
4 Key Components of a Data Processing System There are four
basic components in a computer based data processing system. They are
Machines, programs, data and the people. These are termed in different
ways like hardware and software. A computer program defines a process
to be carried out on a machine. A program consists of a sequence of
statements or commands written in some programming language. A procedure
defines a process to be carried out by a person.
______________________________________________________________________________ Until recently the focus has been largely on the machine and the programs it executes, perhaps explaining the popularity of the terms hardware and software. Both the computer industry and using organizations have worked hard to develop better programming techniques. High level programming languages have been developed to such a degree that today the definition of a process can be quite machine independent. The problems of
data: Today, programs and programming languages are closer to people
and relatively independent of the machine, while data is closely tied
to both programs and the machine and not close to the people who need
it. Programs direct the movement and manipulation of data with in a
computer system. Since the mid1950s computer professionals has
striven to develop programming languages independent of machines and
geared to users. Yet they made relatively little efforts to do the same
for data. Desirable Relationships
in a data processing system: A database management
system has many uses: Functions of
Database Administration: Data is a vital
resource in an organization and must be managed. The organizational
database is an essential component in management information system.
Of the four components of a data processing system, attention to data
has lagged behind the development of machines and programming technology.
Taking a database approach requires an organization to focus on data
as a valued resource.
______________________________________________________________________________ | enterprise data management |data asset management | professional data management | productive data management |
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