Intro

There are multiple guardian masterclasses, on a variety of things that a newspaper thinks it is important. I am not clear on the distinction between a “masterclass” and a “lecture”; before covid19 I went to about 1 lecture per month on a technical domain where I had less experience. Many of these where focussed on particular product; but as long as this is understood at the start, so what? (talking about features of X, not marketing success of X, or growth of X, or sales contract of X). The mini-conference that I attended was on data visualisation; as that is currently approximately sixty percent of my responsibilities at work. Disclaimer; My current employer funded my attendance (but not the write up).
Please note my website is an engineering text; an objective list of boring details about how to achieve technical solutions. I have trimmed or minimised any content from the conference that doesn't meet this focus. This text covers things that I claim to have a fairly detailed understanding of; even when supported by little formal study.

TL;DR: New tools

  • New to me tools; many on this list are commercial SaaS, and so would have been outside my searches for solutions.
  • Xeno,
  • raw,
  • datawrapper,
  • flourish,
  • figma,

New material/ process

As a note on different professions, journalists do not start out with a fixed objective on some data; they start with a need to make story. As a second angle, an actual marketing campaign has more flexibility on delivery artefacts than I do; but must make the product seem useful/ cheap/ relevant/ sexy to potential customers. Therefore these are a different solution spaces to mine.
As a high level/ abstract discussion on process, a path through data may be very large scale, per nation, a century; or very small scale, a person, a single sport event. Journalists find most compelling results with small scale as humans relate to it better. Demographic trends are more important information though; for example: last year there was six million slow painful deaths from X disease, mostly people who drove a lot, or lived near busy main roads. The critical boundary is not observable on small scale data.
There was mention of colour. Personally, I say colour is hard; colour changes with culture, with socialisation, with age, with mood, with lighting conditions, with attitude, with gender (as a subset of culture), with output device (see mac based designer to pc based user in particular). Reference, my life; and trying to get people to understand my thoughts without writing long paragraphs. Read notes by E Meeks.
The lectures mentioned J Bertin, a french cartographer who published a book called “Sémiologie graphique”, which was influential. They did not talk long about different graph types, which makes sense, as “advanced graphing” would leave you with a small audience. They do mention that most people use histograms wrongly (most marketing people either forget or ignore that the area of the bar is part of the definition of the graph, and is consist when the graph is drawn correctly), which is unfortunate.
The lectures mention “designer responsibilities”, for communication, for clarity, for avoiding biases. I would like to send people to a book I bought recently, which covers the same themes (see last entry in reading list)
There was a definition for “intersectional” 1, aka poly-variable analysis. Not what I was expecting, but useful material. The reason this is used by women in the US is that there are many stats recorded in the USA, which only reference ~25% of the population (adult, white, men, CIS, over a certain wealth band).
As an internal process, clearly define preferred user journey; add signposting/ sigals to encourage people to follow this. Mentioned P Morville as an early practitioner, and the UX honey combe. Referencing work by D Kahneman, people will remember emotional states about a thing longer than any of the details.
One of the lectures mostly worked as a UX contractor; and states: Paper sketches are a useful process, as no-one will be attached to them. Do quick iterations to explore options, at the cost of minutes. For big projects likes personas, to try to avoid people working inside the project from forgetting the user. NDAs make user testing hard. Likes grid layouts.

My focus on “tell a story” is limited by regulation, or accounts process; and it is not expected that there will be many casual consumers. In my role, my value improvements are that client took less time or money to achieve the same result; so saved money; that a new client was able to engage is better management strategies, due to better access to information; or that my superior software made an integration of multiple datasets possible (and so one of the previous points). “Data is the new oil” is a catechism for some companies; my work, my profession is busy building better tools for extraction and delivery of new oil.

Appendix A: some visualisations

As an engineer on contract, I do not look at things “for inspiration” (aside from the requirements list). People more strongly aligned to arts culture, may wish to wish to look at data visualisation techniques used in the following:

NB: I am not making a political statement with the choice of data samples; I think the Guardian was following its normal portfolio.

Appendix B: extra reading

These are sites/texts recommended in the lectures, but not covered in them.

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