How Data Mining, Data Warehousing and On-line Transactional Databases are helping solve the Data Management predicament.
How Data Mining, Data Warehousing and On-line Transactional Databases are helping solve the Data Management predicament.
Robert Bialczak
Walden University
How Data Mining, Data Warehousing and On-line Transactional Databases are helping solve the Information Management predicament. Data in itself can be powerful, but also has many pitfalls if left to disparate databases and data collection routines. A collection of spreadsheets with account numbers entered into them can be view as a business liability. This same information in a database that can be queried, secured, organized and related to other data for analytical purposes becomes a power business tool. It takes “big data” and makes it business intelligence.
Data Mining As Seifert states, “data mining does not entail just the collection and management of data but it also consists of analysis and prediction”, this capability can solve the many legacy problems of data being in multiple locations across the organization, only small portions of the data being deemed relevant for specific decisions and the need for external data in making decisions by having a system that ties these disparate and discontinuous data sets into meaningful decision aids. As these depositories are tied together and executed properly, ensuring that data in differing formats can be resolved correctly, the results can be powerful business tools. Anderson-Lehman et al., reports that “companies can leverage data mining techniques to improve customers’ loyalty through market segmentation, understand what their competitors are doing, forecast sales, monitor business performance, and detect fraud, waste, and abuse”. This example shows that data collected and properly analyzed is then transformed into useful and powerful business information. Inmon and Valente state that although data mining as a tool is powerful, “textual data represents the most abundant and common data in enterprise today, yet it is also the least
Robert Bialczak
Walden University
How Data Mining, Data Warehousing and On-line Transactional Databases are helping solve the Information Management predicament. Data in itself can be powerful, but also has many pitfalls if left to disparate databases and data collection routines. A collection of spreadsheets with account numbers entered into them can be view as a business liability. This same information in a database that can be queried, secured, organized and related to other data for analytical purposes becomes a power business tool. It takes “big data” and makes it business intelligence.
Data Mining As Seifert states, “data mining does not entail just the collection and management of data but it also consists of analysis and prediction”, this capability can solve the many legacy problems of data being in multiple locations across the organization, only small portions of the data being deemed relevant for specific decisions and the need for external data in making decisions by having a system that ties these disparate and discontinuous data sets into meaningful decision aids. As these depositories are tied together and executed properly, ensuring that data in differing formats can be resolved correctly, the results can be powerful business tools. Anderson-Lehman et al., reports that “companies can leverage data mining techniques to improve customers’ loyalty through market segmentation, understand what their competitors are doing, forecast sales, monitor business performance, and detect fraud, waste, and abuse”. This example shows that data collected and properly analyzed is then transformed into useful and powerful business information. Inmon and Valente state that although data mining as a tool is powerful, “textual data represents the most abundant and common data in enterprise today, yet it is also the least
References: Anderson-Lehman, R., Watson, H.J., Wixom, B.H., & Hoffer, J. A. (2004). Continental Airlines flies high with real-time business intelligence. MIS Quarterly Executive, (3)4, pp. 163- 176. Inmon, B., & Valente, G. (2010). A peek into the future: The next wave of data warehousing. Enterprise Systems. Retrieved January 19, 2014 from http://esj.com/articles/2010/02/03/next-wave-dw.aspx Inmon, W.H. Building the Data Warehouse, 3rd edition. Wiley, New York, 2002 Inmon, W. Metadata in the data warehouse, White Paper, 2000; www.inmoncif.com/library/whiteprs/earlywp/ttmeta.pdf. Kimball, R., Reeves, L., Ross, M., and Thronthwaite, W. The Data Ware- house Lifecycle Toolkit. Wiley, New York, 1998. Kimball, R. and Ross, M. The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling, 2nd edition, Wiley, New York, 2002. Seifert, J.W. (2006). Data mining: An overview. CRS Report for Congress. Retrieved January 19, 2014 from http://www.au.af.mil/au/awc/awcgate/crs/rl31798.pdf Yang, F., Shanmugasundaram, J., and Yerneni, R. 2009. A scalable data platform for a large number of small applications. In Proceedings of the 4th Biennial Conference on Innovative Data Systems Research. .