The data warehouse structure includes the older level of detail (usually on alternate, bulk storage), the current level of detail, lightly summarized data (data mart level), and highly summarized data. Data flows from the operational environment to the data warehouse. Typically significant data transformations occur on the journey from the operational level to the data warehouse level.
When data has been stored for a long time, the data passes the current detail to the older details. As long as the data are summarized, the data passes the current detail to lightly summarized data, then from lightly summarized data to highly summarized data.
Current Detail Data
Current detail data is data that can be updated at a certain time so that the accuracy of the data is valid. Examples are sales details of the year
1990-1991.
Older Data Details
When the data is old then the data will move from current detail data to the older data details. Older detail data usually use an alternative storage media or also called bulk storage. For example sales details from 1984-1989.
Lightly Summarized Data
Lightly summarized data is a detailed data that has been summarized but this data can not yet become the basis of managerial decision making because its nature has not fully final summary. For example sales reports per week based on subproducts from 1984-1992. The capacity of lightly summarized data is less than the detailed data available because of the different levels of detail that can be accessed.
Highly Summarized Data
Highly summarized data is data summarized from lightly summarized data that is already an overall summary. This data has a high degree of granularity in the data warehouse that can help DSS analyst/end users to define and find information used for corporate decision making. Examples are monthly sales reports by product from 1981-1992.
Metadata
The most important component in the data warehouse is the metadata or data about the data, has become part of the social environment information process since the existence of programs and data. However, the metadata in the data warehouse world brings to the new level of importance of providing the most effective use of data warehouses. Metadata allows the user / DSS analyst to navigate through various possibilities.
Metadata is used for various purposes including the following:
1. The loading and extraction process - metadata is used to map the data source into a common overview of data in the data warehouse.
2. Warehouse management process - metadata used to automate the production of summary tables.
3. Part of the query-metadata management process is used to redirect the query to the most appropriate data source.
When data has been stored for a long time, the data passes the current detail to the older details. As long as the data are summarized, the data passes the current detail to lightly summarized data, then from lightly summarized data to highly summarized data.
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Current detail data is data that can be updated at a certain time so that the accuracy of the data is valid. Examples are sales details of the year
1990-1991.
Older Data Details
When the data is old then the data will move from current detail data to the older data details. Older detail data usually use an alternative storage media or also called bulk storage. For example sales details from 1984-1989.
Lightly Summarized Data
Lightly summarized data is a detailed data that has been summarized but this data can not yet become the basis of managerial decision making because its nature has not fully final summary. For example sales reports per week based on subproducts from 1984-1992. The capacity of lightly summarized data is less than the detailed data available because of the different levels of detail that can be accessed.
Highly Summarized Data
Highly summarized data is data summarized from lightly summarized data that is already an overall summary. This data has a high degree of granularity in the data warehouse that can help DSS analyst/end users to define and find information used for corporate decision making. Examples are monthly sales reports by product from 1981-1992.
Metadata
The most important component in the data warehouse is the metadata or data about the data, has become part of the social environment information process since the existence of programs and data. However, the metadata in the data warehouse world brings to the new level of importance of providing the most effective use of data warehouses. Metadata allows the user / DSS analyst to navigate through various possibilities.
Metadata is used for various purposes including the following:
1. The loading and extraction process - metadata is used to map the data source into a common overview of data in the data warehouse.
2. Warehouse management process - metadata used to automate the production of summary tables.
3. Part of the query-metadata management process is used to redirect the query to the most appropriate data source.
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