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How do data warehousing and OLAP relate to data mining?
Data warehouses and data marts are used in a broad area of applications. Business executives use the data in data warehouses and data marts to implement data analysis and create strategic decisions. In some firms, data warehouses are used as an integral element of a plan-execute-assess “closed-loop” feedback system for enterprise administration.
Data warehouses are used widely in banking and financial services, consumer goods and retail distribution sectors, and controlled manufacturing, including demand-based production. Generally, the longer a data warehouse has been in use, the more it will have developed. This evolution takes place throughout various phases.
Initially, the data warehouse is generally used for generating documents and answering predefined queries. It can be used to analyze summarized and detailed information, where the results are displayed in the form of documents and charts. Later, the data warehouse is used for strategic objectives, implementing multidimensional analysis and sophisticated slice-and-dice operations.
Finally, the data warehouse can be employed for knowledge discovery and strategic decision-making using data mining tools. In this framework, the tools for data warehousing can be classified into access and retrieval tools, database documenting tools, data analysis tools, and data mining tools.
Business users required to have the means to understand what exists in the data warehouse (through metadata), how to create the contents of the data warehouse, how to test the contents using analysis tools, and how to display the results of such analysis.
There are three kinds of data warehouse applications such as information processing, analytical processing, and data mining.
Information processing − It provides querying, basic statistical analysis, and documenting using crosstabs, tables, charts, or graphs. A latest trend in data warehouse data processing is to create low-cost Web-based accessing tools that are then unified with Web browsers.
Analytical processing − It provides basic OLAP operations, involving slice-and-dice, drill-down, roll-up, and pivoting. It usually operates on historical information in both summarized and detailed structure. The major strength of online analytical processing over data processing is the multidimensional data analysis of data warehouse information.
Data mining − It provides knowledge discovery by finding hidden patterns and associations, building analytical models, implementing classification and prediction, and displaying the mining results using visualization tools.
Data mining contains more automated and deeper analysis than OLAP, data mining is expected to have wider software. Data mining can support business managers find and reach more appropriate users, and gain critical business insights that can support drive market share and raise profits.
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