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Introduction
A model is a representation of reality, ‘real world‘ objects and events, associations in which
data is to be collected and stored in a database. A data model depicts the dataflow and
logical interrelationships among different data elements. In a nut shell, data models can be
defined as an integrated collection of concepts for describing and manipulating data, how
data is connected to each other, and how they are processed and stored inside the system.
Data models can be used for a variety of purposes, from high-level conceptual models to
physical data models. For example, in an organization, a data model would usually represent
the organization itself whereby it should provide the basic concepts and notations that will
allow database designers and end users to communicate their understanding of the
organizational data unambiguously and accurately.
Data modelling is the analysis of data objects and their relationships to other data objects. It
explores data-oriented structures and identifies entity types.
Data modelling is often the first step in database design and object-oriented programming as
designers first create a conceptual model of how data items relate to each other. This data
model is then the guide used by functional and technical analysts in the design and
implementation of a database. Data modelling involves a progression from conceptual model
to logical model to physical schema.
Types of Data Modelling
Three basic styles of data modelling are generally used in practice today, namely the
conceptual data models, logical data models, and physical data models.
A conceptual data model or conceptual schema is a summary-level data model that is most
often used on strategic data projects. It typically includes only the main concepts and the
main relationships among them. This describes the semantics of an organization and
represents a series of assertions about its nature. The model specifically identifies and
describes the things of significance to an organization, about which it is more inclined
towards when collecting information, as well as the attributes and interrelationship between
these things of significance.
Usually this is a first-cut model where there is insufficient detail to build an actual database,
however at this level, it describes the structure of the whole database so as to provide
understanding at high level what are the different entities in the data and how they relate to
one another. Because a conceptual schema represents the semantics of an organization,
and not a database design, it may exist on various levels of abstraction.
Logical data models (LDMs) also models information gathered from business requirements.
It is more complex than conceptual model in that column types are set. LDMs should be
based on the structures identified in a preceding conceptual data model, as this describes
the semantics of the information context, which the logical model ought to reflect as well.
Logical data models represent the abstract structure of a domain of information. It seeks to
describe the data in as much detail as possible without considering how the data will be
physically implemented in the database. The models are commonly diagrammatic in nature
and are most typically used in business processes with intent to capture things of importance
to an organization and how they relate to each other. LDM can become the basis of
a physical data model and form the design of a database after it is being validated.
