Making Sense of the World
Sensemaking is a method of practical wisdom grounded in the humanities. We can think of sensemaking as the exact opposite of algorithmic thinking: it is entirely situated in the concrete, while algorithmic thinking exists in a no-man’s-land of information stripped of its specificity. Algorithmic thinking can go wide—processing trillions of terabytes of data per second—but only sensemaking can go deep.
We can trace sensemaking’s roots back to Aristotle, the Greek philosopher, who described practical wisdom as phronesis. When a highly skilled person exhibits phronesis, it’s not just that they possess knowledge of abstract principles and rules. Phronesis is an artful synthesis of both knowledge and experience.
In the business world, we see phronesis when skilled traders perform in concert with market conditions and when experienced corporate managers sense subtle changes in organizations involving tens of thousands of people. When a legislative reform is implemented, a politician exhibiting phronesis can envision the chain of events that will play out in every domain of his or her constituency all at once. Many masterful leaders—knowledgeable and experienced—describe systems, societies, and organizations as an extension of their body. It is a part of them and they are a part of it.
How do these people achieve such extraordinary results? Though there is no shortcut to this work—no five-step plan, “life hack,” or killer PowerPoint to automatically get us there—there are some basic principles that can help all of us stay open to the insights that matter most. These principles are based upon the wealth of theories and methodologies that make up the humanities.
The Five Principles of Sensemaking
- Culture—not individuals
- Thick data—not just thin data
- The savannah—not the zoo
- Creativity—not manufacturing
- The North Star—not the GPS
- Culture—Not Individuals
When we practice sensemaking, we stop seeing a room as a space filled with individual items and we start seeing the structures that form a cultural reality. In algorithmic thinking, a bottle of perfume is defined by how many milligrams of liquid exist within it; a pen is a piece of plastic with metal attached to it. In contrast, sensemaking perceives everything in relationship to everything else. The perfume becomes equipment—along with lipstick, high heels, and text messages—in the world of dating. The pen, along with a word processor and paper and books, is part of the world of writing. Pens, perfume, hammers, word processors: everything in our lives has some bearing on everything else. Nothing exists in an individual vacuum.
Although philosophers have been describing this concept for close to a century now, it is too often forgotten or disregarded in our modern world. In realms where quantitative analysis reigns supreme—corporations and financial firms come to mind, as do, increasingly, education and health institutions—these notions of shared worlds and background practices are radical. Just think about the way companies or political campaigns try to understand markets and voters: they ask people what they think. In a focus group or a survey, they take people out of the context of their regular lives and pepper them with questions about discrete ideas, products, or policy ideas. By decontextualizing experiences—pulling worlds apart in an attempt to create an assembly of facts—they miss almost everything that can shed light on human behavior. This is why their conclusions are wrong most of the time.
The concept of “culture—not individuals” serves as an essential corrective to the widely held belief that human behavior is based on individual choices, preferences, and logical structures.
- Thick Data— Not Just Thin Data
If thin data seeks to understand us based on what we do, thick data seeks to understand us in terms of how we relate to the many different worlds we inhabit. This is precisely why moods are one of our most salient forms of thick data. For example, we can agree, between us, that the mood in the office is dull or the party is just getting started. We can know what it is like to be caught up in the excitement of a sports game or the fervor of a political demonstration. We can all feel the sadness of a cultural moment—“Where were you on 9/11?”—as well as the infectious joy that takes hold when we hear about a courageous act on the news. If our colleague tells us that she feels the organization isn’t ready for change right now—There’s too much stress around here—we nod our heads and agree. When we are attuned to this kind of data, we can sense the subtle but constantly evolving changes of the worlds all around us.
Why is this attunement such an imperative? After all, isn’t this what market research and technical reports are for? Despite what people in power assume, leaders and key strategic thinkers are almost always surrounded by layers and layers of abstraction.
Simply put, the imaginations and intuitions of top leaders are starving. They have been living on a diet of desiccated facts and figures—thin data stripped of all its organic life. This diet may sustain them through periods of relative stability, but they will likely be headed off course when markets shift. In the midst of changing circumstances, it is vital to reconnect with the emotional—even the visceral—context of humanity. This is where thick data comes in.
- The Savannah— Not the Zoo
Where do we go to get more thick data? We must start by studying humanity in the full complexity and beauty of the lived world. This is the basis of a philosophical method we will discover called “phenomenology,” or the study of human experiences. With phenomenology, we are observing human behavior as it exists in social contexts, not in abstract numbers. It’s the difference between watching a pack of lions hunt on the actual savannah and seeing them get fed from a bowl in the zoo. The lions are technically eating in both scenarios. Which one do you think holds more truth?
Take the question of love as an example. In 2012, “what is love” was the most searched query on Google. Helen Fisher, a biological anthropologist, offered an answer that received a significant number of hits. Based on fMRI results, Fisher and her colleagues concluded that “romantic love” is not an emotion, but a motivation system—an involuntary chemical reaction. We love because it incentivizes us to engage in relationships with potential mates.
That is what love looks like in the laboratory or the zoo, but Fisher’s explanation tells us nothing about how we experience love. Historians tell us that romantic love is really only a recent phenomenon. In ancient India, love was seen as dangerously disruptive to social structures, and during the Middle Ages, love was considered akin to insanity. What is love today? Thousands of divorce lawyers would argue against Fischer’s explanation of a motivation system. Insights about how love works are only possible by observing what people do and experience in the real world.
- Creativity— Not Manufacturing
After spending time in the field and engaging with the humanities to better understand the world, how do we achieve actual insights through sensemaking? In which situations is it okay to use a hypothesis and test it out? When is it better not to have any preconceived notions at all? These are different ways of reasoning through a problem: a concern at the center of a centuries-old debate about the scientific method. In the late 1800s, American philosopher and logician Charles Sanders Peirce became famous for defining the three kinds of reasoning we use to solve problems—deduction, induction, and abduction—each one appropriate for different levels of certainty.
Deduction is often called top-down reasoning because it starts with a more general law or theory—a hypothesis—and then attempts to apply it in specific instances. “All women are mortal. Sally is a woman.” Therefore we can deduce: “Sally is mortal.” Deductive reasoning is useful for constrained problems with set boundaries, but it is unable to incorporate new information.
Inductive reasoning, on the other hand, is the exact opposite of deductive reasoning. It is bottom-up, so it starts with specific observations and then moves up into a theory.
- The North Star— Not the GPS
We seem to live in an era of unprecedented complexity. Our world tells us that the pace of the seismic change occurring around us has rendered us incapable of seeing the big picture. Whether we are in the TV industry, attempting to navigate the emergence of streaming content providers like Amazon, Hulu, and Netflix, or medical practitioners barraged by an unending stream of often contradictory health studies, it is easy to accept these claims of overwhelming complexity. We want to throw up our hands and turn to the machines all around us: surely big data and algorithmic programming can make some sense of all of this. We, as humans, no longer can.
Today’s world feels overwhelmingly complex because we are obsessed with organizing it as an assembly of facts. Big data makes us feel as though we can and should know everything there is to know on earth. But this is a fool’s quest, and it leaves everyone involved feeling depleted and lost. We are so fixated on staring at the oracle of the GPS that we have lost all sensitivity to the stars shining right above our heads. The tools of navigation have always been available to all of us. But we must take responsibility for interpreting them. This means executives need to be prepared to understand new and unfamiliar contexts—political, technological, cultural—and to interpret their place in our increasingly interdependent world.
Therein lies sensemaking’s greatest offering: it teaches us two essential things about leadership in an era of big data. To begin with, sensemaking can guide us in selecting an appropriate context for data collection. After all, the mere task of collecting data is meaningless in the abstract. What data do we collect? What for? How? It is impossible to study the world without some sort of paradigm for thinking about what you want to study.
Secondly, sensemaking shows us how to cultivate a perspective on how data fits together as an expressive portrait. Leaders must find a team who can use data to piece together a richly textured view of the world, in which resulting interpretations can add up to something greater than the data collected.
In this way, sensemaking teaches us where to put our attention. We don’t try to know everything; we work to make sense of something. In the midst of complexity, a sensemaking practice allows us to determine what actually matters.