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4.3 Data Warehouse Design and Usage 153

management includes controlling the number and range of queries, dimensions, and
reports; limiting the data warehouse’s size; or limiting the schedule, budget, or resources.
Various kinds of data warehouse design tools are available. Data warehouse
development tools provide functions to define and edit metadata repository contents
(e.g., schemas, scripts, or rules), answer queries, output reports, and ship metadata to
and from relational database system catalogs. Planning and analysis tools study the
impact of schema changes and of refresh performance when changing refresh rates or
time windows.

4.3.3 Data Warehouse Usage for Information Processing

Data warehouses and data marts are used in a wide range of applications. Business
executives use the data in data warehouses and data marts to perform data analysis
and make strategic decisions. In many firms, data warehouses are used as an integral
part of a plan-execute-assess “closed-loop” feedback system for enterprise management.
Data warehouses are used extensively in banking and financial services, consumer goods
and retail distribution sectors, and controlled manufacturing such as demand-based
production.
Typically, the longer a data warehouse has been in use, the more it will have evolved.
This evolution takes place throughout a number of phases. Initially, the data warehouse
is mainly used for generating reports and answering predefined queries. Progressively, it
is used to analyze summarized and detailed data, where the results are presented in the
form of reports and charts. Later, the data warehouse is used for strategic purposes, per-
forming multidimensional analysis and sophisticated slice-and-dice operations. Finally,
the data warehouse may be employed for knowledge discovery and strategic decision
making using data mining tools. In this context, the tools for data warehousing can be
categorized into access and retrieval tools, database reporting tools, data analysis tools, and
data mining tools.
Business users need to have the means to know what exists in the data warehouse
(through metadata), how to access the contents of the data warehouse, how to examine
the contents using analysis tools, and how to present the results of such analysis.
There are three kinds of data warehouse applications: information processing, analyti-
cal processing, and data mining.
Information processing supports querying, basic statistical analysis, and reporting
using crosstabs, tables, charts, or graphs. A current trend in data warehouse infor-
mation processing is to construct low-cost web-based accessing tools that are then
integrated with web browsers.
Analytical processing supports basic OLAP operations, including slice-and-dice,
drill-down, roll-up, and pivoting. It generally operates on historic data in both sum-
marized and detailed forms. The major strength of online analytical processing over
information processing is the multidimensional data analysis of data warehouse data.