The DIKW triangle is a way to organize the relationship between data, information, knowledge, and wisdom. When I first came across this model, it resonated with me. But I wanted to understand: how do you move up the hierarchy?
The DIKW triangle is a way to organize the relationship between data, information, knowledge, and wisdom. Given its pyramidal shape, there is an implied hierarchy moving from data, up to wisdom. When I first came across this model, it resonated with me as a way to learn — starting with raw ideas and ultimately obtaining a deep understanding of a topic. But I wanted to understand: how do you move up the hierarchy?
Around the same time I first saw this, I was exploring computation and data science. It seemed obvious to me that modern tools and data science are one way to turn data into information. Insights can be gleaned from raw data using data science. Whether using exploratory data analysis or sophisticated machine learning models, uncovering the patterns within data leads to new information. With this insight, I began showing the DIKW triangle in data science teachings. What is data science? It is a set of tools that enable people to find information within data. But then what's the difference between information and knowledge?
Knowledge might be best described as information that relates to other information and can be acted upon or used for something. Then how can information be turned into knowledge? I think the key here is relationships between information or ideas. As I continued to explore this idea, I was introduced to a piece of software called Obsidian. This app creates linked articles (think Wikipedia) that create knowledge graphs. This network of ideas links old ideas to new ones using simple internal backlinks. In my mind, software aside, this process of linking ideas is how knowledge can be created. The ability to place information into context allows a person to connect and even act upon this new information. If data science is how to move from data to information, then knowledge graphs are how to move from information to knowledge.
When describing this idea to others, or in teaching, I brushed off the final step of moving from knowledge to wisdom. “That would take a lot longer to explain,” I would say. But candidly, I wasn’t sure. What is wisdom? How is it different than knowledge? And how is wisdom made? Is it just gained through experience? These questions still nagged me.
In attempting to define wisdom, I think a wise person knows how to apply knowledge in many contexts. Like the fortune cookie that spouts wisdom, the pithy sayings are broadly applicable. Perhaps wisdom is the abstraction of knowledge. In teaching, abstraction is the ability to remove the specifics of an example and find the principle that underlies the lesson. Similarly, an abstract idea is just an idea, not anchored to any specifics. For educators, teaching students to understand a lesson in the classroom is relatively easy; teaching students to abstract away the lesson and apply it in the real world is much more difficult. Indeed, transfer learning is considered the “holy grail" in education — taking the specifics of the lesson and transferring it to other scenarios. Abstracting the first principles from specific lessons and applying learning from those lessons is wisdom. A wise person, often times older, is someone who has learned enough lessons to apply them broadly. Beyond “getting old“ or “life experience“, how can we think about moving from knowledge to wisdom?
With this framework in mind, it seems that the process of a abstraction to first principles is the key. In pop culture, another name for first principles is mental models. Mental models are abstract frameworks that can help make sense of the world, describe behaviors, and help decision-making. Mental models are shortcuts to abstraction, identification of first principles, and application of knowledge to real life. Wisdom gained.
Mental models range from explaining the behaviors of molecules to explaining behaviors of man. Some of my favorite mental models follow. In each of these, I describe the model, the abstraction, and how it can be applied to a far-ranging field.
"The map is not the territory"
This quote from Alfred Korzybski means the map of reality is not reality. He said, "A map is not the territory it represents, but, if correct, it has a similar structure to the territory, which accounts for its usefulness." Even the best maps are imperfect, but that's why they're useful: they are abstractions and represent something in an intentional way. For example, the original 1972 NYC subway map showed locations of subway lines on a map that didn’t align with reality. A debate ensued, as to whether it should be updated. Of course, a geographically accurate subway map would be much harder to read, and therefore less useful. The map is not the territory. But beyond physical maps, this mental model applies to any abstraction of an idea, remembering that it is just that: an abstraction.
"The Red Queen"
The Red Queen is a hypothesis in evolutionary biology proposed in 1973, that states a species must constantly adapt, evolve, and proliferate in order to survive while pitted against ever-evolving opposing species. Van Valen coined the term "Red Queen" because under this model, species have to "run" or evolve in order to stay in the same place, or else go extinct.
"Now, here, you see, it takes all the running you can do, to keep in the same place." — the Red Queen, said to Alice in Lewis Carroll's Through the Looking-Glass in her explanation of the nature of Looking-Glass Land
The Red Queen Hypothesis explains competition through selection; however, the idea extends beyond evolutionary biology. For example, in tech, companies need to ever evolve in order to keep up with competition, even if it seems the company is simply running in place.
Brownian motion is the random motion of particles suspended in a medium (a liquid or a gas). In 1827, botanist Robert Brown looked through a microscope at pollen immersed in water and saw random "dance-like" motion. In 1905, almost eighty years later, Albert Einstein published a paper where he modeled the motion of the pollen particles as being moved by individual water molecules. This explanation of Brownian motion served as convincing evidence that atoms and molecules exist. Beyond physics, the notion of Brownian motion can be applied to creativity and idea generation. "Chaotic exposure to many ideas is the elixir for creativity," I wrote previously. Brownian motion can describe the chaotic and random interaction between people and ideas, uncertain of their direction or what they'll interact with next.
The DIKW Triangle is also a mental model, of sorts. It is an organizing framework for different types of information: raw ➡️ specific ➡️ relative ➡️ generic. The abstraction of this concept, it becomes broadly applicable. Applied to expertise, it could explain how people gain skills; applied to art, it could explain the trajectory of great artists; applied to our understanding of an idea, it could explain learning and mastery; applied to mental models, it explains their popularity, but also reveals their potential.