Data Modeling Development Cycle

1. Gathering Business Requirements – First Phase:

Data Modelers have to interact with business analysts to get the functional requirements and with end users to find out the reporting needs.

2. Conceptual Data Modeling (CDM) – Second Phase:

This data model includes all major entities, relationships and it will not contain much detail
about attributes and is often used in the INITIAL PLANNING PHASE. Please refer the diagram below and follow the link to learn more about Conceptual Data Modeling Tutorial.

Conceptual Data Modeling - Example diagram:

3. Logical Data Modeling (LDM) – Third Phase:

This is the actual implementation of a conceptual model in a logical data model. A logical data model is the version of the model that represents all of the business requirements of an organization. Please refer the diagram below and follow the link to learn more about Logical Data Modeling Tutorial.

In the example, we have identified the entity names, attribute names, and relationship. For detailed explanation, refer to relational data modeling.

4. Physical Data Modeling (PDM) – Fourth Phase:

This is a complete model that includes all required tables, columns, relationship, database properties for the physical implementation of the database. Please refer the diagram below and follow the link to learn more about Physical Data Modeling Tutorial.

Example of Physical Data Mode

5. Database – Fifth Phase:

DBAs instruct the data modeling tool to create SQL code from physical data model. Then the SQL code is executed in server to create databases.

 

Data Modeling Standards | Modeling Data

Data Modeling standardization has been in practice for many years and the following section highlight the needs and implementation of the data modeling standards.

Standardization Needs | Modeling data:

Several data modelers may work on the different subject areas of a data model and all data modelers should use the same naming convention, writing definitions and business rules.

Nowadays, business to business transactions (B2B) are quite common, and standardization helps in understanding the business in a better way. Inconsistency across column names and definition would create a chaos across the business.

For example, when a data warehouse is designed, it may get data from several source systems and each source may have its own names, data types etc. These anomalies can be eliminated if a proper standardization is maintained across the organization.

Table Names Standardization:

Giving a full name to the tables, will give an idea about data what it is about. Generally, do not abbreviate the table names; however this may differ according to organization’s standards. If the table name’s length exceeds the database standards, then try to abbreviate the table names. Some general guidelines are listed below that may be used as a prefix or suffix for the table.

Examples:

Lookup – LKP – Used for Code, Type tables by which a fact table can be directly accessed.
e.g. Credit Card Type Lookup – CREDIT_CARD_TYPE_LKP

Fact – FCT – Used for transaction tables:
e.g. Credit Card Fact – CREDIT_CARD_FCT

Cross Reference – XREF – Tables that resolves many to many relationships.
e.g. Credit Card Member XREF – CREDIT_CARD_MEMBER_XREF

History – HIST – Tables the stores history.
e.g. Credit Card Retired History – CREDIT_CARD_RETIRED_HIST

Statistics – STAT – Tables that store statistical information.
e.g. Credit Card Web Statistics – CREDIT_CARD_WEB_STAT

Column Names Standardization:

Some general guidelines are listed below that may be used as a prefix or suffix for the column.

Examples:

Key – Key System generated surrogate key.
e.g. Credit Card Key – CRDT_CARD_KEY

Identifier – ID – Character column that is used as an identifier.
e.g. Credit Card Identifier – CRDT_CARD_ID

Code – CD – Numeric or alphanumeric column that is used as an identifying attribute.
e.g. State Code – ST_CD

Description – DESC – Description for a code, identifier or a key.
e.g. State Description – ST_DESC

Indicator – IND – to denote indicator columns.
e.g. Gender Indicator – GNDR_IND

Database Parameters Standardization:

Some general guidelines are listed below that may be used for other physical parameters.

Examples:

Index – Index – IDX – for index names.
e.g. Credit Card Fact IDX01 – CRDT_CARD_FCT_IDX01

Primary Key – PK – for Primary key constraint names.
e.g. CREDIT Card Fact PK01- CRDT-CARD_FCT_PK01

Alternate Keys – AK – for Alternate key names.
e.g. Credit Card Fact AK01 – CRDT_CARD_FCT_AK01

Foreign Keys – FK – for Foreign key constraint names.
e.g. Credit Card Fact FK01 – CRDT_CARD_FCT_FK01