Data Warehouses and Data Mining

This article gives a detailed summary of the role of data warehouses and data mining, and their relationship to organizational databases. As you read, pay attention to how data warehouses are used to improve decision-making in organizations. Keep a summary in your notes of how an organization you are involved with could benefit from data mining and data warehousing.

Data Mining Applications

Data mining applications have been successfully applied in many areas, such as:

  1. Financial Data Analysis: The financial data in the banking and financial industry is generally reliable and of high quality, which facilitates systematic data analysis and data mining. Some of the typical cases are loan payment prediction and customer credit policy analysis; classification and clustering of customers for targeted marketing; and detection of money laundering and other financial crimes.
  2. Retail Industry: Data mining in the retail industry helps in identifying customer buying patterns and trends that lead to improved quality of customer service and good customer retention and satisfaction. These include the design and construction of data warehouses based on the benefits of data mining; multidimensional analysis of sales, customers, products, time, and region; and customer Retention.
  3. Telecommunication Industry: Data mining in the telecommunication industry helps in identifying the telecommunication patterns, catch fraudulent activities, make better use of resources, and improve the quality of service. These include the multidimensional Analysis of Telecommunication data; fraudulent pattern analysis; identification of unusual patterns; and mobile telecommunication services.
  4. Biological Data Analysis: The following are aspects in which data mining contributes to biological data analysis: semantic integration of heterogeneous, distributed genomic, and proteomic databases; discovery of structural patterns; association and path analysis; and visualization tools in genetic data analysis.
  5. Other Scientific Applications: A large amount of data sets is being generated because of the fast numerical simulations in various fields such as climate and ecosystem modeling, chemical engineering, fluid dynamics, etc. These include data Warehouses and data preprocessing; graph-based mining; and visualization and domain-specific knowledge.