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.