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The advantages and disadvantages of data warehouses
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| Data is extracted, transformed from multiple
data sources and loaded (ETL) into a separate database, called a
data warehouse. 
Figure 1: Data warehouse.
Advantages
Data warehouses tend to have a very high query
success as they have complete control over the four main areas
of data management systems:
 | Clean data |
| Indexes: multiple types |
| Query processing: multiple options |
| Security: data and access |
Disadvantages
However, there are considerable disadvantages
involved in moving data from multiple, often highly disparate,
data sources to one data warehouse that translate into long
implementation time, high cost, lack of flexibility, dated
information, and limited capabilities:
| Major data schema transforms from each of
the data sources to one schema in the data warehouse, which
can represent more than 50% of the total data warehouse
effort |
| Data owners lose control over their data,
raising ownership (responsibility and accountability),
security and privacy issues |
| Long initial implementation time and
associated high cost |
| Adding new data sources takes time and
associated high cost |
| Limited flexibility of use and types of
users - requires multiple separate data marts for multiple
uses and types of users |
| Typically, data is static and dated |
| Typically, no data drill-down
capabilities |
| Difficult to accommodate changes in data
types and ranges, data source schema, indexes and queries |
| Typically, cannot actively monitor
changes in data |
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