Database Reference
In-Depth Information
The Data Management Association's (www.dama.org) definition of data
management is the development and execution of architectures, policies,
practices, and procedures that properly manage the full data life-cycle
needs of an enterprise. Disciplines in data management include:
1. Data modeling
2. Database administration
3. Data warehousing
4. Data movement
5. Data mining
6. Data quality assurance
7. Data security
8. Metadata management (data repositories and their management)
9. Strategic data architecture
There is a difference between data and information. Data is stored in
multiple applications systems on multiple platforms using multiple meth-
ods and is used to perform day-to-day operations. If this distributed data
is grouped together in a meaningful format, it can provide valuable infor-
mation to business organizations and their decision makers.
Data is captured using online transaction processing (OLTP) systems
to perform mission-critical daily operations. Typically, many users simul-
taneously add, modify, delete, and view data using OLTP applications.
OLTP systems are characteristically designed to perform transactions one
record at a time.
Information is derived from online analytical processing (OLAP) sys-
tems used for analysis, planning, and management reporting through
access to a variety of sources. An OLAP system usually references infor-
mation that is stored in a data warehouse. Use of this technology enables
the facility to present a comprehensive view of the enterprise.
Data and information are extremely valuable assets. Data architecture
defines an infrastructure for providing high-quality, consistent data to be
used as the basis for decision support and executive information services
as well as traditional transaction applications statewide. Data architecture
defines all the components, interfaces, and processes for implementing
and managing an integrated, cohesive data policy. These components are
defined in the following subsections.
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