Knowledge discovery mechanisms and technologies can facilitate socialization and combination within or across organizations.

Knowledge Creation Systems can be enabled by the use of data mining (DM) technologies.

Technologies to discover knowledge can be very powerful for organizations.

Knowledge discovery in databases (KDD) is the process of finding and interpreting pattern from data, involving the application of algorithms to interpret the pattern generated by these algorithms (Fayaad et al, 1996, as cited in Becerra-Fernandez and Sabherwal, 2010). Another name for KDD is data mining.

The increasing availability of computing power and integrated DM software tools, which are easier than ever to use, have contributed to the increasing popularity of DM applications in business. Over the last decade, data mining techniques have been applied across business problems.

Examples of data mining applications are as follows:

• Marketing - Predictive DM techniques, like artificial neural networks (ANN), have been used for target marketing including market segmentation. This allows the marketing departments using this approach to segment customers according to basic demographic characteristics such as gender, age group, as well as their purchasing patterns. They have also been used to improve direct marketing campaigns through an understanding of which customers are likely to respond to new products based on their previous consumer behavior.
• Insurance - DM techniques have been used for segmenting customer groups to determine premium pricing and to predict claim frequencies. Clustering techniques have also been applied to detecting claim fraud and to aid in customer retention.
• Operations management - neural networks have been used for planning and scheduling, project management, and quality control (Becerra-Fernandez and Sabherwal, 2010).