A semantic data model is a method of organizing and representing corporate data that reflects the meaning and relationships among data items. This method of organizing data helps end users access data autonomously using familiar business terms such as revenue, product, or customer via the BI (business intelligence) and other analytics tools. The use of a semantic model offers a consolidated, unified view of data across the business allowing end-users to obtain valuable insights quickly from large, complex, and diverse data sets.
What is the purpose
of semantic data modeling in BI and data virtualization?
A semantic data model sits between a reporting tool and the original database in order to assist end-users with reporting. It is the main entry point for accessing data for most organizations when they are running ad hoc queries or creating reports and dashboards. It facilitates reporting and improvements in various areas, such as:
No relationships or joins for end-users to worry about because they’ve already been handled in the semantic data model
Data such as invoice data, salesforce data, and inventory data have all been pre-integrated for end-users to consume.
Columns have been renamed into user-friendly names such as Invoice Amount as opposed to INVAMT.
The model includes powerful time-oriented calculations such as Percentage in sales since last quarter, sales year-to-date, and sales increase year over year.
Business logic and calculations are centralized in the semantic data model in order to reduce the risk of incorrect recalculations.
Data security can be incorporated. This might include exposing certain measurements to only authorized end-users and/or standard row-level security.
A well-designed semantic data model with agile tooling allows end-users to learn and understand how altering their queries results in different outcomes. It also gives them independence from IT while having confidence that their results are correct.
In the classic Software Development Life Cycle (SDLC) process, Data Models are typically initiated, by model type, at key process steps and are maintained as data model detail is added and refinement occurs.
The Concept Data Model (CDM) is, usually, created in the Planning phase. However, creation the Concept Data Model can slide forwarded or backward, somewhat, within the System Concept Development, Planning, and Requirements Analysis phases, depending upon whether the application being modeled is a custom development effort or a modification of a Commercial-Off-The-Shelf (COTS) application. The CDM is maintained, as necessary, through the remainder of the SDLC process.
The Logical Data Model (LDM) is created in the Requirement Analysis phase and is a refinement of the information entities of the Concept Data Model. The LDM is maintained, as necessary, through the remainder of the SDLC process.
The Physical Data Model (PDM) is created in the Design phase to facilitate the creation of accurate detail technical designs and actual database creation. The PDM is maintained, as necessary, through the remainder of the SDLC process.