Tag Archives: knowledge

The Collaborative Improvement Environment

The responsibility of every employee, from the front-line to the executive suite, is business performance improvement. But barriers exist to realizing positive bottom-line impact, operational efficiency, organizational effectiveness, and a continual flow of improvements built on the knowledge of a firm’s employees.

According to a McKinsey & Company study, amazingly 70% to 75% of companies do not have an improvement process defined and implemented on a broad basis. Senior executives seeking performance improvement face formidable tasks such as:

  • Rallying and enabling all employees to seek improvements everyday
  • Defining and implementing a truly sustainable improvement process, sustainable for 10 years or more
  • Shifting an organization’s culture to one of collaboration and improvement
  • Leading the 4 generations which comprise the typical workforce within an enterprise, each with its own preferred style of communication and comfort with current technologies
  • Leveraging constant improvements to gain defensible competitive advantage for a firm.

Improvement Trends

Over the past several decades, stretching back to the mid-20th century, several trends exist in how businesses improve.

  • Many improvement methods have been attempted through the years with varying results
  • Each improvement method has a finite lifecycle of impact to an organization’s performance gains (see my previous blog on “Patterns of Improvement”
  • Sustainable, continuous improvement has been a promise that is seldom realized
  • True sustainability has been hampered by numerous root causes.

Root Causes of Decaying Impact

Root causes of impact decay I have observed over the years include:

  • Improvement is a special program within a firm and is not treated as part of employees’ day-to-day jobs
  • No structural incentives exist to motivate improvement behaviors
  • Outside experts, such as academics, gurus, and consultants, drive improvement programs creating the risk of high levels of organizational resistance
  • At most, only 1% to 2% of an organization’s workforce is asked by senior leadership to participate in improvement efforts, e.g. 300 to 600 people in a 30,000 person company
  • Distribution of deep improvement know-how and tools is limited to a central team or a select few specialized resources, e.g., Six Sigma Black Belts, the Quality Department, or the Office of Reengineering
  • Organizations run out of energy and endurance beyond a 3 to 5 year period
  • Special improvement programs lose focused, visible leadership from senior executives after 12 to 18 months or leadership of the firm changes and along with that comes a new executive agenda
  • New improvement methods hit the market every 4 to 6 years creating systemic discontinuities caused by implementation ramp-up times of 6 months to 2 years.

The Collaborative Improvement Opportunity

To manage through the long trough of the global recession and the protracted recovery, senior executives must improve how their business improves. Root causes of the decaying impact of improvement processes must be attacked through a focused effort to create a high performance collaboration environment.

I believe a window of opportunity exists for an enterprise to leap forward beyond its competitors by requiring and enabling employees to adopt improvement behaviors executed on a routine basis. Also, a window of opportunity exists for an enterprise’s senior leadership team to create a lasting improvement legacy for the organization.

What’s Next?

A thriving enterprise requires continual performance improvement in order to thrive. Truly sustainable improvement methods have been elusive over the past several decades. The impact of improvement programs decays as a result of a myriad of root causes, which must be addressed with a hybrid of traditional and modern techniques. Senior executives must role model improvement behaviors to drive a cultural shift in their organizations towards collaboration and the search for business improvement everyday and in every way.

Knowledge Architecture

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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?

Knowledge Age Economics

Knowledge Economics

What are the new laws of economics in the knowledge age?  In the manufacturing age, the quantity of goods or services demanded and subsequently supplied determine the price of goods or services.  The laws of supply and demand dictate at what price and quantity the economy operates most efficiently – the point of equilibrium.

Manufacturing Age Economics – Physical Assets

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In the information age, the laws of supply and demand still apply.  In a true knowledge economy, knowledge and information are demanded and supplied. The economic system finds equilibrium. However, there exists a fundamental difference between economies based on physical assets and those based on knowledge assets.

Knowledge Age Economics – Knowledge Assets

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The shape of the knowledge demand curve follows the same path as the manufacturing demand curve. The more one piece of information is demanded, the more value the market will place on that knowledge asset (directly proportional).

The shape of the knowledge supply curve, on the other hand, does not follow the same principles as the manufacturing supply curve.  For a manufacturing supply curve, the price of a physical asset decreases as its supply increases (inversely proportional).  For a knowledge supply curve, the price, or value, of a knowledge asset increases as its supply increases (directly proportional).

The knowledge supply function in this economic model is based on three principles.

  1. The more information on a subject that exists, the more it is valued.
  2. Knowledge follows a law of conservation. As knowledge is consumed, it does not disappear as a physical asset does. Rather, knowledge has infinite duration. (Side note: In physics, this law is known as the conservation of information. There also exists the information paradox which some physicists have argued exists at the singularity of a black hole. As matter collapses in a field of infinite gravity, does the information stored in atoms disappear?)
  3. As knowledge is utilized, more knowledge is generated.  Two pieces of knowledge come together to form new knowledge. The production of knowledge is an infinite, self-perpetuating process.

Equilibrium in the knowledge economy is achieved when the supply curve perfectly overlays the demand curve. As a result, an infinite number of equilibrium points occur.

What are the shapes of the supply and demand curves for the output of your industry?  What are the shapes of the supply and demand curves for the output of your business? Do you or your organization develop and distribute knowledge that follows the knowledge supply curve? Have you fully leveraged the new paradigm of supply in the knowledge age?

The 5 I’s of Change

Much has been written about major change and transformation efforts in organizations. But what roles within organizations are actually critical to successfully achieve transformational goals?  The simplistic answer is “change agents”.

But in order to provide a more tangible definition of change agents, here is a taxonomy for the various types of knowledge workers that can be applied when considering the formation of transformation teams.  This taxonomy goes beyond examples of profession and vague, global descriptions of knowledge activities.  The classification of knowledge worker types takes a process view – what knowledge workers do.  Five classifications of knowledge worker comprise the taxonomy: (1) initiators, (2) innovators, (3) integrators, (4) implementers, and (5) instigators.

5 I's PictureAll five roles must be filled by an organization undergoing major transformation and change. A balance must be struck dependent on which stage in a transformation lifecycle the organization moves. Importance must be placed on the “personality profile” of a transformation effort and all its contributors.

Initiators create knowledge through “original thinking” and trigger step-change breakthroughs in new paradigms and new business models for the transformed business.

Innovators modify, refine, and build upon ideas to generate new knowledge that go beyond the initiator’s work.

Integrators aggregate, consolidate, synthesize, and broker existing knowledge to develop holistic, systems views.  These holistic views provide new perspectives and insights.

Implementers apply, utilize, and execute the “know how” within an intrinsic knowledge base.  Implementers unleash the tangible, extrinsic value inherent in knowledge – value that is unreleased until applications are realized.

Instigators challenge ideas, old and new, throughout the knowledge process.  They drive out-of-the-box thinking as well as ground new ideas, innovation, integration, and implementation in the harsh realities of feasibility and viable economic returns.  Instigators say “Yea” and say “Nay.”

So what?  Determine the team personality required for each stage of the business transformation lifecycle.  “Profile” potential members based on individual personalities as demonstrated by past behaviors.  Develop and manage a fine balance of personalities through the change process.  Introduce new members/personalities as required.  Visibly recognize and reward specific team members for playing varying roles.