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  Data Warehouse-Data Mining

Data Warehouse or Data Mining:

Data Mining (DM):

Data mining, also called as "knowledge discovery," refers to techniques and computer-assisted tools for sifting through and analyzing these vast data stores in order to discover trends, patterns, and correlations that can guide decision making and enhanced understanding. Data mining covers a extensive variety of uses; from examine customer purchases to discovering galaxies. Essentially, data mining is the equivalent of searching gold nuggets in mountain of data. The monumental task of discovery of hidden gold depends heavily upon the power of computers.

Applications of Data Mining:

Data mining includes a variety of interesting applications. A few instances are listed below:

1) By recording the activity of shoppers in an online store, such like over time, Amazon.com, retailers can use knowledge of these patterns to develop the placement of items in the layout of a mail-order catalog page or Web page.

2) Telephone companies mine customer billing data to recognize customers who spend considerably more than average on their monthly phone bill. Then, the Company can aim these customers to sell extra services.

3) Marketers can target effectively the needs and wants of specific consumer groups by analyzing data regarding customer preferences and buying patterns.

4) Hospitals use data mining to recognize groups of people whose healthcare costs are probable to increase in the near future so that preventative steps can be taken.

Data Warehouse:

In computing, a data warehouse or enterprise data warehouse (DW, DWH, or EDW) is a database used for data analysis and reporting. It is a central repository of data that is created by integrating data through one or more disparate sources. This store current in addition to historical data and are used for making trending reports for senior management reporting such like quarterly and annual comparisons.

A data warehouse is integrated, subject-oriented, time-variant, and non-volatile collection of data in support of management's decision making process.

Subject-Oriented:

A data warehouse can be used to examine a particular subject area. For instance, "sales" can be a particular subject.

Integrated:

A data warehouse integrates all data from multiple data sources. For instance, source A and source B may have different ways of identify a product, but in a data warehouse, there will be just a single way of identify a product.

Time-Variant:

Historical data is kept in a data warehouse. For instance, one can retrieve data within 3 months, 6 months, 12 months, or even older data from a data warehouse. It contrasts with a transactions system, where frequently only the most recent data is kept. For instance, a transaction system may hold the most recent address of a customer, where a data warehouse can hold all of the addresses related with a customer.

Non-volatile:

Once data is in the data warehouse, it will not modify. So, in a data warehouse historical data should never be altered.

Difference between Data Mining and Data Warehouse:

The terms data mining and data warehousing are frequently confused by both technical staff and business staff. The whole field of data management has experienced a phenomenal growth along with the implementation of data collection software programs and the reduced cost of computer memory. The primary reason behind both these functions is to provide the tools and methodologies to explore the patterns and meaning in great amount of data.

The main differences between data warehousing and data mining are the system designs, methodology used, and the reason. Data mining is the use of pattern recognition logic to identity trends in a sample data set and extrapolate this information against the larger data pool. Data warehousing is the procedure of extracting and storing data to let easier reporting.

Data mining is a general term used to define a range of business processes that derive patterns from data. In general, a statistical analysis software package is utilized to identify specific patterns, depend on the data set and queries produced by the end user. A distinctive use of data mining is to create targeted marketing programs, recognize financial fraud, and to flag unusual patterns in behavior as part of a security review.

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