I got to see renowned authors Malcolm Gladwell and Steven Levitt talk about The Future: Disrupted and Reimagined. What a treat! I found both men to be articulate and humble storytellers. Steven Levitt was particularly self-deprecating and funny.
My aim was to look for things that we, as researchers and designers, could learn from the way economists think – or more specifically, from the way these two people think.
Here are 7 things I learned.
1. Build statistical literacy, understand the language of probability, and understand how risk works.
This was the key learning for me, in terms of how to strengthen our skills; and is something I will explore with our team.
2. Analysing data and economic models only takes you so far; at some point you need to take a more human and qualitative approach.
Yes! I was delighted to hear both Steven and Malcolm talk about this. To generate meaningful insights – and in turn, innovative ideas – you need to look beyond quantitative data, observe real human behaviour in context, and use human judgement to make sense of (create hypotheses about) what’s really going on.
3. Malcolm Gladwell has a macro view of economic and human behaviour. He looks at big problems and links ideas together to explain what’s happening in our world today.
Malcolm would make an incredible design researcher because he is a master of sense-making and storytelling. The ability to craft an insightful story about a complex situation is exactly what we aim for during the Understand phase of our projects.
Malcolm reflected on his previous work with an air of embarrassment. “Looking back at my work is like listening to Duran Duran – it’s like, what are they singing about and look at that hair”. Malcolm seemed somewhat perplexed about the impact he has had on people all over the world – which I find rather ironic given the concept of contagions and tipping points discussed in his early books (Tipping Point, Outliers, Blink).
4. Steven Levitt has a micro view of economic and human behaviour. He looks for patterns in data to understand very specific problems.
He shared stories about using banking data to identify terrorists; and analysing the financial records of drug dealers and prostitutes to understand pricing strategy. Lesson here: meaningful insights come from connecting things that seem unrelated, or even undesirable.
5. The weak link vs. strong link paradigm
Is it better to improve our strongest links or improve our weakest links?
Should we invest in making the strong and powerful stronger? Or should we focus on raising the baseline level of competency for our weakest links?
Malcolm says, and I concur, it depends on the context; however, we would do better as a society if we focused on improving our weakest links.
Malcolm used sport analogies to explain his thinking. I love this because there are a lot of parallels between managing a business and managing a sports team.
Soccer is a ‘weak link’ sport. The team is only as good as its weakest player. Health care is a weak link environment. Mistakes are costly. In a weak link environment, the baseline level of competency needs to be high.
Basketball is a ‘strong link’ sport. You only need 2-3 superstars to succeed. The New Yorker magazine is a strong link organisation. It only needs 2-3 strong articles in each edition. In a strong link environment, the baseline level of competency doesn’t matter as much.
Conclusion: It’s better to improve our weakest links.
I, like many of my peers and colleagues, am drawn towards improving our weakest links. I sat there listening to Malcolm’s views and felt so proud of the work we do at Meld Studios. We design little things and big things that improve everyday life for people as they interact with the world around them.
This doesn’t mean we avoid the big, strong and powerful players. We work with a huge range of organisations; and we do so with purpose. What better way to improve financial literacy for all Australians than to work with the big players in our financial services sector? What better way to improve access to education, public transport, health care and social services than to work with big government departments and institutions to ensure the diverse needs of all citizens are taken into account?
6. Problem solving: Mysteries vs. puzzles
Solving simple problems is like solving a puzzle. You can collect data, analyse it and fit findings into a neat structure. Simple problems are relatively contained, with clear solutions.
The challenges we face today, and that we try to solve for in our work at Meld Studios, are complex. We face systemic challenges that are multifaceted and ambiguous. These problems are mysteries not puzzles. Mysteries cannot be solved merely by collecting and analysing data. Solving a mystery requires observation in real context, experimentation, sense-making and human judgement.
You can read more about the complexity of problems we solve for in this post by Oliver Dykes. And this post by Kimberley Crofts talks about observation, experimentation and judgement when designing visitor experiences in the GLAM sector (galleries, libraries, archives, museums).
7. Experiment and observe
Both speakers talked about experiments and observation as a way to understand problems, test ideas and generate meaningful insights. Again, yes! This is what we aim for in the Explore phase of our projects.
Malcolm took this idea further (as he does) and spoke about replacing traditional education and recruitment models – i.e. a 3-year degree, exams, grades, CVs, interviews – with an experiment and observation model. Our traditional model for training teachers does not guarantee a good quality teacher or learning experience in the classroom. Our traditional model for hiring staff does not guarantee success for the individual, team or business. So what could we do differently?
Malcolm proposed: what if, instead of studying for 3 years or going through a lengthy recruitment process, we just start doing the job? We experiment and learn along the way. Professors or managers observe, guide, and assess “students” based on fit rather than competence. If, at the end of the experiment, the “student” doesn’t fit the role, or the role doesn’t fit the student, they switch to something else.
As someone responsible for people – recruitment, internships, staff wellbeing, professional development – I am fascinated by this idea.
So what’s next? My fellow Meldsters and industry friends, I pose 3 questions:
1) If economists and data scientists see value in taking a human-centred, common sense approach to problem solving, then how might we, as human-centred folk, benefit from taking a more data-centric, data modelling approach?
2) How might we create better ways of coaching people towards work that fits their nature? And how might we celebrate quitting when something doesn’t fit?
3) How might we do more work to improve life for people in ‘weak link’ environments?