As organizations scramble to implement knowledge management programs at the behest of their executive officers and in an attempt to remain competitive, these newly appointed knowledge officers, having quickly become acquainted with the types of knowledge projects they can engage in will often encounter the fog that exists around knowledge projects like the creation of a knowledge repository. By its name, one would think this is a repository that houses knowledge, but how is this different from a repository that houses information? I mean, what you retrieve from a repository may be knowledge to you, but it may only be information to me. As pointed out by Davenport and Prusak (2000), the difference between information and knowledge is still a bit fuzzy to many people. They state, “In the examples of internal knowledge repository projects, we observed the storage of both knowledge and information. If the distinction between knowledge and information is seen as more of a continuum than a sharp dichotomy, most projects that focus on internal knowledge deal with the middle of the continuum – information that represents knowledge to certain users.”
So we know that knowledge is categorized as one of two types: tacit – the stuff that’s hard to separate from the individual – and explicit – the stuff that the individual can codify and share easily. When tactic knowledge is made explicit it is codified, or documented, and stored for further use by anyone who needs it. Explicit knowledge is essentially information since knowledge resides in the minds of human beings. Explicit knowledge, or information, only becomes knowledge to the user when the user has, as Nonaka and Takeuchi demonstrate in their knowledge spiral model, gone through a combination and internalization process. In their four quadrant knowledge spiral model, “explicit knowledge is then systematized and recombined in the combination quadrant, whereupon it once again becomes part of individuals’ experiences. In the internalization quadrant, knowledge has once again become tacit knowledge” (Dalkir, 2011).
Whether or not codified information or codified explicit knowledge is in fact information or knowledge is a lot like the idea behind predicting the location of an electron when released from a known point – its new location is based on a number of possibilities so then the question is not where is the electron now, but rather what is the likelihood the electron is in a specific location. In Quantum Mechanics, this is explained using the electron’s probability wave. Using this analogy, we could say the same thing about codified information and codified explicit knowledge. Whether the user sees it as information or knowledge is based on a number of possibilities, which could include the user’s ability to understand it, the user’s need at the moment he encounters it, etc. So, the probability that codified information and codified explicit knowledge are either information or knowledge depends on the user – that is, if we combine this analogy with the Nonaka and Takeuchi model, it is the probability of internalization.
Adding to the complexity of this identification issue, whether or not something is information or is knowledge also depends on the work done along the continuum mentioned by Davenport and Prusak. So something must be said about the relationship of information management and data management to the goals of knowledge management which include knowledge creation, knowledge acquisition, and knowledge transfer. The efforts and activities associated with managing data, information, and knowledge are mutually supporting. This is especially true since these are activities that take place within organizational systems comprised of people, processes, and technologies. Before we can create, acquire, and transfer knowledge, we must have data, which we use to develop information, which we internalize to create knowledge.
At the heart of data management is the creation of a data strategy that allows one to fuse disparate pieces of data to create information. At the heart of information management is the creation of an information strategy that allows one to manage the flow of information using technologies so that the right information can be placed before the right people at the right time so that they can analyze that information, adding value to it through the combining process aforementioned and internalizing this new product, create knowledge. Like a bee transferring pollen from a plant to a new location allowing the plant to propagate, these individuals then, through knowledge management initiatives like communities of practice, meet with others in an environment conducive to knowledge transfer. Once these communities have transferred knowledge amongst one another, they then return to their organizational systems in order to deposit their knowledge in databases, supporting the evolution of organizational memory. This is by no means a comprehensive view of the relationship that exists between data, information, and knowledge management as these are broad disciplines in their own right, but this provides a nice example of how these disciplines support one another, which should clear up any confusion regarding when something is considered data, information, and knowledge.
Dalkir, K. (2011). Knowledge management in theory and practice. Cambridge, MA: The MIT Press; second edition (March 4, 2011)
Davenport, T., & Prusak, L. (2000). Working knowledge. Boston: Harvard Business School Press.