A data warehousing is defined as a technique for collecting and managing data from varied sources to provide meaningful business insights. It is a blend of technologies and components which aids the strategic use of data.
A data warehouse is a database, which is kept separate from the organization's operational database. It possesses consolidated historical data, which helps the organization to analyze its business. However, the data warehouse is not a product but an environment. It is an architectural construct of an information system which provides users with current and historical decision support information which is difficult to access or present in the traditional operational data store.
The data warehouse (DW) acts as a central repository of information originating from one or more data sources. Data flows from transactional systems and other relational databases to the data warehouse and generally consist of structured, semi-structured, and unstructured data. This data is loaded, processed and consumed on a regular basis.
The main features of data warehousing can be summarized as follows:
*Easy access to nonskilled computer users.
*Data integration based on a model of the enterprise.
*Flexible querying capabilities to take advantage of the information assets.
*Synthesis, to enable targeted and effective analysis.
*Multidimensional representation to give the user an intuitive and handy view of information.
*Correctness, completeness, and freshness of information.
There are the three types of data warehouse applications.
*Information Processing – A data warehouse allows to process the data stored in it. The data can be processed by means of querying, basic statistical analysis, reporting using crosstabs, tables, charts, or graphs.
*Analytical Processing – A data warehouse supports analytical processing of the information stored in it. The data can be analyzed by means of basic OLAP (online analytical processing) operations, including slice-and-dice, drill down, drill up, and pivoting.
*Data Mining - Data mining supports knowledge discovery by finding hidden patterns and associations, constructing analytical models, performing classification and prediction. These mining results can be presented using visualization tools.
Data warehouse technologies are successfully used:
• Trade Sales and claims analyses, shipment and inventory control, customer care and public relations
• Craftsmanship Production cost control, supplier and order support
• Financial services Risk analysis and credit cards, fraud detection
• Transport industry Vehicle management
• Telecommunication services Call flow analysis and customer profile analysis
• Health care service Patient admission and discharge analysis and bookkeeping in accounts departments
Data warehouse
*Easy access to nonskilled computer users.
*Data integration based on a model of the enterprise.
*Flexible querying capabilities to take advantage of the information assets.
*Synthesis, to enable targeted and effective analysis.
*Multidimensional representation to give the user an intuitive and handy view of information.
*Correctness, completeness, and freshness of information.
There are the three types of data warehouse applications.
*Information Processing – A data warehouse allows to process the data stored in it. The data can be processed by means of querying, basic statistical analysis, reporting using crosstabs, tables, charts, or graphs.
*Analytical Processing – A data warehouse supports analytical processing of the information stored in it. The data can be analyzed by means of basic OLAP (online analytical processing) operations, including slice-and-dice, drill down, drill up, and pivoting.
*Data Mining - Data mining supports knowledge discovery by finding hidden patterns and associations, constructing analytical models, performing classification and prediction. These mining results can be presented using visualization tools.
Data warehouse technologies are successfully used:
• Trade Sales and claims analyses, shipment and inventory control, customer care and public relations
• Craftsmanship Production cost control, supplier and order support
• Financial services Risk analysis and credit cards, fraud detection
• Transport industry Vehicle management
• Telecommunication services Call flow analysis and customer profile analysis
• Health care service Patient admission and discharge analysis and bookkeeping in accounts departments
Data warehouse
