Recently, I had the opportunity to finish Stanford SCPD’s XINE 217 “Empathize and Prototype” course, as part of the Stanford Innovation and Entrepreneurship Certificate, which emphasizes the use of design thinking ideas to develop product and solution ideas. It is during this course, that I wrote down a few ideas around the use of data in improving design decisions. Design thinking is a modern approach to system and product design which puts the customers and their interactions at the center of the design process. The design process has been characterized over decades by many scholars and practitioners in diverse ways, but a few aspects are perhaps unchanged. Three of these are as follows:

1. The essential nature of design processes is to be iterative, and to constantly evolve over time
2. The design process always oversimplifies a problem – and introduces side effects into the customer-product or customer-process interactions
3. The design process is only as good as the diversity of ideas we use for “flaring” and “focusing” (which roughly translate to “exploring ideas” and “choosing few out of many ideas” respectively).

Overall, the essential idea conveyed in the design thinking process as explained in XINE 217, is “Empathize and Prototype” – and that phrase conveys a sense of deep customer understanding and focus. Coming to the process of integrating data into the design process – by no means is this idea new, since engineers starting from Genichi Taguchi, and perhaps even engineers a generation before Taguchi, have been developing systems models of processes or products in their designs. These systems models are modeled as factor-response models at some level, because they are converted to prototypes via parameter models and tolerance design processes.

Statistically speaking, these are analogues of the overall designed experiment practice, where a range of parameter variables may be considered as factors to a response, and are together modeled as orthogonal arrays. There’s more detail here.

Although described above in a simplified way, data-driven design approaches, grouped under the broad gamut of “statistical engineering” are used in one or other form to validate designs of mechanical and electrical systems in well-known manufacturing organizations. However, when you look at the design thinking processes in specific ways, the benefits of data science techniques at certain stages become apparent.

The design thinking process could perhaps be summarised as follows:

1. Observe, empathise and understand the customer’s behaviour or interaction
2. Develop theories about their behaviour, including those that account for motivations – spoken and unspoken aspects of their behaviour, explicit and implicit needs, and the like
3. Based on these theories, develop a slew of potential solutions that could address the problem they face (“flare”)
4. Qualify some of these solutions based on various kinds of criteria (feasibility, scope, technology, cost, to name some) (“focus”)
5. Arrive at a prototype, which can then be developed into a product idea

While this summary of the design thinking approach may appear very generic and rudimentary, it may be applicable to a wide range of situations, and is therefore worth considering. More involved versions of this same process could take on different levels of detail, whether domain-specific detail, or process-wise rich. They could also add more fine-grained steps, to enable the designer to “flare” and “focus” better. As I’ve discussed in a post on using principles of agility in doing data science, it is also possible to iterate the “focus” and “flare” steps, to get better and better results.

Looking more closely at this five-step process, we can identify some ways in which data science tools or methods may be used in it:

1. Observing consumer behaviour and interactions, and understanding them, has become a science unto itself, and with the advent of video instrumentation, accelerometers and behavioural analysis, a number of activities in this first step of the design thinking process can be improved, merely by better instrumentation and measurement. I’ve stressed the importance of measurement on this blog before – for one, fewer samples of useful data can be more valuable for building certain kinds of models. The capabilities of new sensors also make it possible to expand the kinds of data collected.
2. Developing theories of behaviour (hypotheses) may be validated using various Bayesian (or even Frequentist) methods of data science. As more and more data gets collected, our understanding of the consumer’s behaviour can be updated, and Bayesian behavioural models could help us validate such hypotheses as a result.
3. In steps 3 and 4 of the design thinking process I’ve outlined above, the “focusing and flaring” routine, is at one level, the core experimental design practice described by statistical pioneers including Taguchi. Using some of the tools of data science, such as significance testing, effect size determination and factor-response modeling, we could come up with interesting designs and validate them based on relevant factors.
4. Finally, the process of prototyping and development would involve a verification and validation step, which tends to be data-intensive. From reliability and durability models (based on Frequentist statistics and PDF/CDF functions), to key life testing and analysis of data in that context, there are numerous tools in the data science toolbox, that could potentially be used to improve the prototyping process.

I realize that a short blog post such as this one is probably too short to explore this broad an intersection between the two domains of design thinking and data science – there’s the added matter of exploring work already done in the space, in research and industry. The intersection of these two spaces lends itself to much discussion, and I will cover related ideas in future posts.