Some researchers consider data mining as a component of knowledge management; however, it is needed to understand what activities are common and what activities are different in both.
Data Mining is a process of discovering knowledge form available stored data. It requires some activities like:
data collection
data cleaning
integration
selection
transformation
drill (use of intelligent data processing methods in order to obtain, among others, rules, patterns, relationships)
verification (interpretation of results)
presentation of knowledge
Knowledge Management is considered as a strategy for company; it defines which tools can provides information relevant to the company's business in a way that improves staff efficiency and competitiveness. KM is an organized process which requires data collecting, verification, data storage and spreading knowledge to particular actors in company. KM also is a system that helps organizations to acquire, analyse the use (re-use) of knowledge in order to make faster, smarter and better decisions. It defines also the knowledge flows in the enterprise and actors (employees) who are taking part in it. But the problem of KM is how to change esoteric knowledge (hidden, not available for everybody) in exoteric knowledge (popular, accessible, codified, formal).
According to by Headland, knowledge management includes such elements as::
knowledge creation
representations
storage
transmission
processing
application
protection of organizational knowledge.
An important element is to analyse the correlation between implicit and explicit knowledge. Both of these types of knowledge are three forms:
Data mining is a powerful new technology with great potential to help companies focus on the most important information in the data they have collected about the behavior of their customers and potential customers whereas knowledge management is simply refers to a multi-disciplined approach to achieving organisational objectives by making the best use of knowledge.