Why more knowledge is not always better for your organization

The challenge lies in addressing knowledge failures to maximize transformation of knowledge into organizational wisdom.

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1. Knowledge is survival

Descartes famously proposed cogito ergo sum; “I think, therefore I am.” This was a basic but profound philosophical proposition for an individual thinker’s existence as a thinking entity.

In today’s corporate and wider organizational world, “I know, therefore I will continue to be,” may well be a basic but profound philosophical proposition for an individual organization’s continued existence as an organizational entity. This significance of this is only increasing in tandem with the ever-greater proliferation of business management and enterprise resource management software, and, more profoundly, as a result of the unprecedented (and still nascent) digital transformation.

No longer does the big fish eat the small fish; in today’s world – as Klaus Schwab, founder and executive chairman of the World Economic Forum, famously noted – it is the fast fish that eats the slow fish. And to be fast, an organization must know where it is – based on data and information.

Therefore, how an organization and its component parts obtain data and information and develop knowledge – down to the role of the individual contributor – is of vital existential importance.

Data > Information > Knowledge > Wisdom (DIKW) model

Even though an organization must use data and information to know sooner and know better, this leaves out a significant piece of the picture.

An organization must transform knowledge into organizational wisdom.

The full life-cycle of this process is represented by the Data > Information > Knowledge > Wisdom hierarchy, or Pyramid, brought to notoriety by Dr. Russel Ackoff. Imagine a pyramid with Data as its base, Information as a second level, Knowledge as a third, and Wisdom as the peak. The flow, logically, is from the base upwards to the peak.

For wisdom, think applied knowledge to form independent, next-level conclusions. For example, an organization may know something about the marketplace, but only reflection on this knowledge can lead to wisdom on what the knowledge means in terms of the optimal course of action.

Although the model has its detractors, it has tremendous conceptual value for how we as humans, and our organizations, use data to gain extensive information, and grant ourselves broad knowledge and ultimately, a wisdom beyond the knowledge itself.

Although the pyramid is only one representation, what stands out about the DIKW Pyramid is the representation of the smooth flow from data to wisdom that tapers towards a peak. What this means is that the length of the base (Data), directly impact the base of the second level (Information). The same is true up the pyramid.

However, this is not a reflection of the actual reality, and the broader language of DIKW hierarchy is quite apropos here. The DIKW as a hierarchy is what could be considered not as a pyramid, where the quantity (represented by the length) of each level directly impacts the quantity (represented by length) of the next level from upwards, but instead as a dynamic and variable step function.

Under this model, a broad base at one level does not lead to an almost similarly broad base at the next. A broad base of data does not smoothly taper into information, knowledge, and, at the peak, wisdom. In fact, under this model, the edges are not smooth at all, but instead visually appear as steps.

Ultimately what this means is that there is not a guaranteed broad flow from data upwards. This will become more apparent deep-diving into the top two levels of the model.

2. Focusing in on the ‘knowledge > wisdom’ relationship

Now I want to focus in on the top two levels of this step-function, knowledge and wisdom. Although turning data and information into knowledge is important, there are multiple possibilities worth discussing within the K > W relationship itself.

Let’s assume that your organization is pushing the knowledge management limits, creating and managing ever more knowledge – both technical and procedural – at an ever-faster pace. This is reason to celebrate, no…!? Isn’t this the goal?

Consider two organizations; the first, Organization No. 1, has significant knowledge but only minimal organizational wisdom. It transforms only a small percentage of its knowledge to organizational wisdom.

The second, Organization No. 2, has less knowledge (and knowledge under active management) than Organization No. 1. However, it has significant organizational wisdom. The organization transforms its knowledge into organizational wisdom at a much higher rate than Organization No. 1.

What this effectively means is that Organization No. 2 is in a more optimal position than Organization No. 1, even though it has less organizational knowledge; It is gaining more wisdom from less knowledge. And for this reason, pushing knowledge management limits isn’t always the goal.

Under these scenarios, Organization No. 2 would likely be better served focusing on improving its knowledge-to-wisdom processes, as opposed to developing greater knowledge. Here I will discuss two potential challenges to this and approaches towards solutions.

Knowledge failures: How overproduction and defects limit the ‘knowledge > wisdom’ potential

There are multiple reasons that an organization could fall into the situation of Organization No. 2 above, facing the challenge of ineffective knowledge > organizational wisdom processes. These could be considered "knowledge failures," and I will discuss two key failure types here.

(Knowledge) Overproduction

Knowledge overproduction is the development of knowledge in excess of that required to optimally maximize organizational wisdom. The overproduction of knowledge is quite counter-productive for many reasons; one is in terms of wasted efforts making sense of things, perhaps most aptly referred to by David Shenk nearly two decades ago as "data smog."

The overproduction of knowledge is also counterproductive as an unnecessary effort in terms of knowledge development, sharing, processing, and management. Remember, underlying this knowledge creation is effort towards obtaining data and information, and further developing information into knowledge.

This means that the challenge is not always to know more, but to know enough.

(Knowledge) Defects

Knowledge defects are of multiple types, but the constant across them is that the knowledge developed or held is fundamentally inaccurate. How often is knowledge flawed, inaccurate, or misleading (as a small component of a larger picture)? This breaks down into two further categories.

Defective knowledge: This is knowledge that is or becomes flawed/inaccurate at one of two points or periods:

  1. Point of development: This knowledge was flawed at the point of recognition or creation. In other words, it’s accuracy was initially accepted in error.
  2. Process of evolution: This is knowledge that was once accurate and would still be accurate if not for defective knowledge management. In other words, it’s original accuracy is no longer sound due to mismanagement.

Expired knowledge: This is knowledge that was once accurate but is now flawed/inaccurate due to changing environment. In other words, it’s accuracy has expired.

This means that the challenge is not always to know more, but to know accurately.

3. Channeling organizational wisdom towards performative action

Recognizing and combating knowledge overproduction and knowledge defects are an easily available means to encouraging, promoting, and achieving optimal knowledge management and maximization of the knowledge > organizational wisdom process. However, although the DIKW model stops there, the organizational knowledge ecosystem does not.

To be effective itself, organizational wisdom must be channeled into actual action, or what Nick Bontis called “performative action.” For performative action, think the deliberate actions performed via organizational agency. I’ll tackle the challenges and transitions from organizational wisdom to performative action in the future.

For now, the takeaway is that more knowledge is not always better; the challenge is not always to know more, but to know enough and to know accurately. This can be actively promoted by addressing knowledge overproduction and knowledge defects, within a larger picture of effectively channeling data and information through the DIKW model as effectively as possible towards knowledge and organizational wisdom.

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