In this blog, Alex Quenby explores what Design Thinking is and its place within the Data & Analytics industry.
In our first two months with Rockborne we have covered a range of both technical and non-technical topics, one of which was Design Thinking. As a graduate in Product Design, this is an area that I am very familiar with, albeit in a very different context to the world of data and analytics. I had never really thought of Design Thinking outside of Product Design, but I have slowly come to realise that it subconsciously impacts us all on a daily basis. For example, Harry Beck’s 1931 design for the London Underground map is probably, in my opinion, the best piece of Design Thinking to date, and it has subsequently been replicated across the globe. What made the design so effective was the disregard for irrelevant information, such as geographical proximity and topographical landmarks, and a focus purely on what was relevant – the different train lines and stations within them.
So, what exactly is Design Thinking?
It is an iterative, user-centred process put in place to solve complex problems through redefining what is required, in order to create solutions that may not necessarily be apparent at the outset. Design Thinking revolves around a desire to fully understand the user/client that will be benefitting from the output, and keeping their requirements and desires at the forefront of the entire process.
The Design Thinking process can be broken down into 5 steps as follows:
Empathise – understand your user and the challenges that they face. This will require research in order for you to fully immerse yourself in their perspective and appreciate the problem that you are trying to solve.
Define – take the insights you have gained from your research and create a problem statement that will address the core problem(s).
Ideate – come up with potential solutions to your problem(s). It is important to get as many ideas as possible during this phase before you later narrow down the options and choose your chosen final solution. In most cases, your final solution will likely be a combination of the best features from a selection of your initial ideas.
Prototype – turn your chosen solution into a typically low-cost and low-fidelity product, to help make your design more tangible.
Test – rigorously test your design with your client/user against the problem(s) identified earlier in the process. This will likely lead to further iterations of the design, until you have a solution that satisfies all parties.
Undoubtedly, the most important stage of the Design Thinking process is the first stage – Empathise. If you do not fully understand your user, then you will not be able to fully appreciate the problems that they face. This will result in you having a final solution that fails to solve the problem that it was supposed to address. I mentioned earlier that Design Thinking is a user-centred process, and the Empathise stage of the process completely encapsulates this, through providing that critical platform and point of reference throughout the design process.
So why is Design Thinking relevant to the world of data and analytics? Whether it be through database creation, the production of insights, or some simple trend analysis, we will be carrying out some steps of the process without even realising. But by fully understanding the process and its different stages, we can ensure that we are satisfying the wishes of any/all of our project stakeholders by keeping their perspective at the forefront of our approach. What was so great about this particular training, is we took our learnings from this to actually design a star schema, a visualisation, and a Rockborne Bag.
Where to next? Read our article exploring what is a virtual machine?