What is Data Modeling?
to manage data modeling and want practical standards and best practices
for business intelligence governance and accountability?
A data model
is a graphical means of documenting data requirements
defining database design specifications. It is used by information
is structure and design?
structure and design discipline
focuses primarily on information modeling. The goal is to
direction on standards, best practices, and the use of conceptual,
logical, and physical models, naming standards, and tools.
- Confirm understanding of data
requirements with business owners; and
- Provide direction to technical teams who
must ultimately built the database structures
types of models are required?
The following models are required to
support business intelligence
model, is generally created at the
information management strategy
stage. It contains key entities and relationships and
high level look at all of the entities within an organization.
of the conceptual model as an architect’s conceptual drawing of a
house. It provides a good idea of what is required with very little
model, is a fully attributed entity
relationship diagram (ERD), which shows
entity, its relationship to other entitles and specifies the applicable
Model, represents facts and dimensions,
which are used for reporting and
analytical purposes. There is no logical representation of a
dimensional model. It is a “physical” model but sometimes it is shown
in a logical version in the modeling tool with English like
as opposed to physical names; and
data models created?
Model, is the final representation of the data
base structures that will be
generated from the model. It contains the detailed specifications for
the database design and, in a model driven environment, the modeling
tool will generate the data definition language (DDL)
that is used to create the database structure.
- The conceptual model is
created at the information management strategy stage;
- The logical model, and
conceptual dimensional model are created during the
requirements analysis phase; and
we always need a logical data model?
logical data model is a mandatory requirement for an on-line
transaction processing system. It is required to show the correct
business rules that need to be applied to the information.
- A physical model and
dimensional model are created during the architecture and design phase.
A logical model may not be required if the project only involves
movement. These types of information management projects
move data from several sources and consolidate it into one target
system. Although we may have interim data storage, it is transient in
nature and sometimes there is little to be gained by creating a
What standards and best practices are required?
be created as part of the
strategy. This model is part of the business model and
shows what classes of information are required to support
management and business intelligence purposes;
logical model (EDM) should be created
as part of the
management framework. This model should eventually contain
all entities and their relationships and a complete set of
- An application logical model
created during project requirements
- This should be based on the enterprise
conceptual model created in the information management strategy phase;
model should show what information is required to support the specific
application. If new entities or definitions are discovered during
project requirements analysis, they should be added to the
models should be created during
and design phase and should be:
- Based on the logical model;
- Created for each environment that will be
used by the application; and
- Used to generate data definition language
(DDL) for each database
- Environment models should be created
for each data base environment; and
- The model should generate the data
definition language (DDL) used to create database schema's and objects.
model should specify the data base
design and should always be synchronized 100% with the
and best practices are required as part of the business intelligence to ensure rapid project delivery and
optimal return on information management investment.