Common Data Modeling is defining the unifying the structure used in allowing heterogeneous business environments to interoperate. A Common Data Model is very critical to a business organization.

Especially with today’s business environment where it is common to have multiple applications, a Common Data Model seamless integrates seemingly unrelated data into useful information to give a company a competitive advantage over its competitors. Data Warehouses make intensive use data models to make companies have a real update on how the business is faring.

In Common Data Modeling, Business Architects and analysts need to face the data first before defining a common data or abstraction layer so that they will not be bound to a particular schema and thus make the Business Enterprise more flexible.

Business Schemas are the underlying definition of all business related activities. Data Models are actually instances of Business Schemas – conceptual, logical and physical schemas. These schemas have several aspects of definition and they usually form a concrete basis for the design of Business Data Architecture.

Data Modeling is actually a vast field but having a Common Data Model for a certain domain can answer problems with many different models operating in a homogeneous environment. To make Common Data Models, modelers need to focus on one standard of Data Abstraction. They need to agree on certain elements to be concretely rendered so uniformity and consistency is obtained.

Generic patterns can be used to attain a Common Data Model. Some of these patterns include using entities such as "party" to refer to persons and organizations, or "product types", "activity type", "geographic area" among others. Robust Common Data Models explicitly include versions of these entities.

A good approach to Common Data Modeling is to a have a generic Data Model which consists of generic types of entity like class, relationships, individual thing and others. Each instance of these classes can have subtypes.
Common Data Modeling process may obey some these rules:

1. Attributes are to be treated as relationships with other entities.
2. Entities are defined under the very nature of a Business Activity, rule, policy or structure but not the role that it plays within a given context.
3. Entities must have a local identifier in an exchange file or database. This identifier must be unique and artificial but should not use relationships to be part of the local identifier.
4. Relationships, activities and effects of events should not be represented by attributed but by the type of entity.
5. Types of relationships should be defined on a generic or high level. The highest level is defined as a relationship between one individual thing with another individual thing.

Data Modeling often uses the Entity-Relationship Model (ERM). This model is a representation of structured data. This type of Data Modeling can be used to describe any ontology (the term used to describe the overview and classification of terms and their respective relationships) for a certain area of interest.

Other techniques used in Data Modeling include the family of ICAM Definition Languages (IDEF), Object Role Modelling or Nijssen’s Information Analysis Method, Relational Model/Tasmania (RM/T), Object-relational mapping, EBNF Grammars, Bachman diagrams, Barker’s Notation and Business rules or business rules approach.