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Data Mining VS Data Warehousing

By SiliconIndia   |   Thursday, 28 June 2012, 11:44 Hrs
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Bangalore: Often business and technical staffs get confused with data mining and data warehousing. Though, both aim at providing a mean to understand large data, the main difference between them lies in their purpose, their methods and system designs.  



Data Mining aims at explaining an array of business methods that develop patterns from data. With the increasing amount of data, data mining tools are important to study the data and converting them into business intelligence. Data mining tools are widely used in analysis, detection of fraudulent activities apart from marketing. The key purpose of data mining is to identify patterns in the data. The significance of the output is highly dependent on the data used. If the data is limited to users in a specific geographical area, it will not depict broader and more general information.



On the other hand, Data Warehousing analyzes data but with a different set of goals. Data Warehousing when used in the retail segment, it is more interested in identifying the purchases which are accepted by the customers. The data warehouse tool is divided into data extraction, table and query management and data reliability. It acts as storage for transactional data. The type of data in the warehouse differs as it is highly dependent on the focus of the organization. For adding data to the warehouse, an ETL (Extraction, Transform and Load) tool is used. The data may need to be modified in order to match the data warehouse structure. Data loading in warehousing is critical as the connections with other databases needs to be maintained to ensure flexibility.  For accuracy, information in a warehouse is maintained in tables which need to be validated regularly. Faults in the table will lead to inaccurate reports.



Data Mining studies the correlations between the data and the various patterns to accept a hypothesis. However, Data Warehousing extracts and stores data for easy reporting.


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