Databases Reference
In-Depth Information
stakeholders, quality concepts, improvement strategies, quality metrics, and
weightings.
14.2.1
Quality Factors
In the literature related to quality in data modeling, there exist a lot of quality
factors definitions. We list here the more relevant ones:
Completeness. Completeness is the ability of the data model to meet
all user information and functional requirements.
·
Correctness. Correctness indicates whether the model conforms to
the rules of the data modeling technique in use.
·
Minimality. A data model is minimal when every aspect of the
requirements appears once in the data model. In general, it is better
to avoid redundancies.
·
Normality. Normality comes from the theory of normalization asso-
ciated with the relational data model; it aims at keeping the data in a
clean, purified normal form.
·
Flexibility. Flexibility is defined as the ease with which the data
model can be adapted to changes in requirements.
·
Understandability. Understandability is defined as the ease with
which the concepts and structures in the data model can be under-
stood by users of the model.
·
Simplicity. Simplicity relates to the size and complexity of the data
model. Simplicity depends not on whether the terms in which
the model is expressed are well known or understandable but on the
number of different constructs required.
·
While it is important to separate the various dimensions of value from the
purposes of analysis, it is also important to bear in mind the interactions
among qualities. In general, some objectives will interfere or conflict with
each other; others will have common implications, or concur; and still others
will not interact at all.
14.2.2 Stakeholders
Stakeholders are people involved in building or using the data modelthere-
fore, they have an interest in its quality. Different stakeholders will generally
be interested in different quality factors.
