MBA (General) - IV Semester, Information Technology and E-Business, Unit 2.1
Architecture for Data Mining
To best apply these advanced techniques, they must be fully integrated with a data warehouse as well as flexible interactive business analysis tools. Many data mining tools currently operate outside of the warehouse, requiring extra steps for extracting, importing, and analyzing the data. Furthermore, when new insights require operational implementation, integration with the warehouse simplifies the application of results from data mining. The resulting analytic data warehouse can be applied to improve business processes throughout the organization, in areas such as promotional campaign management, fraud detection, new product rollout, and so on. Figure illustrates an architecture for advanced analysis in a large data warehouse.
Architecture for Data Mining
To best apply these advanced techniques, they
must be fully integrated with a data warehouse as well as flexible interactive
business analysis tools. Many data mining tools currently operate outside of
the warehouse, requiring extra steps for extracting, importing, and analyzing
the data. Furthermore, when new insights require operational implementation,
integration with the warehouse simplifies the application of results from data
mining. The resulting analytic data warehouse can be applied to improve
business processes throughout the organization, in areas such as promotional
campaign management, fraud detection, new product rollout, and so on. Figure
illustrates an architecture for advanced analysis in a large data warehouse.
The ideal starting point is a data warehouse
containing a combination of internal data tracking all customer contact coupled
with external market data about competitor activity. Background information on
potential customers also provides an excellent basis for prospecting. This
warehouse can be implemented in a variety of relational database systems
Sybase, Oracle, Redbrick, and so on, and should be optimized for flexible and
fast data access.
An OLAP (On-Line Analytical Processing) server
enables a more sophisticated end-user business model to be applied when
navigating the data warehouse. The multidimensional structures allow the user
to analyze the data as they want to view their business – summarizing by
product line, region, and other key perspectives of their business.
The Data Mining Server must be integrated with
the data warehouse and the OLAP server to embed ROI-focused business analysis
directly into this infrastructure.
An advanced, process-centric metadata template
defines the data mining objectives for specific business issues like campaign
management, prospecting, and promotion optimization. Integration with the data
warehouse enables operational decisions to be directly implemented and tracked.
As the warehouse grows with new decisions and results, the organization can
continually mine the best practices and apply them to future decisions.
This design represents a fundamental shift from
conventional decision support systems. Rather than simply delivering data to
the end user through query and reporting software, the Advanced Analysis Server
applies users’ business models directly to the warehouse and returns a
proactive analysis of the most relevant information.
These results enhance the metadata in the OLAP
Server by providing a dynamic metadata layer that represents a distilled view
of the data. Reporting, visualization, and other analysis tools can then be
applied to plan future actions and confirm the impact of those plans.
