The March version of the #DataVizChat happened a few days ago, this time the time was convenient for APAC and the US, not so much for Europe and India. Next time I plan to do it in the evening in NZ, which means all of APAC and the EU should be able to join.
Introduction
After a slow start around 15 people joined, mostly from the US. From what I can see only 2 people from Australia and New Zealand joined. This time I didn’t ask for a data related introduction question, which I think I will do again next time, as it opens up the discussion right away. I only asked which job their childhood self would have chosen and we most of the answers were quite predictable, although some of them had a slight twist. Michelle was a bit more specific than “Astronaut” and if she wasn’t afraid of flying she would surely be in space by now and Pamela did want to become a teacher and actually ended up teaching people although probably not in a way she would have imagined as a child.
Discussion
The chat was started by checking if people consider themselves “creative”. I did not anticipate the can of worms I opened there. Some great discussions started by people who considered themselves at every level between “not creative” and “very creative”.
But very quickly we had to realise that “creativity” is a very vague concept, especially when combining it with “imagination” and “being artistic”. In a few independent discussions the fact that creativity is not necessarily limited to “artistic output” but also includes the creative approach of technical problems and analytical thinking in order to even define a problem came up. Also the importance of “recipes” (ie. best practise) was mentioned a few times. Even if you don’t consider yourself creative, just by exposing yourself to other people’s work and learning about basic rules about what works and what doesn’t can work around a (perceived) lack of creativity.
Kat followed with a question about common misconceptions around data and visualisation. Everybody can relate to this question and so did the other participants.
Summary
For me it was great to get so much different input from people with very different backgrounds. It’s good to see everybody engaging and exchanging ideas and tips how approach problems and progress in data visualisation. It’s also good to see that people continue to participate in casual discussions which outside of this often only happen coincidentally. It’s also great to see that people get to know each other outside of the dataviz bubble, particularly the very long discussion about quilting and combining the two hobbies with this idea:
For next time I will try to lead with a few different questions, as I think this will give more people the chance to chime in.