Tag Archives: management

Knowledge Architecture


Not all knowledge is created equal.  Knowledge is one of those vague, global terms of which people assume they know its definition.  Very seldom in conversations about knowledge or learning will someone raise a hand and ask, “Hey, what is knowledge anyway?” unless he is Aristotle or some philosophy major.  One can postulate that there are different types of knowledge.

A hierarchical structure is one method to use to classify knowledge.  We show here a knowledge architecture that has seven levels: null, data, facts, know-how, memories, wisdom, and connections. Notice that the model begins with a state known as “void”. In this state, even the recognition of nothing does not exist.

Each layer of the hierarchical knowledge architecture is separated from other layers by a specific type of insight. Insights differ according to the particular level of knowledge on which they are acting. Seven insights elevate knowledge through the hierarchy: instinctual, definitional, contextual, utilitarian, experiential, reflective, and networked. The progression of insights up through the hierarchy represents increasing sophistication in thinking with “connections” as the highest order of knowledge.

Void + Instinctual Insight = Null

We see “instinctual insight” acting on the void to create null, the first layer of knowledge. This is the base level of knowledge. Null emerges from void as primal instincts create an awareness of one’s environment. This level of knowledge is deemed null because this level is the level at which simple consciousness of existence or non-existence occurs. Expressed another way, null takes on a binary state, 1 or 0. In the void even this simple a consciousness does not exist. Note that null represents the “whole” as within Zen philosophy.

Null + Definitional Insight = Data

We see “definitional insight” acting on the null to elevate knowledge into data. At this level, thought processes give definition to objects and actions in the null. In other words, definitional insight labels a collection of unnamed and unidentified things so that distinctions are drawn between them. Each object or action is now defined and becomes data.

Data + Contextual Insight = Facts

“Contextual insight” acts on data to create facts in the next layer in the hierarchy. Facts represent a richer and fuller set of knowledge than pure data. For example, if we take the word “coffee” as data there is no context for what coffee actually means. Given some context such as the commodities trading market, coffee takes on the meaning of a traded good. If food service is the context, then coffee takes on the meaning of a beverage. Contextual insight allows distinctions be made between data to create different facts.

Facts + Utilitarian Insight = Know-how

“Utilitarian insight” acts on facts to create know-how. How is an object to be used and for what purpose? In our coffee example, utilitarian insight emerges to provide the know-how for what to do with coffee. In the commodities market context, know-how would be how to trade coffee on the spot or futures markets. In the food service context, know-how would be how to prepare coffee for consumption. Without utilitarian insight, coffee has no real value. Simply speaking, utilitarian insight provides the knowledge of utility.

Know-How + Experiential Insight = Memories

“Experiential insight” acts on know-how to create memories. Actions taken or the execution of know-how generates experience that can be remembered and used for improved execution. Following the coffee example, experience in making coffee enables a barista to remember how much foam to put on top of a latte.

Memories + Reflective Insight = Wisdom

“Reflective insight” acts on memories to create wisdom. Reflection works on a “meta-plane” of thinking and takes on a new layer of abstraction in the knowledge hierarchy. Insights are not simply generated on single points of execution but on a set of memories. For example, remembering how to make a latte is a memory but being able to forecast demand in a coffee shop during the course of a day takes wisdom. Wisdom emerges as a person can take a step back to reflect and learn from prior actions and decisions.

Wisdom + Networked Insights = Connections

“Networked insights” act on wisdom to create connections, which represent the highest order of knowledge in the hierarchy. Making connections links related or unrelated pieces of wisdom to generate knowledge that would not emerge otherwise. For example, connecting the preparation of a perfect latte to the film “Seven Samurai” in which one of the samurai has dedicated his whole life to perfect his skills as a swordsman represents connecting two topics that on the surface are completely unrelated. Networked insights create connections that drive thinking further and generate the highest order of knowledge, connections.

At what level of knowledge do you work most of the day? At what level do you believe your colleagues, subordinates, and superiors work? Do you drive yourself to think at higher levels within the knowledge hierarchy? At what level do you believe you can add the most value to your work group or your organization during times of large-scale change?

Risk and Change

Risk-Change Matrix

People love control and abhor chaos.  Yet most of our daily lives are filled with uncertainty and change.  The first step to maintaining a sense of order amidst the chaos is awareness of change and awareness of the type of uncertainty (risk) with which you are faced.  An understanding of the current state of your environment will inevitably benefit you in your quest for control.

To aid awareness of the chaos-control state of your environment, we can use the simple matrix shown here.

Type of Environment

Type of Risk




1. Certain, stable state

“Business as Usual”

3. Certain, changing state



2. Uncertain, stable state


4. Uncertain, changing state


Risk is broken down into two types: deterministic and stochastic. Actions and decisions in a state of deterministic risk produce outcomes that are linked to past and present known behaviors in the system. In other words, future system behavior can be predicted with certainty based on the knowledge of previous outcomes. We label this state’s risk as “certain” but one must realize that this is an extreme case. Nothing is ever certain but to serve the purpose of this matrix we assume that this deterministic state can exist.

Stochastic risk is risk that has uncertain outcomes, outcomes that are not necessarily linked to past history of events or occurrences. Probability does play a role in the stochastic state. Expected outcomes can be used in decision making with the understanding that the past does not play a strong predictive role in system behavior. One can look at this state as one of a complex, non-linear system. Decisions and actions can cause completely unexpected system behavior.

Environment is also broken down into two types: stable and dynamic. Stable environments do not change in the short to medium term. Drivers of a business’s state are relatively constant. For example, an industry’s competitors, customers, and suppliers are not undergoing major changes or shifts in relative power.

Dynamic environments are undergoing major changes that could be caused by industry consolidation of suppliers, customers, and/or competitors. Other drivers of dynamic environments are disruptive technologies that enable disintermediation or cause obsolescence of particular products or services.

Looking at the 2×2 matrix formed by type of risk and type of environment, we see four possible states for a business at any one time.

1.   Certain, stable state

  • Future system behaviors can be predicted by prior, historical behaviors
  • No disruptive change is occurring on the business’s landscape
  • “Business as usual” characterizes this type of environment

2.   Uncertain, stable state

  • Future system behaviors cannot be predicted by knowledge of past responses to decisions and actions
  • No disruptive change is occurring on the landscape
  • “Non-linear” characterizes this environment

3.   Certain, changing state

  • Future system behaviors can be predicted by prior, historical behaviors but only on a short time horizon given the dynamic, changing industry state
  • Disruptive change is occurring on the business landscape that might or might not be caused by an executive’s own business
  • “Transformation“ characterizes this environment

4.   Uncertain, changing state

  • Future system behaviors cannot be predicted by knowledge of past responses to decisions and actions
  • Disruptive change is occurring on the business landscape that might or might not be caused by an executive’s own business
  • “Chaos” characterizes this environment and presents the greatest challenge to an executive’s decisions and actions to be taken by an organization

Where do you see your business?  Would others agree with you?  How do you conduct your daily life and under what assumptions about the state of your business? Where are you the most comfortable?  Where are you the least comfortable? Is your business properly prepared to thrive in its current state? What about your competitors?