1. Knowledge is survival\nDescartes famously proposed cogito ergo sum; \u201cI think, therefore I am.\u201d This was a basic but profound philosophical proposition for an individual thinker\u2019s existence as a thinking entity.\nIn today\u2019s corporate and wider organizational world, \u201cI know, therefore I will continue to be,\u201d may well be a basic but profound philosophical proposition for an individual organization\u2019s 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.\nNo longer does the big fish eat the small fish; in today\u2019s world \u2013 as Klaus Schwab, founder and executive chairman of the World Economic Forum, famously noted \u2013 it is the fast fish that eats the slow fish. And to be fast, an organization must know where it is \u2013 based on data and information.\nTherefore, how an organization and its component parts obtain data and information and develop knowledge \u2013 down to the role of the individual contributor \u2013 is of vital existential importance.\nData > Information > Knowledge > Wisdom (DIKW) model\nEven though an organization must use data and information to know sooner and know better, this leaves out a significant piece of the picture.\nAn organization must transform knowledge into organizational wisdom.\nThe 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.\nFor 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.\nAlthough 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.\nAlthough 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.\nHowever, 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.\nUnder 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.\nUltimately 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.\n2. Focusing in on the \u2018knowledge > wisdom\u2019 relationship\nNow 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.\nLet\u2019s assume that your organization is pushing the knowledge management limits, creating and managing ever more knowledge \u2013 both technical and procedural \u2013 at an ever-faster pace. This is reason to celebrate, no\u2026!? Isn\u2019t this the goal?\nConsider 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.\nThe 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.\nWhat 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\u2019t always the goal.\nUnder 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.\nKnowledge failures: How overproduction and defects limit the \u2018knowledge > wisdom\u2019 potential\nThere 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.\n(Knowledge) Overproduction\nKnowledge 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."\nThe 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.\nThis means that the challenge is not always to know more, but to know enough. \n(Knowledge) Defects\nKnowledge 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.\nDefective knowledge: This is knowledge that is or becomes flawed\/inaccurate at one of two points or periods:\n\nPoint of development: This knowledge was flawed at the point of recognition or creation. In other words, it\u2019s accuracy was initially accepted in error.\nProcess of evolution: This is knowledge that was once accurate and would still be accurate if not for defective knowledge management. In other words, it\u2019s original accuracy is no longer sound due to mismanagement.\n\nExpired knowledge: This is knowledge that was once accurate but is now flawed\/inaccurate due to changing environment. In other words, it\u2019s accuracy has expired.\nThis means that the challenge is not always to know more, but to know accurately. \n3. Channeling organizational wisdom towards performative action\nRecognizing 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.\nTo be effective itself, organizational wisdom must be channeled into actual action, or what Nick Bontis called \u201cperformative action.\u201d For performative action, think the deliberate actions performed via organizational agency. I\u2019ll tackle the challenges and transitions from organizational wisdom to performative action in the future.\nFor 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.