Visualization in design thinking

Challenge  In order to design the solution, first we have to build a good understanding of the problem  that we are trying to solve. Interviews, observations, desk research and other research  techniques that are applied during this phase generate huge amount of data – data that has to  be grouped and analyze. Creating a research wall is a great way to display insights, photos,  and artifacts – all the relevant research outputs in one place. This approach helps us build  connections between different findings and understand the bigger picture. The visual aspect  of the wall triggers idea generation and enables project members to better understand  research insights. For this reason design thinkers often use tools such as persona which give a  face to the customer/interviewee.

Visual tools that can be used for understanding the problem are for e.g. personas, mind map,  customer journey maps, moodboard, foto safari.

Selection  In his book Change by Design, Isiah Strickland states that drawing for designers is a way to  express their ideas and only drawing “can simultaneously reveal both the functional  characteristics of an idea and its emotional content.” It is not necessary to poses good  drawing skills – as the popular saying goes “picture is worth a thousand words” and even a  simple sketch can tell us more than a 10 minutes description of an idea. In a group setting,  visualization helps to spark idea generation. Seeing what other people are drawing, adding  elements to their drawing or simply discussing the sketches helps to build on top of each  others ideas. Brainstorming sessions can be also enhanced by the use of visual props such as  nudge cards to stimulate idea creation.

Drawing can benefit our understanding of how the idea unfolds through different stages of  the service, particularly when generating ideas for a new service,

Visual tools that can be used in idea generation are for e.g. 10×10, storyboard, nudge cards

Visual tools that can be used for prototyping are for e.g. desktop walkthrough, cardboard  prototyping, paper prototyping, role playing.

Application  Citing Katja Tschimmel “in the same way that sketching helps the designer to think and  elaborate ideas, early prototyping is another way of visualizing and testing new solutions. It is  a visual manifestation of concepts, the transformation of an idea in a testable model”. In  another words, use of prototyping transforms ideas from drawings on a paper to tangible  solutions. This shift helps us explore different possibilities, compare how the customers  perceive prototypes, and improve solutions without investing a lot of money. Prototypes tend  to be “cheap and dirty”, which means that they are not a polished solution but instead a  working version of a service that is good enough to be understood by the customer. For this  reason, development of prototypes doesn’t require a lot of time and money. Isiah Strickland

summarizes it in a great way – “the faster we make our ideas tangible, the sooner we will be  able to evaluate them, refine them, and zero in on the best solution”.

Insight

It is unrealistic to expect that data visualization tools and techniques will unleash a barrage of  ready-made stories from datasets. There are no rules, no ‘protocol’ that will guarantee us a  story. Instead, I think it makes more sense to look for ‘insights’, which can be artfully woven  into stories in the hands of a good journalist.

Every new visualization is likely to give us some insights into our data. Some of those  insights might be already known (but perhaps not yet proven) while other insights might be  completely new or even surprising to us. Some new insights might mean the beginning of a  story, while others could just be the result of errors in the data, which are most likely to be  found by visualizing the data.

In order to make the finding of insights in data more effective, I find the following process  very helpful:

Approach

The scientific visualization tool used in my challenge is hard to find any visualization or  data wrangling technique that is not already built into R. Therefore better visualization tools  may be preferred for future development.

