Design & SimulationJanuary 25, 2022

2022: When Data Science meets Design Thinking

The title of this blog site is “Navigate the Future.” The writing…
Avatar Randall Newton

The title of this blog site is “Navigate the Future.” The writing team here embraces the philosophy of that title as our North Star. We are intentionally forward thinking in our coverage of manufacturing technology and related issues, and we guide the reader in that direction.

Attempting to ape the spirit of such foresight, many pundits in the wider world of media are suggesting 2022 will be a recycled version of 2020. They point to two factors. First is that wily little virus which continues to take its cue from famous imposters and reinvent itself every few months. Second is a political climate that seems hellbent on extreme polarization.

I am not one who thinks 2022 will be “2020 too.” Instead, I believe 2022 will be a year when the challenges of the past two years will be addressed with hard-won wisdom gained from our collective experiences. Specifically for manufacturing, it will be a year when two very separate schools of information — Data and Design — realize they were meant for each other.

Data science uses the rational tools of algorithms and search to pull information from unstructured aggregations of data bits. Design thinking is a problem-solving process that unites observation with intuition and empathy to achieve innovative results.

High tech Yin and Yang

Alphabet (the company formerly known as Google) and Apple appear to be polar opposites as tech giants. Google is all about the math and the algorithms of data. Apple is revered as a “design first” company.  Yet these two companies can be the poster child of possibilities when seen as complementary opposites: Yin and Yang, Data and Design. For the end-user, the products of these two companies have been — and continue to be — complementary. Maps, YouTube, and Google Search are ubiquitous as the first three app downloads when someone buys a new iPhone or iPad.

The lesson here is not to suggest companies seek out an ideological opposite and hope for serendipitous advancement. Instead, they should internalize the success that comes from the marriage of data science and design thinking.

Let me quote Matthew Humphreys, who was named Chief Designer at a leading automotive design consultancy at the tender age of 21:

“The creative folks intuitively design what’s best for the user, while data folks provide great insights. The true unicorns are those who can go end-to-end designing, building, measuring, analyzing, and iterating with a combination of user intuition and deep analytics.” — Matthew Humphreys

There are three “top-level challenges” in uniting data and design thinking, says Valerie Pegon, a design and innovation strategist for Dassault Systemès. The first is company agility. For manufacturing, agility means “being able to simulate right away, in real-time. Or being able to test virtual experiences quickly.”

The second challenge is responding to the rise of that ocean of data possibilities we call the Internet of Things. “Sensing and data analytics enable a continuous feedback loop to improve new designs, to adapt in real-time.”

The third challenge is using social systems to inform design. The data is tougher to quantify but the rewards are worth the effort, Pegon says. “Building these ecosystems requires some level of structure to work smoothly, a high level of flexibility, and a deep connection to the context and usage.”

The human element

The marriage of data science and design thinking can optimize workflows and stimulate innovation.  It makes data “smart” by merging the human element into the data stream.

“Breakthrough innovation depends on the ability to create a link between things that seem unrelatable,” says Kristen Van den Bergh of additive manufacturing software and services company Materialise. “And while computers might process connections faster than humans, the human brain is able to create associations that wouldn’t naturally appear in a given dataset.” Those associations are at the heart of design thinking.

“Companies need to invest in human expertise as well as in machine intelligence,” Van den Bergh continues. “But they will still fail if they don’t invest in the third ingredient: a process to make those two successfully work together.”

Van den Bergh’s colleague at Materialise, Bart Van der Schueren, applies this notion of merging data and design thinking to additive manufacturing processes: “When an abundant amount of data is available, as is the case with additive manufacturing, data alone is not sufficient to automate production flows. Because even in such a data-rich context, the domain-specific knowledge of human experts is required to optimize the process before it makes sense to automate it. In other words, if you use a lot of data to automate and scale up a bad production process, you still end up with a bad process.”

If you are looking for competitive advantage in 2022, introduce your internal data science and design thinking teams to each other. Maybe lock them in a room until they come out with workflows and strategies that unite the best of what each has to offer.


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