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A data warehouse (DW) is a database used for reporting. The data is offloaded from the operational systems for reporting. The data may pass through an Operational Data Store (ODS) for additional operations before it is used in the DW for reporting.
A data warehouse maintains its functions in three layers: staging, integration and access. A principle in data warehousing is that there is a place for each needed function in the DW. The functions are in the DW to meet the users' reporting needs. Staging is used to store raw data for use by developers (analysis and support).
The integration layer is used to integrate data and to have a level of abstraction from users. The access layer is for getting data out for users.
This definition of the data warehouse focuses on data storage. The main source of the data is cleaned, transformed, catalogued and made available for use by managers and other business professionals for data mining, online analytical processing, market research and decision support. 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.
Data warehousing arises in an organization's need for reliable, consolidated, unique and integrated analysis and reporting of its data at different levels of aggregation.
The practical reality of most organizations is that their data infrastructure is made up by a collection of heterogeneous systems.
For example, an organization might have one system that handles customer-relationship, a system that handles employees, systems that handle sales data or production data, yet another system for finance and budgeting data, etc. In practice, these systems are often poorly or not at all integrated and simple questions like: "How much time did sales person A spend on customer C, how much did we sell to Customer C, was customer C happy with the provided service, did Customer C pay his bills?" can be very hard to answer, even though the information is available "somewhere" in the different data systems.
Another problem is that enterprise resource planning (ERP) systems are designed to support relevant operations. For example, a finance system might keep track of every single stamp bought; When it was ordered, when it was delivered, when it was paid and the system might offer accounting principles (like double entry bookkeeping) that further complicates the data model. Such information is great for the person in charge of buying "stamps" or the accountant trying to sort out an irregularity, but the CEO is definitely not interested in such detailed information, the CEO wants to know stuff like "What's the cost?", "What's the revenue?", "Did our latest initiative reduce costs?" and wants to have this information at an aggregated level.
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