Bits and bobs from day 4 and 5 and lessons learned from week 1 UXDI at GA

1 done 9 to go. I must say I barely survived. Excitement, frustration and all in between.

What Worked Well: group collaboration was good, I received support by both instructors and student colleagues, willing to give up their time to brainstorm, test, give suggestions.

What did not work well: I’ve been exposed to 5 new software in the space of a few days: Axure, Balsamiq, Sketch, Lucidchart, POP, Canva, Google notes, maybe more. Right, so what should I do? Learn them all? Not even time to have a look … On top of that — out of the blue — we’ve been asked to publish a blog (on Tumblr, or medium … never used them before, not even keen on sharing with the world personal student experiences) to ask questions on User Stack Exchange (cannot even remember if that’s the right name). And this, besides the lectures, the assignments etc etc. Time management will become of paramount importance.

What I’d like to see more of: case studies, and more case studies. The lectures provide basic examples, disconnected from real life experience. If an assignment was preceded by a case study, we would be provided with so much more guidance instead of beating around the bush. Also I’d love to receive precise answers to precise questions … well I’ll give it a go to this Stack Exchange.

We had stand up meetings at the beginning of each day and that helped me comparing my experience with the rest of the class. A few take outs from my personal journey

1. I was able to manage my time well in terms of leaving enough time for building — and rehearsing — the presentation
2. I touched with hand the importance of defining the problem: a poorly defined project will lead to a poor solution, a well defined project will make the solution generation process easy (or was it chance only? Let’s see next week)

There are two points I reflected upon from day one, but still have not found a clear answer:

1. Personas: how to effectively build it, what to add what to leave out, how to bring it to life, how to maximise signal and minimise noise. When we all presented our assignment, everybody showed personas, which were generally very very detailed, to the point of exact age, exact suburb of residence, exact profession, extremely specific behaviour. So, are we going to build a solution for “Jack Rush (clearly invented name referring to the fact the person is time-poor), young professional, SINK, living in outer suburbs”; or rather “Jack Smith (real name), 28 yo accountant at Deloitte, single (his girlfriend dumped him last month) with a dog, living in Pakenham”. If the goal is to improve the customer experience for Metro Melbourne, do we care the person is 28 (instead of 29), that he is an accountant, that he works at Deloitte. Does that mean that if he works as a designer at Isobar the solution will not meet his needs? Who cares about his dog and girlfriend, and how does the fact he lives in Pakenham affect his needs compared to if he was living in any other outer sub? I found this good article about personas (http://www.uxbooth.com/articles/creating-personas/), but still my question does not have a precise answer.
2. Affinity Map: How to distill the essence to build an effective Affinity Map? How do you actually form themes by grouping related things together. I got to the stage where I could group issues or behaviours, but that is not quite a theme. I could not quite grasp how to go to the next step. I think it has to do with the very limited field research we could do (and what’s in this article partially explains it “You can also use affinity diagramming when you have a large amount of information — for example, at the end of a contextual enquiry, when you may have hundreds or even thousands of individual notes.”), but that does not change the outcome of being unclear on how to proceed. I’ll definitely read about it and look for best practices.
3. A nice TED talk about using research data in UX design: The complex relationship between data and design in UX. It’s about how to harness and leverage the power of that data.