Data Management: Data Warehousing and Data Mining

Data Management: Data Warehousing and Data Mining

The future of data warehousing

We are moving to the stage of a data ware housing applications that can provide information to many decision makers operational, strategic, and tactical and also to the customers as well in an integrated fashion (Mattison 2009).

Data Mining

Data mining is primarily used today by companies with a strong consumer focus - retail, financial, communication, and marketing organizations. It enables these companies to determine relationships among "internal" factors such as price, product positioning, or staff skills, and "external" factors such as economic indicators, competition, and customer demographics. And, it enables them to determine the impact on sales, customer satisfaction, and corporate profits. Finally, it enables them to "drill down" into summary information to view detail transactional data. With data mining, a retailer could use point-of-sale records of customer purchases to send targeted promotions based on an individual's purchase history.

By mining demographic data from comment or warranty cards, the retailer could develop products and promotions to appeal to specific customer segments. For example, Blockbuster Entertainment mines its video rental history database to recommend rentals to individual customers. American Express can suggest products to its cardholders based on analysis of their monthly expenditures. WalMart is pioneering massive data mining to transform its supplier relationships. WalMart captures point-of-sale transactions from over 2,900 stores in 6 countries and continuously transmits this data to its massive 7.5 terabyte Teradata data warehouse. WalMart allows more than 3,500 suppliers, to access data on their products and perform data analyses. These suppliers use this data to identify customer buying patterns at the store display level. They use this information to manage local store inventory and identify new merchandising opportunities. In 1995, WalMart computers processed over 1 million complex data queries (Becker 2002).

Designing the data warehouse

The following discussion outlines the process of the data warehouse design. It involves the logical design, the OLAP design, and Data mining design. The logical Design The logical design of data-warehouse is defined by the dimensional data modeling approach. The dimensioning design process followed in this project adheres to the methodology described by Mattison (2009). Dimensional data modeling approach. To minimize the join operations which slow down queries, normalization is not the guiding principle in DW design. A schema is a collection of database objects, including tables, views, indexes, and synonyms. There is a variety of ways ...