Managing Information Technology

102 Part I • Information Technology

(and, hence, inconsistently), and different databases will
receive data transferred from a common source. The sys-
tems that populate these databases, move data, and pro-
duce reports are described later in this chapter.
Besides the focus on management levels in Figure
4.3, there are other independent schemes for understanding
levels of data. One such scheme focuses on scope of influ-
ence for data, based on the following:

• LocalLocal data are those that have relevance to
  only a single user or small group of organization
  members. Local data generally do not need extensive
  control and do not need to follow organizational
  standards. Local data may have limited life and use,
  and it is acceptable that local data may be duplicated
  (and inconsistent) across the organization.


• SharedShared data are those that are exchanged
  between different user groups, and hence there must
  be agreements on the definition, format, and timing
  for exchange of these data among those sharing the
  data. Shared data exist because of a dependency
  between different organizational units or functions.


• CoreCore data are those that require an organization-
  wide definition and sourcing (in other words, core data
  is enterprise-shared data). There may be one copy of
  core data, but if there are multiple copies, the creation
  of these copies are carefully planned and managed.

The final scheme for levels of data we discuss is

based on the differences between data and metadata,

which is described by the following:

• PhysicalPhysical data exist in databases and other file
  systems. Critical issues for physical data are computer
  performance, accessibility, integrity, and security.


• LogicalLogical data is the view or understanding of
  data needed to use data. Logical data are presented to
  users via query languages and applications.
  Presenting data at a logical level promotes usability
  and proper interpretation of data.


• SemanticSemantic data are the metadata that describe
  organizational data. Critical issues for semantic data
  are clarity, consistency, and sharability.
  These and other schemes for considering levels of
  data are useful because they allow us to focus on specific
  aspects of data without having to consider all the aspects at
  once. However, conversations about data can be confusing
  or ambiguous if it is not clear what scheme is being used
  and what level is being discussed.

APPLICATION SOFTWARE SHOULD BE SEPARATE FROM

THE DATABASE One goal of data management is

application independence, the separation, or decoupling,

of data from applications systems. This concept, embodied

in Figure 4.3, is further illustrated in Figure 4.4.

Raw

Data

Security

Transfer

Capture

Data

Information

Factory

Long-term

Operational

Warehouse

Quality

Control

Analysis

and

Presentation

FIGURE 4.4 Categories of Information Processing with Application Independence