It’s difficult to be a regular contributor of Tableau data visualisations online via Tableau Public without knowing about the excellent Makeover Monday initiative. For those who aren’t aware of it, I penned an ode to it last year. As last year continued, I posted a contribution every week, from the simple to the complex, learning something new each week. Towards the year’s end, I even decided to go back to the start of the year and complete those from weeks before I started.
Makeover Monday has been good to me – not just a way to test my skills and learn something new each week, but it’s really raised my profile, to the extent of recognition from Tableau itself. Here’s an unsolicited T-shirt by way of a gift from them which I often wear proudly with my name emblazoned on the back (OK, not usually in public, but still …)
Since then, it has gone from strength to strength. This year it is being run by Eva Murray and Andy Kriebel, with, from memory, about 140 unique contributors every week, from all around the globe. In a new move, they will report back on their favourite 4-6 contributions of the week, with great feedback for all the participants of all the good and bad things seen through the week.
So, wait, doesn’t this post say that I’m *no longer* contributing to Makeover Monday? Fifteen weeks in, and I have contributed fifteen more visualisations, proudly sitting with sixty-seven out of sixty-seven weeks completed.
Let me explain.
I’m sure the reboot at the start of the year resulted in a lot of new goals. New resolutions, from people wanting to get involved more regularly. I actually made a resolution as well, but resolved to do the opposite: my previous year had been so good that this year I wouldn’t feel I have to do everything that came up. I’d loved Makeover Monday, but I’d been involved every week, and no longer had to in 2017. I can pick and choose now, choose other projects and ways to advance my skills and experience. Maybe I don’t need Makeover Monday any more. I’ll still take part from time to time if it looks interesting, and if I have enough time to spare on a given week. The weekly roundup of favourites still seems like a good goal/aspiration to have, but I can attain that without entering a visualisation every week. But those will be my terms for the year. And that’s why I decided to stop contributing this year.
So 2017 began with a makeover of some data published by the Australian government about the gender pay gap. I was happy to do my version of the makeover, but it occurred amid quite a controversial week. The reporting method of the original data source was questioned, and there were strong feelings in the community about whether or not it was the responsibility of participants to investigate and call this out. Though the argument was valid, it didn’t feel comfortable, nor did it feel like the right time to leave the project. Before I knew it, it was time for week 2, and a less controversial makeover about Apple sales figures was upon us. I could stop my regular weekly contributions on a happier note now.
Week 3 of my “non-contributing”, and a fascinating dataset about the tweets of Donald Trump. This looked a fun set to get stuck into and teach myself something new, so I didn’t mind contributing to this one. In fact I definitely didn’t want to miss this – the result was my spiral visualisation below, which I blogged about here (click the image for interactive version).
A lot of appreciation for my Trump visualisation. No call-out in the Makeover Monday favourites for the week, but no matter. It’s unorthodox and faced some fantastic stiff competition. Week 4 – wait a minute, I can invent a new tile map for this one! Having had such fun and success working on my Africa tile map last year, this would be a good opportunity to try a New Zealand tile map. The result (and the improvements made on it by others in the community) was blogged about here:
Weeks 3 and 4 had got me back into the swing of things, and before I knew it weeks 5 through 8 were completed with visualisations on employment, Chicago taxis, Valentine’s Day and potatoes. Nothing groundbreaking from me, but the fantastic contributions from the community meant I wasn’t really ready to step away yet. Weeks 9 and 10, on credit card spending and YouTube channels started to make me revisit my intentions at the beginning of the year, as I spent more time practicing, polishing and visualising my contributions but not learning a huge amount more in what i was doing. Most of what I learn now comes from the jaw-dropping standard of fellow participants rather than anything new from myself.
Finally, week 11 was officially going to be the week I stopped contributing. A risqué visualisation on the frequency of orgasms. I don’t like small datasets, this had just 6 numbers. The results – well, let’s just say they presented no great surprise to any man or woman, so there were no analytical nuggets of wisdom to be found. I don’t feel I have infographic/creative skills, and as for the subject matter … well, we’re all grown up consenting adults (apart from poor 8-year-old Joe!) but it’s not really a subject I envisaged visualising. I sat this one out in the knowledge I’d made the break at last, and waited for the weekly round-up.
Finally as a non-contributor I had my freedom! From now on, as I resolved 11 weeks previously, I’ll just do the ones I feel like. I don’t need another Tableau T-shirt, or the commitment of a couple of hours a week for a new viz (they don’t take just an hour for most people, I don’t care what the guidelines say!)
Week 12 – March Madness. A huge detailed dataset, about sport. Perfect for me. I don’t mind that I’m no longer at 100% now, I’ll do this one. It’s a perfect opportunity to try a chart type I’ve been planning, adapting a previous viz of the day from user Shivaraj. At this point, I realised it would take me a fair amount of time to set up and execute, so … I fell back on the wagon. Yes, I quickly did a late contribution for week 11 – it would be a shame to drop from the 100% completion rate for the sake of a simple slope graph. After all, it was only 6 data points to visualise!
A big thank you to Shivaraj for the inspiration, who himself drew it from a detailed blog post from Tableau guru Bora Beran – another great example of the openness of the data visualisation community and Tableau community in particular. Below is my March Madness visualisation, showing the progress of different seeds in the first and last year of competition (1985 and 2016).
A lot of recognition from those who enjoyed this viz, and probably a lot of shrugged shoulders from those who didn’t! The latter group probably included the Makeover Monday organisers, and that’s completely understandable, but I’m really happy with how it turned out (with apologies to those who thought that the round circular skin tones of the visualisation meant it resembled something a little risker, sorry about that! I suppose it’s a downside of doing so many circular visualisations!).
Free from the constraints of contributing every week, I fancied a go at week 13 – the “secrets of success” was an ugly representation of some questionable Russian survey data. I was pleased with my makeover but the community was non-plussed, however there was a lot more debate on how, or indeed whether, we should visualise data which at best left as many questions as it answered. I’m strongly of the opinion that Makeover Monday is a visualisation exercise only – if the data source is correctly attributed then it’s not up to us to critique and analyse the research, only to display it accurately and without errors. When the commentary around each of the projects focuses more around issues of data collection, research validity and shouting down the original data report (as opposed to the visualisation) then I might feel it’s a valid argument, but it’s a shame, and my interest fades slightly because that’s not what I want to be involved in. And so, by week 14 I re-affirmed my intentions of week 11, and had no intention of competing unless I could find an angle of learning or something to pique my interest.
Well, I found it – it was Marimekko week (for me, anyway). Here’s the visualisation I created but the main blog post on this can be found here:
Now, I try not to be competitive, and really I gain so much personal satisfaction from those visualisations I feel have gone well, and take lessons from those who do so much better than I do, particularly on the weeks where my entries are less inspiring. But I really fancied a shout-out in the weekly round-up here. Personal congratulations from three Zen masters and 40-odd likes for my tweeted contribution must count for something? No, still, nothing!
Now, I should add – I completely and utterly don’t mind! Yes, it’s a goal of mine to be included in a weekly round-up, and I’ll be disappointed if this doesn’t happen at least once this year. To see why my Marimekko didn’t get included, see the quality of competition I’m up against by reading the round-up from week 14 I’m biased, and might have included my contribution if it were up to me, but you can see the competition is phenomenal. But perhaps my goal to be included in the round-up at least once is why I’ve found it harder to let go than I thought! After all, the round-up with Andy or Eva’s expert commentary and advice, and the additional community feedback are newer, even better reasons to get involved in the project every week.
Deciding not to do this every week has increased my enjoyment and given me a little more freedom with my visualisation projects. Ironically the result is that I’ve still continued to produce something each week. I even contributed to week 15 by mistake – I didn’t particularly relate to the dataset or produce anything new, but before I knew it, I’d knuckled down, produced a nice analytical chart (nowhere near the standard of some of the visually stunning best) and enjoyed the contributions of the rest of the community.
Now, I don’t claim that I will do all 52 in 2017, nor am I angling for another T-shirt. But I can’t say enough about the benefits of the project, and, contribute this week or not, I’m sure I’ll be checking my twitter stream this Sunday afternoon secretly hoping it’s a nice interesting dataset. Perhaps the title of this post is wrong, and should read as follows:
Why do I continue to contribute to Makeover Monday every week?