Subject Oriented :
A data warehouse can be used to analyze a particular subject area. For example, "sales" can be a particular subject.
A data warehouse integrates data from multiple data sources. For example, source A and source B may have different ways of identifying a product, but in a data warehouse, there will be only a single way of identifying a product.
Time Variant :
Historical data is kept in a data warehouse. For example, one can retrieve data from 3 months, 6 months, 12 months, or even older data from a data warehouse. This contrasts with a transactions system, where often only the most recent data is kept. For example, a transaction system may hold the most recent address of a customer, where a data warehouse can hold all addresses associated with a customer.
Once data is in the data warehouse, it will not change. So, historical data in a data warehouse should never be altered.
At Ingen we take pride in our Data warehouse process. This is a combination of Identifying Subject areas and mapping to an internal build data model. Data model is dynamic and has the basis on FSLDM (Financial Services Logical Data Model). It is normalized so as to support report requirement and fast Data warehouse loads.
We also provide a powerful Scheduler (ScheduleIngen) and Auditing Platform (AuditIngen) to Monitor Loads and to find if there are any discrepancy. It also provides a daily or Adhoc success & failure reports which is utilised by management for monitoring. (References available on request)