This classic definition of the data warehouse focuses on data storage. However, the means to retrieve and analyze data, to extract, transform and load data, and to manage the data dictionary are also considered essential components of a data warehousing system. Many references to data warehousing use this broader context. Thus, an expanded definition for data warehousing includes business intelligence tools, tools to extract, transform, and load data into the repository, and tools to manage and retrieve metadata.
In contrast to data warehouses are operational systems which perform day-to-day transaction processing.
Benefits of data warehousing
Some of the benefits that a data warehouse provides are as follows: [2][3]
- A data warehouse provides a common data model for all data of interest regardless of the data's source. This makes it easier to report and analyze information than it would be if multiple data models were used to retrieve information such as sales invoices, order receipts, general ledger charges, etc.
- Prior to loading data into the data warehouse, inconsistencies are identified and resolved. This greatly simplifies reporting and analysis.
- Information in the data warehouse is under the control of data warehouse users so that, even if the source system data is purged over time, the information in the warehouse can be stored safely for extended periods of time.
- Because they are separate from operational systems, data warehouses provide retrieval of data without slowing down operational systems.
- Data warehouses can work in conjunction with and, hence, enhance the value of operational business applications, notably customer relationship management (CRM) systems.
- Data warehouses facilitate decision support system applications such as trend reports (e.g., the items with the most sales in a particular area within the last two years), exception reports, and reports that show actual performance versus goals.
[edit] Data warehouse architecture
Architecture, in the context of an organization's data warehousing efforts, is a conceptualization of how the data warehouse is built. There is no right or wrong architecture. The worthiness of the architecture can be judged in how the conceptualization aids in the building, maintenance, and usage of the data warehouse.
One possible simple conceptualization of a data warehouse architecture consists of the following interconnected layers:
- Operational database layer
- The source data for the data warehouse - An organization's ERP systems fall into this layer.
- Informational access layer
- The data accessed for reporting and analyzing and the tools for reporting and analyzing data - Business intelligence tools fall into this layer. And the Inmon-Kimball differences about design methodology, discussed later in this article, have to do with this layer.
- Data access layer
- The interface between the operational and informational access layer - Tools to extract, transform, load data into the warehouse fall into this layer.
- Metadata layer
- The data directory - This is often usually more detailed than an operational system data directory. There are dictionaries for the entire warehouse and sometimes dictionaries for the data that can be accessed by a particular reporting and analysis tool.
Bottom-up design
Ralph Kimball, a well-known author on data warehousing, [4] is a proponent of the bottom-up approach to data warehouse design. In the bottom-up approach data marts are first created to provide reporting and analytical capabilities for specific business processes. Data marts contain atomic data and, if necessary, summarized data. These data marts can eventually be unioned together to create a comprehensive data warehouse. The combination of data marts is managed through the implementation of what Kimball calls "a data warehouse bus architecture".[5]
Business value can be returned as quickly as the first data marts can be created. Maintaining tight management over the data warehouse bus architecture is fundamental to maintaining the integrity of the data warehouse. The most important management task is making sure dimensions among data marts are consistent. In Kimball words, this means that the dimensions "conform".
[edit] Top-down design
Bill Inmon, one of the first authors on the subject of data warehousing, has defined a data warehouse as a centralized repository for the entire enterprise.[5] Inmon is one of the leading proponents of the top-down approach to data warehouse design, in which the data warehouse is designed using a normalized enterprise data model. "Atomic" data, that is, data at the lowest level of detail, are stored in the data warehouse. Dimensional data marts containing data needed for specific business processes or specific departments are created from the data warehouse. In the Inmon vision the data warehouse is at the center of the "Corporate Information Factory" (CIF), which provides a logical framework for delivering business intelligence (BI) and business management capabilities. The CIF is driven by data provided from business operations
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