Data, Information and Knowledge

Davenport and Prusak, in Working Knowledge (2000), possibly the best introduction to Knowledge Management, say that knowledge, data and information are not identical concepts. In their clearly written account, they point out that that confusion between the three has resulted in many organizations investing large amounts of money in the technology of knowledge management without achieving any useful results. They consider that understanding the difference between the three concepts is crucial:

"Organizational success and failure can often depend on which one of them you need, which you have, and what you can and can't do with each. Understanding what these three things are and how you get from one to another is essential to doing knowledge work succesfully."

Data is hard, factual information often in numerical form - it can tell you when, and how often something happened, how much it cost and so on but it does not say why it happened. Organizations love accumulating vast quantities of data - the sheer bulk of which serves to confuse and obscure any value.

Information for Davenport and Prusak comes in the form of a message - and it is the receiver rather than the sender of the message who determines that it is information - through some communication channel whether voice, e-mail, letter, etc. It is different from data in that it has meaning or shape. In fact, data can be transformed into information with the addition of meaning and they list a number of ways (each beginning with C):

Contextualized - the purpose of the data is known;

Categorized - the unit of analysis or key component is known;

Calculated - perhaps through a statistical or mathematical analysis;

Corrected - through the removal of errors;

Condensed - by being summarized or tabulated.

Knowledge transcends both data and information in a number of ways.

Knowledge Management

Tacit and Explicit Knowledge

Knowledge Management Practice

The Psychology of Expertise

Working Knowledge