Data Warehousing and Data Mining in Business
Data warehousing and data mining have emerged as key technologies and essential components of modern decision support systems. Strengths and weaknesses and success factors are considered and practical steps are provided to help organisations implement successfully.
Technique Overview
Data Warehousing and Data Mining in Business Definition
Data warehousing is a subject-oriented, integrated, non-volatile, and time variant collection of data that supports management’s decision making processes (Inmon, 1996). Data mining is the process that uses statistical, mathematical, artificial intelligence and machine-learning techniques to extract and identify useful information and subsequently gain knowledge from large databases or data warehouses (Turban et al., 2007). Both data warehousing and data mining are being applied in a wide range of businesses including retail, finance, manufacturing, transportation and aerospace.
Data Warehousing and Data Mining in Business Description *
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Business Evidence
Strengths, weaknesses and examples of Data Warehousing and Data Mining in Business *
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Business Application
Implementation, success factors and measures of Data Warehousing and Data Mining in Business *
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Professional Tools
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Further Reading
Data Warehousing and Data Mining in Business web and print resources *
Data Warehousing and Data Mining in Business references (4 of up to 20) *
- Berry, M., and Linoff, G. (2000) Mastering data mining. Wiley, Hoboken, NJ.
- Bolton, R. J., and Hand, D. J. (2002) Statistical fraud detection: a review. Statistical Science, Vol.17(3), pp. 235-255.
- Cabena, P., Hadjinian, P., Stadler, R., Verhees, J., and Zanasi, A. (1998) Discovering data mining: from concept to implementation. Prentice Hall, Upper Saddle River, NJ.
- Cao, L., Yu, P.S., Zhang, C., and Zhang, H. (2009) Data mining for business applications. Springer.
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