credit for the fact that (at the time of writing) only 2 out of this week’s 102 submissions featured bubble charts, but maybe our constant ranting about those charts is having an effect 🙂

The Lessons:

Given my praise above, I don’t actually have a lot of critique this week, but there are a couple of points I will call out


Yes, many people consistently state their sources for the data. But what about your images? This week a lot of authors used robot images. These may have been freely available, but you should still state that fact and credit the source. If the image isn’t freely available, don’t use it. Find a free one or change the design so you don’t breach copyright.


No, I’m not being nit-picky here, I simply want to encourage and push everyone to strive for excellence. Some of these apply regardless of your skill level, while others can be more of a focus for advanced users who want to take their viz to the next level:

  • Check your spelling and grammar (get a native speaker to peer-review if you’re unsure)
  • Be neat about the alignment of dashboard objects so they form a harmonious picture overall
  • Reduce clutter where possible – less is more
  • Pay attention to font sizes and font types: are you being consistent with your formatting, are you using fonts effectively and are they easy to read in terms of size and style?
  • Is your viz consistent overall? Are you using consistent terminology? Are your tooltips designed effectively and do they carry the design throughout the user interaction?
  • Use white (or black) space, let your data stand out and clearly tell the message, you don’t need to fill up the entire screen with words, charts, and images
  • Are your colours easy to comprehend? Do you need a legend to explain the colours and if so, is it formatted effectively?


It’s those seemingly small things that can make a big difference. Before you publish your viz, check them off and go through your work with a focus on consistency and apply the final polish so that you always publish something that actually reflects your level of skills and is better than what you did before.

I would much rather you create a very simple visualisation, e.g. just a bar chart with a title, explanation and sources stated, but make it look outstanding. Remove unnecessary lines, borders, etc., give it an appropriate font, colour scheme, add annotations or customise tooltips. That will always be better than creating a complex behemoth of a dashboard that doesn’t invite me to interact with it because it’s ‘too full’ and no thought has been put into its appearance and visual appeal.


With all that said, here are my favourite ‘Robot vizzes’…

Mathieu Lacome

Author: Mathieu Lacome

Link: Tableau Public

What I like:

  • Simplicity and minimalism
  • Effective use of colour
  • The data takes center stage
  • The key message is clear
  • The title sentence is small and leads to a larger font size for the key message, which aligns with the sorting of the bar chart from small to large/significant, so the reader gets a sense of urgency as they work from the top to the bottom of the viz
  • This is Mathieu’s first Makeover Monday submission, way to kick it off! (and welcome to the party 😉 )
Michael Mixon

Author: Michael Mixon

Link: Tableau Public

What I like:

  • The design is outstanding and Michael consistently delivers excellent work, it is truly inspiring
  • There is essentially only a single chart in this viz but the design, colours, image use and text objects are combined so effectively that the story comes to live and conveys certain emotions and the relevance of this topic
  • The image used is neither cute nor threatening which aligns with the neutral, journalistic tone of the viz
  • Michael takes Tableau far beyond a data visualization tool
David Krupp

Author: David Krupp

Link: Tableau Public

What I like:

  • Simplicity and effective use of white (grey) space makes the data stand out and come together in a very effective design
  • The list is sorted by impact on the workforce which to me is more sensible than the sort order of the original article
  • David breaks the data down to the 1,000 people scale which helps me imagine it in real terms
  • Combining the key numbers with a unit chart and a bar chart appeals to me because it gives me different ways of comprehending the same data and at the end of it, I really *get* it
Chantilly Jaggernauth

Author: Chantilly Jaggernauth

Link: Tableau Public

What I like:

  • Outstanding design. Chantilly consistently produces vizzes that have such a visual impact and are a powerful way to tell a story
  • Great story flow aided by lines, boxes, text elements and colours
  • Cool font that suits the theme
  • Human vs robot is reflected in the sides of the viz and the colours. Look closely.
  • Really nice images that bring it all together

Author: shivaraj

Link: Tableau Public

What I like:

  • Simplicity and tidy design
  • Lots of space for the data
  • Circles used as marks consistently
  • Improving the original viz by showing the ‘gap’ between job share and automation risk as a grey bar rather than having the grey bar as row banding as done by the original
  • Great use of the colour legend for the job share and automation risk metrics as a dividing line between the title and the charts
  • A simple and subtle nod to the robot theme is included with the icon that divides the divider line/bar chart
  • I really like the inclusion of the scatterplot to show where different industries fall in terms of job share and automation risk