UX South Africa Cape Town 2016

At the UX South Africa 2016 Conference in Cape Town I delivered a talk on the future of UX design in South Africa, and I facilitated a workshop on design critique. The UX South Africa Conference is in its third year and has grown from its base in Cape Town to an annual Johannesburg counterpart. The conference is growing in popularity and for the last two years international speakers has been added to the list. The popularity of the conference bears testimony to the growing local awareness of the value of UX design. The audience includes developers, designers, agilists, marketers, and content editors working across the tech industry.

My talk was inspired by conversations I’ve had in the past year with people interested in learning more about UX design. Themes running through the conversations were people’s desire to do meaningful work, and the future of UX design in South Africa. This raised two questions for me: Firstly, how can an industry with empathy at the core of its practice ignore the problems facing South Africa? And secondly, in a rapidly changing design landscape will UX designers be relevant in the future?

UX design exists at a unique interdisciplinary juncture and with the maturity of design thinking, social innovation, and lean startup, UX designers are well placed to re-apply their user-centred-design skills in new areas to make greater impact. In my talk I explored the new mindsets, skills and attitudes UX designers need to adopt to shift from merely doing design to becoming design activists.

Earlier in 2016 I gave a talk at a UX masterclass meet-up titled ‘The Lost Art of the Critique’. The talk was inspired by observing that many design teams that I work with lacked a process for discussing design critically. Feedback after the talk confirmed that many people experienced this in their organisations and they wanted to do something about it. So I designed a workshop to meet that need and presented the first version of workshop at the conference.

The best thing about doing talks and workshops is the hard questions that come from the audience. By exposing the canon of your knowledge in public, you get to grips with what you actually know, and how much more there is to learn.

Design principles for VR

I’ve been looking into VR for education recently and I am strangely intrigued. I find VR hard going on the senses, so I think its value lies in short bursts of usage. It’s not a long form medium. But there is something about the immersion that intrigues me. I’d like to find out more. The Cardboard Design Lab app gives the following best practices when designing for VR.

The Google Cardboard app.

  1. Reticule: use visual overlays to make targeting objects easier.
  2. UI depth and eye strain: shifting focus between near and far objects may cause eye strain, 3 metres away from the user results in best UI.
  3. Constant velocity: good motion is smooth with a constant velocity. Acceleration and deceleration may cause people to feel sick.
  4. Keep the user grounded: include reference points to that the user can understand their surroundings.
  5. Maintain head tracking: at least one element in the scene should retain head tracking. Smooth low latency head tracking is key for VR.
  6. Guiding with light: users are drawn to the lightest part of the scene. Use light to direct users.
  7. Leveraging scale: use scale to influence how the user perceives their size in a virtual world.
  8. Spatial audio: use audio to direct the attention of the user and to immerse them in the environment.
  9. Gaze cues: use gaze as a cursor to trigger passive interactions to reveal more information
  10. Make it beautiful: the better it looks the better the immersive experience. This includes good interaction design and audio.

The Fallacy of Active Versus Passive Data

Clayton Christensen’s new book, Competing Against Luck, lays out the Theory of Jobs to Be Done. It focuses on understanding customers’ struggle for progress and then creating the right solutions and experiences that solve customers’ jobs.

What I like about the Jobs to Be Done (JTB) model is that it avoids words like customer experience, service design, design thinking, agile, big data etc., which are often used in different contexts meaning different things to different people, although these practices are all implicit in JTB.

It shifts thinking from product features to customer value. It provides a model for organisations to become truly customer centric by focusing business functions on understanding and solving the struggles of customers by providing diverse teams with a language of integration.

Key to this shift is for organisations to rethink how they engage with data.

Passive data: the messy context of real life

Much of the information needed to make decisions about solving for a job is found in the context of the struggle. We call that “passive data” because it has no voice or clear structure or champion or agenda.

Making meaning out of the jumble of real-life experiences is not about tabulating data but about assembling the narrative that reveals the Job to Be Done.

Innovators have to immerse themselves in the messy context of real life to figure out what potentially successful new products might offer to customers.

Active data: the crips precision of a spreadsheet

We can predict, however, that, as soon as a Job to Be Done becomes a commercial product, the context-rich view of the job begins to recede as the active data of operations replaces and displaces the passive data of innovation. [emphasis mine]

Managers feel an understandable sense of reassurance when they shift their attention from the hazy contours of a story of struggle to the crisp precision of a spreadsheet.

Data is always an abstraction of reality based on underlying assumptions as to how to categorise the unstructured phenomena of the real world.

As data about operations broadcasts itself loudly and clearly, it’s all too easy–especially as the filtering layers of an organisation increase–for managers to start managing the numbers instead of the job. [emphasis mine]

Organisations are gathering a lot of data about customers, what they are doing. Getting insight from this data without knowing the why of people’s behaviour is hard. Which is why organisations don’t know what to do next. Getting out of the building and understanding the struggles of customers is the place to start. The next challenge is getting the entire organisation focused on the real life struggles of their customers.

The problems of rising complexity

I find George Monbiont’s analysis of current world events the most insightful. In Trump’s climate denial is just one of the forces that point towards war he refers to an article by Paul Arbair where Arbair notes that beyond a certain level of complexity economies become harder to sustain. I would suggest reading the article in full, but the main takeaways for me are (emphasis mine):

As complexity rises problems become more difficult to solve.

In addition to weighing on economic growth, biophysical constraints – in particular the various rising constraints on the energy supply – may also negatively impact societies’ capacity to innovate and to maintain their level of complexity. As shown by American anthropologist and historian Joseph Tainter, human societies can historically be conceived as problem-solving organisations, which tend to develop ever-greater organisational and technical complexity in order to solve the social, economic and political problems they are confronted with. As their complexity rises, the problems they have to deal with become more difficult to solve, requiring growing investment in further economic and societal complexity.

Innovation in industrialised societies become more expensive and less productive over time.

A key determinant of a society’s capacity to develop greater organisational and technical complexity, according to Joseph Tainter, is its capacity to harness ever-growing supplies of energy. The availability of abundant, inexpensive, high quality energy has indeed been historically instrumental in the development of industrial societies’ capacity to build increasing complexity into their economic, technical, political and social systems. As biophysical constraints on the quantity and quality of energy and other resources rise, this ‘energy-complexity spiral’ may transition from being an upward spiral to being a downward one. Technical innovation that increases productivity may slow down this evolution, but research shows that innovation in industrialised countries tends to become more expensive and less productive over time, meaning that societies need to continuously step up their investments in innovation to continue solving problems through building up increasing complexity.

We need to scale down expectations, because of the cost of rising complexity.

As there are growing signs that we might be in a crisis of complexity caused by rising biophysical constraints and characterised by diminishing returns of investments in societal complexity, we are entering an era when circumstances will trump personalities and institutions. What we now need, hence, is not so much to find new political ‘leaders’ capable of designing and enacting grand plans to lead us further up the complexity pathway, but to ensure that we can make collective choices that are fit and appropriate for an age of scaling-down expectations.

Decide for yourself how this links to what Stephen Hawking writes in This is the most dangerous time for our planet:

So taken together we are living in a world of widening, not diminishing, financial inequality, in which many people can see not just their standard of living, but their ability to earn a living at all, disappearing. It is no wonder then that they are searching for a new deal, which Trump and Brexit might have appeared to represent.

Thinking in Models

In The Personal MBA Josh Kaufman writes that:

Correcting your mental models can help you think about what you’re doing more clearly, which will help you make better decisions.

To make sense of the world we create models of it. Models are abstractions that help us think. No model is perfect. The only perfect model is the universe itself. So we don’t have to worry about our models being perfect, all models are wrong, but some models are more useful than others. That is the power of models – they can always be improved. And when we make them better, our thinking improves. Models can do a lot of work for us.

Kaufman continues quoting Charles T Munger:

I think it is undeniably true that the human brain works in models. The trick is to have your brain work better than the other person’s brain because it understands the most fundamental models-the ones that do the most work.

What you need is a latticework of mental models in your head. And, with that system, things gradually fit together in a way that enhances cognition.

I remember Clayton Christensen saying that when someone asks him a question he does not answer the question directly, instead, he looks for a theory or model that can be applied to the question. He then sees how the question holds up. (I recall this from memory, so if I have this wrong, please let me know.)

I’m experimenting with this approach. Often when presented with a question, looking for an answer is not the best approach. Applying a model to the question may produce a better question. When I can’t find a suitable model I’ll try to sketch one out on paper. I’ll evolve the model by showing it to people, iterating until it starts to simplify and communicate the situation better. In this way shifting the burden of work onto the model. My work then becomes to continually grow the latticework of models in my head and to practise them whenever I can.