Sunday, June 10, 2007

Tacit and Explicit Knowledge?

In order to understand the Knowledge Management, we need to understand the two main components or pillars of knowledge, Implicit or Tacit and Explicit Knowledge.

Tacit knowledge is more commonly used term for implicit knowledge. Tacit knowledge is what the expert knows, which is derived from experience and embodies beliefs and values. It is the root or base of generation of new knowledge. The most important aspect of knowledge management is ability to convert the tacit knowledge into explicit knowledge.

Explicit knowledge is represented as document, content which is created with sole purpose of sharing or communicating with another person. It is knowledge which is coded and stored in some form of repository. The most common form of explicit knowledge are documents, files, images, audios and videos.

Both forms of knowledge are important from knowledge management's perspective and effectiveness.

We will discuss key functional areas of Knowledge Management in subsequent posts. After discussing key functional areas of Knowledge Management, we will come back to the "ability to convert the tacit knowledge into explicit knowledge".

What is data, information, knowledge, wisdom?

Why do we use so many terms for same thing: data, information, knowledge etc? Do all terms have same meaning or are these semantically different terms not understood correctly by majority of people? It would be best if we understand these terms ourselves and then make a decision.

We are talking about four terms: Data, Information, Knowledge and Wisdom.

Data is set of records. Data represents a fact or statement of event, a raw data with no knowledge of relationship. For examples, records of customer.

Information is when we attach semantic to the data. Information wraps understanding of relation in conext of the subject. If we are able to answer on who, what, when, where questions. For example, information about current customers, information about new customers.

Knowledge is when we attach intelligence to the information. If we are able to infer "how" about the information. For example, how many customer have cancelled the accounts in current fiscal year? How many leads were converted into new customers?

Wisdom: philosophical definition says that wisdom consists of making the best use of available knowledge. Wisdom embodies more of an understanding of fundamental principles embodied within the knowledge that are essentially the basis for the knowledge being what it is.

So as we move up from data to wisdom, we go up a level of maturity. We add more semantics and intelligence to add value to raw data to make it more useful.

Then how is Data Management different from Information Management and Knowledge Management. Why are we not talking about Wisdom? Is Wisdom not required in all other aspects?

We will talk deeper into knowledge in subsequent posts and try to understand more about explict and implicit knowledge.