Seven Thinking Models

I often get asked to talk about design thinking, or running 'design thinking workshops'. This post is a reaction to the overuse of design thinking in business.

You hear a lot about design thinking now, and it’s often used as the answer to almost every problem. And it’s often translated to any kind of creative workshopping in conference rooms/ zoom calls. But of course, design thinking is not equal to workshops. And it is necessary but not sufficient for a lot of problem-solving scenarios. Here are 7 different kinds of thinking required for most organisations, especially in digital and innovation work. 

(1) Design thinking - empathy led activity, understanding the end-user(s) contextually, and through observation and interaction, in order to create unique insights which can shape the product being built. Joseph Friedman invented the bendy straw after watching his daughter struggle with the height of regular straws, in the 1930s. (Note, he didn't need to run workshops. And he didn't need post-its!).

(2) Engineering thinking - the ability to deliver outcomes through the use of materials or software, in a manner that is robust and exploits the properties of the materials, and inputs. Lego spent almost a decade perfecting the interlocking mechanism of its plastic bricks in the 1950s to meet the founder’s insight that they needed to be snapped together and pulled apart by a 6-year-old child. 

(3) Scientific thinking: recognising the need to question even things we believe to be fundamentally true when presented with new data. The ability to construct hypotheses and test empirically to create entirely new truths, substances, or products. The view of the human body and medicine has evolved significantly over time, but at each stage, doctors and scientists believe what they currently know to be true. As true of the rudimentary understanding of diseases a thousand years ago, as it is of microbial and genetic analyses today.

(4) Data thinking: learning to use the properties of data which include externalities, and aggregation, and to consistently design products and solutions keeping the data inputs and outputs in mind. Also the culture of evaluating the data even when we think we intuitively know the answer. The smart thermometer Kinsa publishes ‘Healthweather’ maps of the US with anonymised readings of temperature recordings taken across the country which is a useful insight to epidemiologists. 

(5) Systems thinking: considering all the components of an ecosystem while designing and creating products and solutions, and taking into consideration all the interactions between the various components. When Edison created the workable lightbulb, he also realised it was useless without people having access to electricity at home. He also set up the worlds first commercial power plant - the Pearl Street Power Station in Manhattan. 

(6) Philosophical thinking: the ability to ask and answer ‘why’ and not just ‘how’, and to bring a subjective view of right and wrong, especially from a societal perspective. Sir Tim Berners Lee’s decision to open-source HTML and the hypertext protocol is foundational to everything that has happened on the internet.

(7) Agile thinking: the ability to start acting without perfect information, the willingness to start small and improve with feedback, the wisdom to identify the minimum viable features of a product/ solution and maturity to change direction based on emerging data. Amazon was a website for one product with largely manual fulfilment on day 1. This is also the principle of 'strong opinions, weakly held'. Which is a very useful mental model to live by.

What other kinds of thinking are critical in your work in innovation and digital today?