The Value Of Timeboxing Your Makeover Monday
At this stage I figure I don’t need to explain Makeover Monday to anyone. If it turns out you aren’t familiar, then the best place to start is at makeovermonday.co.uk.
This post follows up on attending Makeover Monday LIVE again, this time at the new home of the Data School, among the new and future cohorts and various other enthusiasts who came along for the fun. I think this event counted as my fifth live Makeover Monday (one with DS5, twice at Tableau Conference in the US and also one at London TUG). On top of that, at my workplace we have occasionally done them in a group as well, so perhaps ten times in total.
I think that over time I’ve probably only “finished” and published around half the Makeover Monday vizzes that I’ve started. Bad habits are hard to kick – I’ve been all too willing to give up, even failing to publish things that I’ve spent hours upon. Yet, when doing it live, and with the formal time constraint of an hour, I’ve always completed a viz, on time and published it. Why?
The Value Of Time Constraints
Timeboxing gives you focus, and an objective: to produce something that is complete by a deadline. While I’m not great with open-ended projects, I generally work well to a deadline. But having an hour to produce something is a good simulator for a work-based problem. A colleague or superior might have a presentation they need to give in an hour and want to show some fresh insight to an audience. Go!
The objective of having something, anything, that is viable can be quite empowering sometimes, and lead you down some interesting paths. My timeboxed visuals have often been more simplistic. Simple is good if it’s also clear. Further, by forcing yourself to complete something, there is a satisfaction in completion, and there are also always lessons to take away. Let’s look at my viz from this event as an example:
I spent a good deal of time in the first 15 minutes of this session wrangling the data. I was burning time and needed to focus my approach. Find a story and work with it. I created a very basic KPI for bus reliability, which I could see varied over time. I knew that there were equivalent metrics for Rail, and could comfortably put them side-by-side – if there was a story to tell I was going to find it.
Necessity Is The Mother Of Invention
This all came in the same week that Sophie Sparkes produced an amazing quantified-self style viz of her interactions at TC18, and shortly after Georgia Lupi and Stefanie Posavec’s book was published, a copy of which I was kindly gifted recently. Hashing these two concepts together, I worked up the idea of a commuter’s experience of the DC Metro system, and then extended it by crafting it into a series of diary-style entries.
This is absolutely not my normal style, and it’s certainly not meant to be a particularly serious piece of visualisation. But having the time constraint forced me to think more laterally. And as a result I tried something new, perhaps something I’d never otherwise have tried. And I learned a lot, too.
Moreover, having actually completed the exercise I could also critically reflect upon my work. Were it unfinished, I’d not have had that opportunity in the same way. With around 10 minutes to go I’d stumbled upon an idea to improve the visual style by having a single row of boxes, colour-coded, rather than sparklines. However, I also had some formatting to complete, and had to pick between them. But, now I have an idea of how to iterate the work, and also will be more likely to apply this better approach in future work.
If You Complete, You Can Share And You Can Show
Taking part in Makeover Monday live gives an opportunity to practice another skill – presenting. I used to absolutely hate the idea of presenting. Mainly because I’d not had enough opportunities to. These days I enjoy it. I don’t do it very well, but in time I hope I will, with sufficient practice.
At this event perhaps 20 different attendees each presented, each of whom took one additional step towards honing their own Tableau and presenting skills.
— Sarah Bartlett (@sarahlovesdata) November 8, 2018
As always, grateful to Andy Kriebel and Eva Murray for coordinating, and continuing to give up their time in the pursuit of developing other people. And thanks also to future Data Schooler Aude, who helped steer me through some problems and acted as a great sounding board when I hit a fork in the road.
Thanks to Sarah Bartlett for the featured image she tweeted.