FRAMEWORK : Learning
Listening to Leonardo: “Learning Never Exhausts the Mind.”
Curiosity and Learning
“I roamed the countryside searching for answers to things I did not understand,” Leonardo writes in his notebooks. Walter Isaacson believes that Leonardo’s “distinguishing and most inspiring trait was his intense curiosity.”
Consider a to-do list sample from Leonardo’s Notebooks:
“Get hold of a skull
Get your books on anatomy bound
Get Messer Fazio to show you about proportion
Calculate the measurement of Milan
Get the master of arithmetic to show you how to square a triangle
Ask Benedetto Potinari by what means they go on ice in Flanders
Draw Milan
Observe the goose’s foot
Find a master of hydraulics
Describe the tongue of the woodpecker”
He’s curious about everything—and he’s always collecting data.
As data analysts, we start our work with a set of observations about the data and how to use it. Then our curiosity drives questions: Why is this happening? What is causing this? How could we do things differently? To change, what would we have to know? How can we figure all this out? What do we want to learn?
Like Leonardo roaming the countryside, or the streets of Florence or Milan, listening to and watching the people, my own intense curiosity fueled my study of consumers, and in particular, consumer complaints and dissatisfaction. To listen to their voices, I dug deep into open source data, reading hundreds of complaint narratives, making observations, forming questions, looking for patterns, searching for answers.
Oftentimes, in our transaction-based world, when we indulge our curiosity, we’re viewed as “slow”. Or we police ourselves to be expedient with our data work.
Kenneth Clark, the art historian and critic, believes Leonardo da Vinci was “the most relentlessly curious man in history.” In the chapter “Learning from Leonardo,” Isaacson advises “Be curious, relentlessly curious.”
Let’s learn from Leonardo: give ourselves permission to be curious and let our curiosity drive continuous learning in our lives.
Art and Science
While looking at the “swirling power” of Leonardo’s “Deluge drawings,” (worth a click) Isaacson recounts asking the curator at Windsor Castle if Leonardo had done the drawings as works of art or science.
The curator replied, “I do not think that Leonardo would have made that distinction.”
Both Leonardo’s art and science was based in experiment and testing. According to Isaacson, “Leonardo broke with tradition by basing his science primarily on observations, then discerning patterns, and then testing their validity through more observations and experiments.”
Sounds like today’s data science, doesn’t it?
Although Leonardo began with his strictly empirical approach, when he had access to books in his native Italian in the 1490s, “it helped him realize the importance of being guided not only by experiential evidence but also by theoretical frameworks. More important, he came to understand that the two approaches were complimentary, working hand in hand.”
What strikes me some 500 years later is that our scientific testing methodology is so like Leonardo’s.
Yet, so often today, understanding the complexity of test design is rare. You have to plan what you want to learn.
Today, we have to be the catalysts—to make the effort and investment, to plan the learning and testing, to coordinate cross-functional groups for systematic experiment and implementation.
Leonardo wrote that no instant is self-contained and his ongoing experiments reflect that insight. As Isaacson comments, Leonardo “relished a world in flux.”
Leonardo “looked upon his art and engineering and his treatises as part of a dynamic process, always receptive to a refinement by the application of a new insight.”
Just as we know today, for Leonardo “there was always something to be learned, another stroke to be gleaned from nature.” This is the core of our do/learn/do approach—ongoing process, always updating and evolving, continuously creating and recreating.
At the intersection of art and science.
Mindset and Method
Although we highly recommend Isaacson’s entire biography, his “Conclusion” includes a richly annotated list titled, “Learning from Leonardo.” This list and Isaacson’s comments are worth your time a hundred-fold!
Isaacson concludes that “Leonardo’s brilliance spanned multiple disciplines, which gave him a profound feel for nature’s patterns and crosscurrents. His curiosity impelled him to become among the handful of people in history who tried to know all there was to know about everything that could be known…
…and even though we may never be able to match his talents, we can learn from him and try to be more like him.”
When I reflect on Isaacson’s “Learning from Leonardo” lessons, I group them into “Mindset” and “Method”. In my way of thinking about our work in data analytics, mindset is as important as method.
Let’s look at Mindset first:
Be curious, relentlessly curious.
Seek knowledge for its own sake.
Retain a childlike sense of wonder.
Get distracted.
Let your reach exceed your grasp.
Create for yourself, not just for patrons.
Indulge fantasy.
Be open to mystery.
These lessons represent a mindset—the way you approach something before you actually get your hands dirty with the scientific method and concrete data.
Allowing for some overlap, I see the Method lessons as:
Be curious, relentlessly curious.
Observe.
Start with the details.
See things unseen.
Go down rabbit holes.
Respect facts.
Think visually.
Procrastinate.
Create for yourself, not just for patrons.
Collaborate.
Whether it’s in our consulting work or in product development, it’s critical to create the proper mindset before digging into the data. Rather than merely following a prescribed method, a mindset like Leonardo’s prepares you for the discipline of method while allowing for adaptation and flexibility.
Finally, just as Leonardo innovated with his painting technique “Sfumato” (literally “going up in smoke”), Isaacson reminds us that “not everything needs sharp lines.”
This last lesson bridges Mindset and Method, allowing us to “embrace ambiguity, paradox and uncertainty:” *
Be open to mystery.
*How to Think like Leonardo, Michael J. Gleb, Delta, 2004.
Why Leonardo Matters
It may not come naturally to mind among the data analytics and decision management communities—but the person we could learn the most from in our field is Leonardo da Vinci. Leonardo remains relevant in his merging of art and science, his scientific methods, his innovation.
For this reason alone, it’s worth thinking about Leonardo da Vinci and taking a leap to apply it to your work—and your life.
Most of all, we should embrace his relentless curiosity and his life-long search for learning. Surely, Walter Isaacson didn’t intend this biography to be a “business book,” and it’s not listed as such on the best-seller lists.
Yet, just as Leonardo saw the interconnectedness of all things, what we can learn from him weaves intricate patterns throughout our work and lives.
