Data Warehouse

A data warehouse is a

• Subject-oriented
• Integrated
• Time-varying
• Non-volatile

collection of data that is used primarily in organizational decision making.

Subject Oriented -

           Data warehouses are designed to help you analyze data. For example, to learn more about your company's sales data, you can build a warehouse that concentrates on sales. Using this warehouse, you can answer questions like "Who was our best customer for this item last year?" This ability to define a data warehouse by subject matter, sales in this case, makes the data warehouse subject oriented.

Integrated -

   Integration is closely related to subject orientation. Data warehouses must put data from disparate sources into a consistent format. They must resolve such problems as naming conflicts and inconsistencies among units of measure. When they achieve this, they are said to be integrated.

Time-varying -

     In order to discover trends in business, analysts need large amounts of data. This is very much in contrast to online transaction processing (OLTP) systems, where performance requirements demand that historical data be moved to an archive. A data warehouse's focus on change over time is what is meant by the term time variant.

Non-volatile -

    Nonvolatile means that, once entered into the warehouse, data should not change. This is logical because the purpose of a warehouse is to enable you to analyze what has occurred.

Need for Data Warehousing ?

• Better business intelligence for end-users
• Reduction in time to locate, access, and analyze information
• Consolidation of disparate information sources
• Strategic advantage over competitors
• Faster time-to-market for products and services
• Replacement of older, less-responsive decision support systems
• Reduction in demand on IS to generate reports

Why Separate Data Warehouse?

missing data: Decision support requires historical data which operational DBs do not typically maintain

data consolidation:  DS requires consolidation (aggregation, summarization) of data from heterogeneous sources: operational DBs, external sources

data quality: different sources typically use inconsistent data representations, codes and formats which have to be reconciled.