Successful data warehouses ultimately become part of the operations of the business. Although these do occur from time to time, it is the every day and periodic company activities, that seek to track the business and detect anomalies, which are their mainstay. Most ad hoc queries originate as a result of finding anomalies in the business and then seeking to understand their causes. The majority of the time spent in building a data warehouse is consumed in requirements definition, product selection, understanding the data and ensuring its quality. These processes are still largely manual efforts, with many products focused on reducing implementation effort rather than reducing requirements gathering effort.
Data quality is critical to a user's willingness to use the data warehouse.
Attention to data quality often produces a side benefit of improving the data quality of the operational source systems as well. This area often consumes significantly more time and effort than what is required to implement transformation and loading processes. Many warehouse projects, with especially dirty data sources, fail when they are unable to adequately address data quality problems within budget constraints. Ease of use is also critical to a user's willingness to use the data warehouse. Users must be able to quickly find the information they need, and data warehouses must accommodate a wide audience of users from sophisticated analysts who want to execute ad hoc queries, to users who have no interest in understanding the data and simply want standard reports.