Knowledge discovery 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).

Business organizations can profit greatly from mining the Web. There are three types of uses for Web data mining

• Web Structure Mining
• Web Usage Mining
• Web Content Mining

Web Structure Mining examines how the Web documents are structured and attempts to discover the model underlying the link structures of the Web. This is useful to categorize Web pages, and to generate relationships and similarities among Web sites.

Web Usage Mining, also known as clickstream analysis, involves identification of patterns in user navigation through Web pages in a domain. Web Usage Mining tries to discover knowledge about the Web surfer's behaviors through analysis of their interactions with the Web site including the mouse clicks, user, queries, and transactions. Web Usage Mining includes three main tasks: preprocessing, pattern discovery and pattern analysis.

Web Content Mining is used to discover what a Web page is about and how to uncover new knowledge from it.

Customer relationship management (CRM) is the mechanisms and technologies used to manage the interactions between a company and its customers. Database marketers were the early adopters of CRM software, in order to automate the process of customer interaction.

CRM implementations can be characterized as being operational and/or analytical. Operational CRM includes sales force automation and call centers. Most global companies have implemented such systems. The goaö of operational CRM is to provide a single view and point of contact for each customer. Analytical CRM uses data mining techniques to uncover customer intelligence that serves to better understand and serve the customer.

However, although many of the DM techniques have been around more than ten years for scientific applications, only in the past few years have we witnessed the emergence of solutions that consolidate multiple DM techniques in a single software offering. One of the most significant barriers to the explosion of the use of knowledge discovery in organizations relates to the fact that still today implementing a data mining model is still considered an art.

Knowledge Discovery by Semantic Technology	Text Mining and Knowledge Discovery with Knewco	Microsoft Data Mining Demo -- Forecasting

 

Basic source for this text is: Becerra-Fernandez, I. and Sabherwal, R. (2010). Knowledge Management: Systems and Processes. Armonk (N.Y.); London : M.E. Sharpe.