Introduction

The knowledge management literature provides many definitions of knowledge, most of which build the concept from data, to information, to knowledge. Some of the literature even takes this one step further and expands knowledge to understanding and wisdom (Ackoff 1989; Kannegieter 2001; Stewart 1999); however there is little agreement for a precise definition of knowledge (Biggam 2001, p. 2; Håkanson 2001, p. 3). Unfortunately data and information are often used interchangeably, and information and knowledge are used as synonyms.

Data

Data is typically thought of as being ‘a set of discrete, objective facts existing in symbolic form that have not been interpreted’ (Davenport & Prusak 1998, pp. 2-3), but which can be ‘shaped and formed to create information’ (Laudon & Laudon 1998, p. 16. 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. When data is enriched by adding context it may become information.

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Information is data that have been shaped by humans into a meaningful and useful form.

Laudon & Laudon

Confusion about what data, information, and knowledge are – how they differ, what the words mean – has resulted in enormous expenditures on technology initiatives that rarely deliver what the firms spending the money needed or thought they were getting.

Tom Davenport

Time, like the term knowledge, is an elusive concept. Like knowledge, a definition of time that is satisfactory to everyone has defied the greatest minds from Antiphon to Newton, and on to Einstein and Planck. Yet time is pervasive across cultures, and at least in the Western world, drives much of what we do. Time is also intrinsically linked to knowledge management, and provides some insights as to why knowledge management is so difficult.

Take for example the common platitude –“just in time knowledge management”. I take this to mean that the right information (I have chosen my words carefully) should be available to decision-makers at the right time and in the right place, and not before or after the time it is actually required! Now this begs all sorts of questions, like: ...

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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.

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There is a good deal of criticism of the data, information, knowledge, wisdom model of knowledge, which is sometimes called the DIKW hierarchy but I prefer to call it the ‘pyramid to wisdom’. Most of the criticism says the model is too simple. I wonder, however, if the model has some use. As usual it is useful to return to source documents.

In knowledge management circles Russell Ackoff is usually credited as the originator of the hierarchy, and indeed published two seminal papers, the first in 1989. However Milan Zeleny published a paper two years before Ackoff, and Harlan Cleveland published a paper in 1982. Both of these authors mention the hierarchy and provide examples. ...

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