Planning a Data Warehouse
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I dedicate this article to the persons who requested an article on planning a data warehouse in response to my article "Accessing OLAP using ASP.NET."This article starts with an introduction to data warehouse and further proceeds to the design-process of a data warehouse and concludes by giving some best practices.
Click this link to access the article "Accessing OLAP using ASP.NET."
Introduction
Databases store information about business transactions, plus other data such as employee records. Those types of systems are called online transaction processing (OLTP) databases. OLTP data contains a wealth of information that can help you make informed decisions about your business. The process of analyzing your data for that type of information, and the data that results, are collectively called Business Intelligence (BI).
A Data Warehouse is an enterprise-wide solution for data collection and analysis to meet the requirements of Decision Support Systems. A Data Warehouse is a complete architecture, and as such, requires a rigorous, yet iterative design approach and development methodology to ensure successful deployment. A data warehouse stores current and historical data from disparate operational systems (i.e., transactional databases) into one consolidated system where data is cleansed and restructured to support data analysis.
The first step in building a successful data warehousing application is to identify the specific information-based problems that are causing the organization the most amount of pain. These typically include:
- The inability to extract data from multiple disparate data sources and resolve differences in data definitions
- A lack of high quality data on which to base critical business decisions
- No "single version of the truth" for business rules and data definitions
- Inability to share consistent information across business divisions
- A proliferation of non-integrated, "stovepipe" applications
- The inability to generate consolidated and reconciled financial and other business reports
Data warehousing technology is ideally suited to solving all of these problems and more. Data integration platforms can be used to access a wide range of heterogeneous data sources, resolve the inconsistencies between these sources of data, and populate target databases.
Business intelligence (BI) tools and analytic applications may be used to access the target databases to support query, reporting, and analysis of the data. Metadata, generated and maintained by the data integration platform, may be used to create a centrally managed definition of business rules and entity definitions. And it is now possible to integrate data warehousing solutions with real-time systems-including real-time click stream analysis, real-time analytic applications, and EAI infrastructures-to drive real-time business responsiveness.
Next: Power of Meta Data >>
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