Knowledge Matters™

About three years ago I came up with the idea of the “Knowledge Conduit”. The idea is still a bit raw but I thought I would share it with you anyway. The Knowledge Conduit is illustrated below.

First, you should observe that there are two distinct domains – the descriptive domain and the predictive domain – and that data and information belong to the descriptive domain. I like Davenport and Prusaks’ (1998, pp 2-3) definition of data as being "a set of discrete, objective facts existing in symbolic form that have not been interpreted". The symbolic form may be text, images, or pre-processed code. Data is usually organised into structured records, however it lacks context. The declaration ‘Iron melts at 1,538 degrees Celsius.’ is a data statement because it has no context.

In this model when data is enriched by adding context it may become information. Information is data with a message, and therefore has a receiver and sender. It is data with relevance and purpose that is useful for a particular task, and is meant to enlighten the receiver and shape their outlooks or insights. Information results in an action that allows the data to be applied to a specific set of circumstances and to be employed effectively. Data only becomes information after it has been interpreted by the receiver. Furthermore information is descriptive. For example the statement ‘Newcastle steel-mill’s smelter temperature has been set at 2,300 degrees Celsius.’ conveys information because it has been enriched by context. The enrichment from data to information is a ‘know what and how’ procedure that results in an understanding of relationships and patterns. However, information by itself remains descriptive and without additional data or information it cannot be used to predict an event or outcome.

Information that moves further up the knowledge conduit may become knowledge. Again I like Davenport and Prusaks’ idea that knowledge is “a fluid mix of data, experience, practice, values, beliefs, standards, context, and expert insight that provides a conceptual arrangement for evaluating and incorporating new data, information and experiences”. Knowledge is therefore processed information in context and in action. It is at once descriptive, predictive and adaptive and can be applied to many situations. Information only becomes knowledge after it has been examined and compared to other information or data, and is then applied to describe, predict or adapt to a situation. A ‘know how and why’ enrichment occurs with the addition of further context, experience and understanding, to result in an understanding of principles. The statement ‘If the steel-mill’s smelter temperature is set at 2,000 degrees Celsius, then all the iron in the smelter will melt in 30 minutes.’ represents knowledge, because it is both predictive and descriptive, has context, and demonstrates understanding.

In this model, I have depicted knowledge as a cycle between tacit knowledge and explicit knowledge, aided by the codification and diffusion processes. Explicit knowledge is clearly articulated, theoretically making it available to all and sundry. I like Tiwana’s (2002) definition of tacit knowledge as 'personal context-specific knowledge that is difficult to formalise, record, or articulate'. I think Nonaka’s (2000) definition - "the understanding held by an individual that is derived from the integration of values, perceptions, opinions and personal beliefs with experience and information, which allows it to be employed effectively" – is also useful.

I have placed tacit knowledge first on the ‘tacit to explicit cycle’ for a reason. If indeed knowledge is a synthesis of information, further context, experience and understanding, and I submit it is, then that synthesis has to occur somewhere. Synthesis occurs in the human brain, and can occur nowhere else because understanding and experience are psychological phenomena. If the result of that synthesis can be codified into text, images, or pre-processed code, and action is taken to articulate it, then tacit knowledge becomes explicit. If the synthesis can be codified but isn’t the knowledge is implicit tacit knowledge. The point is that information first has to be synthesised and then codified in order to metamorphose into explicit knowledge, hence tacit knowledge comes first.

The knowledge conduit also has feedback loops from knowledge to information and data. These may be labelled osmosis and diffusion. Diffusion is well defined in the literature in a concept known as Codification and Diffusion Theory, or CD Theory (Boisot 1994). CD Theory posits that providing the receivers are receptive increasing codification will result in increasing diffusion of knowledge, until it becomes public or general knowledge. This is illustrated below.

The process of diffusion can result in explicit knowledge being added to tacit knowledge, to produce yet more tacit knowledge that may, or may not be codified. Equally, it may disseminate to the data or information level in the descriptive domain, and then given new context, experience and understanding transform into tacit knowledge.

On the other hand, osmosis occurs when it is either not possible to codify the tacit knowledge, or the implicit component has yet to be codified. Osmosis therefore is an intangible human transfer of knowledge from one person to one or more others, such as a master musician may pass to a student. As for diffusion it may disseminate to the data or information level in the descriptive domain, and then given new context, experience and understanding transform into tacit knowledge.

Now I know this is all a bit simple, but the model seems to work. I also know that knowledge is at once both tacit and explicit, but a model is abstract representation to aid understanding. I would welcome comments.

Regards, Graham

References: 

Boisot, M 1994, Information and organisations: the manager as anthropologist, Harper Collins, London.

Davenport, TH & Prusak, L 1998, Working knowledge: how organisations manage what they know, Harvard Business School Press, Boston.

Tiwana, A 2002, The knowledge management toolkit : orchestrating IT, strategy, and knowledge platforms, Prentice Hall, Upper Saddle River.