A logical data model is an important aspect in the design and implementation of a data warehouse in that the efficiency of the databases depends heavily on data models.

Logical Data Model refers to the actual implementation of a conceptual module in a database. It represents normalized design of common data model which is required to support the design of an information system.

The logical data model elaborates the representation all data pertaining to the organization and this model organizes the enterprise data in management technology jargon.

In 1975 when the American National Standards Institute (ANSI) first introduced the idea of a logical schema for data modeling, there were only two choices that time which were the hierarchical and network models.

Today there are three choices for logical data model and these choices are relational, object oriented and Extensible Markup Language (XML). The relational option defined the data model in terms of tables and columns. The object oriented option defines data in terms of classes, attributes and associations. Finally, the XML option defined data in terms of tags.

The logical data model is based closely on the conceptual data model which describes all business semantic in natural language without pointing any specific means of technical implementation such as the use of hardware, software or network technologies.

The process of logical data modeling could be a labor intensive technique depending on the size of the enterprise the data model will be used for. The resulting logical data model represents all the definition, characteristics, and relationships of data in a business, technical, or conceptual environment. In short, logical data modeling is about describing end-user data to systems and end-user staff.

The very core of the logical data model is the definition of the three types of data objects which the building blocks of the data model and these data objects are the entities, attributes, and relationships. Entities refer to persons, places, events or things which are of particular interest to the company.

Some examples of entities are Employees, States, Orders, and Time Sheets. Attributes refer to the properties of the entities. Examples of attributes for the Employee entity are first name, birthday, gender, address, age and many others. Lastly, relationships refer to the way where in the entities relate to each other.  An example relationship would be "customers purchase products" or "students enroll in classes". 

The above mentioned example is a logical data model using the Entity-Relationship (ER) model which identifies entities, relationships, and attributes and normalize your data.
A logical data model should be carefully designed because it will have tremendous impact on the actual physical implementation of the database and the larger data warehouse.

A logical data model influenced the design of data movement standards and techniques such as the heavily used extract, transform and load (ETL) process in data warehousing and the enterprise application integration (EAI), degree of normalization, use of surrogate keys and cardinality.

Likewise, it will determine the efficiency in data referencing and in managing all the business and technical metadata and the metadata repository. Several pre-packaged third party business solutions like enterprise resource planning (ERP) or HR systems have their own logical data model and when they are integrated into the overall existing enterprise model with a well designed logical data model, the implementation may turn out to be easier and less time consuming resulting in saving of money for the company.