Design Thinking Needs To Think Bigger

Design thinking is one of the most important ideas of the 21st century. The methodology’s impact on product design, how organizations go about solving problems, and how we live our everyday lives has been profound. And its influence has expanded far beyond business and design circles. Universities, nonprofits, even science labs run design sprints based on design-thinking principles. The most popular course at Stanford is one on how to approach your life as a design challenge. The concept is even taught at some elementary schools.

But it’s been 15 years—a generation—since David Kelley had his epiphany to stop calling Ideo’s approach “design” and start branding it as “design thinking.” And much has changed in that time.

Each day we now generate 2.5 quintillion bytes of data—from internet posts, mobile phone activity, internet-of-things sensors, purchase transactions, and more. So much data that over 90% of it in existence was created in just the last two years. Two years of Twitter tweets produce more words than are contained in all the books ever printed, combined. At about the same time that Kelley’s “design thinking” lightbulb was going off, in 2002 a full human genome sequence cost $100 million. These days it can be done for $1,000. And by 2020 it’ll cost less than a movie ticket.Not only is the world more infinitely complex than at the turn of the century, it is also profoundly more intertwined. We are halfway to connecting everyone on the planet, with 3.7 billion internet users worldwide. In the U.S., 99% of 18- to 29-year-olds use the internet. Smartphones have become ubiquitous: roughly half the world’s adult population owns one, and it’s projected that by 2020 the figure will climb to 80%. WhatsApp was founded less than a decade ago, but now traffics in 10 billion more messages a day than the SMS global text-messaging system.

We live in a massively complex, intricately interconnected global system. And it’s increasingly impossible to be designers (or human beings) without taking into account how we affect and are, in turn, affected by all the moving pieces of this organic machine. “The more complex an organism is,” says artist and teacher Adam Wolpert, “the more capable it becomes. And the more capable it is, the more it can address challenges and seize opportunities. The downside of that is, the more complex it becomes, the more vulnerable it becomes.” The challenge for designers is learning how to balance the production of evermore complex capability against the threat of a resultant breakdown. That’s why I think design thinking, which emphasizes solving problems holistically, needs to look at a bigger whole by incorporating another body of thought: systems thinking.

WHAT IS SYSTEMS THINKING?

Systems thinking isn’t new—though it may be unfamiliar to many designers. It’s a mode of analysis that’s been around for decades. But it has newfound relevance for today’s everything-is-networked, Big Data world. Systems thinking is a mind-set—a way of seeing and talking about reality that recognizes the interrelatedness of things. System thinking sees collections of interdependent components as a set of relationships and consequences that are at least as important as the individual components themselves. It emphasizes the emergent properties of the whole that neither arise directly, nor are predictable, from the properties of the parts.

Systems thinking can be used to explain and understand everything from inventory changes in a supply chain, to populations of bacteria and their hosts, to the instability in Syria, to the seemingly irrational behavior of certain elected officials. The vocabulary of formal systems thinking is one of causal loops, unintended consequences, emergence, and system dynamics. Practicing systems theorists employ tools such as systemigrams, archetypes, stock and flow diagrams, interpretive structural modeling, and systemic root cause analysis—all of which is beyond the scope of this post. For the purposes of this discussion, I’ll simply introduce the Iceberg Model and briefly discuss two key concepts in systems thinking—emergence and leverage points.

The Iceberg Model is a helpful way to explain the concerns that drive systems thinking. Events are at the top of the iceberg. They’re incidents that we encounter from day to day—the hurly-burly of life. Patterns are the accumulated habits or behavioral “memories” that result from repeated, unconsidered reaction to events. Systemic structures are how the components of the system are organized. These structures generate the patterns and events that confront us. Mental models are the assumptions we have about how the world works; they give birth to systemic structures. Values are the vision we have for our future—what we aspire to. They’re the basis for our mental models.

Mostly we live at the level of events, because it’s easier to notice events than it is to discern hidden patterns and systemic structures. Even though it’s underlying systems that are actually driving the events we’re captive to. It’s there, at the tip of the iceberg, that we expend most of our energies and attention, and like the Titanic, it’s there that we run aground because we don’t see the truth of the problem, the variables and influences lying below the surface. We take actions without understanding the impact of those actions on the system, making the situation worse.

As an apocryphal illustration, let’s say, your favorite fancy coffeehouse (or Philz, for those of us in the Bay Area) serves you an anomalously bad cup of joe. That would be an event. A pattern would be noticing there’s a higher frequency of bad coffees produced during shift changes from morning to afternoon to evening barista staff. Perhaps the systemic structure generating this pattern of unpalatable coffees is that the shift changes are scheduled so as there’s no overlap between the incoming and outgoing teams of baristas.

The mental model that the baristas hold leads them to believe that they’re only responsible for the mochas and lattes that they make, not the team after them. And say, the value that drives that belief is one of competition—of wanting to make better coffees than the other shifts, and therefore not being concerned about the pour-over apparatus being properly cleaned, or the beans correctly ground and apportioned, at the end of a shift.

It’s usually the case that moral character or human error are blamed for what are really system failures. The people who made the mistakes—the “bad apples”—need to be reprimanded, retrained, or fired. Out with the offending baristas! But systems thinkers understand that these are symptoms and not causes. Systems-savvy designers will know the real answer is to unearth what patterns or assumptions are generating those suboptimal behaviors—the bad containers, as Stanford psychologist Philip Zimbardo puts it, rather than bad apples. Not just what happened and when, but how and why these things happened. In the apocryphal example, maybe the solution is to stagger the staff turnovers, so that baristas from earlier and later shifts are always intermingled.

HOW TO UNDERSTAND SYSTEMS THINKING

There are two key concepts to understanding systems thinking. The first is emergence. What makes a system a system rather than just a collection of parts is that the components are interconnected and interdependent. Their interconnectedness creates feedback loops, which change the behavior of the system—in fact, they define the behavior of the system. Emergent properties arise that exist only in the system as a totality, and not in its disparate components, making it impossible to understand the system without looking at the whole.

You can’t understand how we get to an anthill by looking at a single antenna or thorax. A Tesla driving down Highway 280 is an emergent property of the innumerable parts that go into making the car—as well as the national grid of recharging stations that had to be built and the web of regulatory oversight that needed to be navigated. In the inextricably connected world we live in, it’s no longer possible or wise to solve for the part without due consideration of the sum of the parts.

So how are designers supposed to address this onslaught of socioeconomic, techno-political complexity? I think the trick is to analyze systems with an eye toward finding leverage points—the second key concept in systems thinking. Rather than attempt to design a wholly new, perfect solution, oftentimes it’s better to find areas where an incremental change will lead to significant renovation in the system. The smallest nudge for the biggest effect.

“Everything is networked now,” says Pinterest cofounder Evan Sharp, whom I interviewed for my project on designer founders. “All of culture, all of communications, it all is going through networks.” Therefore, at the scale of 7 billion people, “any small, little improvement you make has massive aggregate value.” This will cut against the grain of most designers’ instincts, because the end result will likely be far from an ideal proposed design, but designing for the real world means dealing with the practical constraints of that reality and trying to make refinements in the face of compromise.

Now, I don’t want to oversell systems thinking. It’s not always possible in real-world cases to reasonably model very complex systems in ways that lead to good design strategies and outcomes. Systems thinking will also be novel and perhaps somewhat jarring to many designers, because as designers we’re usually laser-focused on a single, discrete design problem. But when appropriate, applying a systems mind-set to design thinking will give designer founders a powerful tool for circumnavigating the problems of the age. Focus on relationships over parts; recognize that systems exhibit self-organization and emergent behaviors; analyze the dynamic nature of systems in order to understand and influence the complex societal, technological, and economic ecosystem in which you and your organization operate.

The challenge is to rise above the distraction of the details and widen your field of vision. Try to see the whole world at once and make sense of it. It’s a heady challenge, but you either design the system or you get designed by the system.