Logical data models and database models are essential components of technology today. The following lists data modeling standards and guidelines and outlines some data modeling principles.

Conceptual Data Modeling (CDM)

• CDM consists of data entities and their relationships.
• CDM describes key business information by subject area from a data perspective.
• CDM should be divided into subject areas of manageable size. In practical terms, this means a model usually has between 4 and 15 entities per subject area.
• Every subject area must have a unique title.

Logical Data Modeling (LDM) Standards and Guidelines

• A corresponding logical data model also has associative classes to resolve many-to-many relationships, is fully attributed, and is normalized to Third Normal Form (3NF).
• If a CDM was used as a foundation for adding details to develop a logical data model, then the non-specific relationship line between entities will be replaced with identifying or non-identifying relationships.
• A LDM also shows all native (that is, non-foreign key) primary key attributes and non-key attributes in the attribute area.
• A fully attributed logical data model will be in Third Normal Form (3NF). This means that each entity instance has exactly one unique record. All non-key attributes fully depend on primary key attributes, and no non-key attributes depend on any other non-key attributes.
• Depending on the particular logical data modeling methodology and tool used, there are a number of acceptable ways to indicate cardinality or multiplicity on the ends of relationships between two equally important entities.

Physical Data Model (PDM) Standards and Guidelines

• Designate a unique primary key column for every table.
• Each column name should contain all of the elements of the logical attribute from which it was derived, but should be abbreviated to fit within the maximum length.
• Do not use hyphens in table or column names because some programming languages interpret hyphens as subtraction operators.
• Implement table and column names in a way that is supported by all target DBMS tools.
• The physical model will assign lengths and data types to all columns. Data types should be specific to the target DBMS tool.
• The physical data model will, at a minimum, provide examples of possible values for identifier, indicator, and code columns.
• A certain amount of demoralization is usually necessary when implementing the physical data model.
• Estimate the expected storage requirements for each table based on the size of each row, expected growth, number of rows, and archiving requirements.
• Understand the capabilities of the specific database product. Performance improvements may be realized by taking advantage of features such as clustered indices, caching, and index optimization.

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