A semantic
layer is basically a group of pre-defined virtual views or models which
represent a business enterprise's data that are represented in recognizable and
important terminologies rather than 100% technical fields. It is usually a
business translation layer which assembles between end users and the database
and insulates the end users by the technical details and difficult database structure.
By having a semantic layer as your
reporting tool, business users cannot see the data like a collection of
technical tables with difficult inter-relations, rather as a simple collection
of well-known business strings systematized into a significant structure. It gets
a lot simpler for users to access, control, systematize and evaluate the data.
Without requiring learning a data
language or recognizing where exactly the data is being stored and what rules will
be appropriate to them, users have a self-service competence to generate a massive
assortment of different questions and reports themselves.
Explore the Key
Benefits Semantic layers Has Over the Traditional Reporting Tools:
- Business Users Create the Reports: Generally, the report authors are the business users and they devise and generate the reports on the basis of their specific business needs to meet certain analytical or management requirements. Generally, valuable reports are created on the basis of complex data sources which make use of lots of tables and data joins to draw the information, but business users might lack the necessary knowledge and professional dexterities on databases. Eventually, they might not make use of the most resourceful join as the reports need join data and this leads to poor performance. By having a semantic layer, developers can build views as per a predefined business situation and create the joins among tables or data sources. Therefore, the business users only need to focus on designing and creating reports that meet their business needs. Additionally, it gets simple for the developers to recognize and maintain the reports which are generated by others.
- Betterment of Result Accuracy & Constancy between Reports: A semantic layer assures the accuracy and exclusivity of the report outcomes. No matter what tools the end users make use of to consume it or while the end users require running the reports which are generated on the basis of it, semantic layer will constantly return the similar and consistent outcomes to the reports, given that it is being used in the same database. The outcome is an enhancement in the efficiency, accuracy and uniformity between the reports. This is helpful not just for the business users; however to all users that require to generate reports.
- Recycle Of Data Elements: In the traditional approaches, data elements are consumed in every individual report which is generated by making use of them. While these reports run, a massive volume of unnecessary work is generated. What is bad is that while the data elements require being updated or customized, every individual report requires being changed consequently for making the elements successful. In a semantic layer, recycle views are generated through developers. These recycle views are used to comprise other views which represent difficult business situations. While there is a requirement for updating or changing any data, just the relevant recycle views require being altered; there is no requirement to modify the individual reports.
- Better Safety through Role Division: Semantic layers enable the administrators or developers to arrange the equivalent authorizations for users as per their business responsibilities. Users can just access the models or views for that they have the authorizations. In this manner, the business enterprise's data and data can be protected by the users or user groups.
- Flexibility & Sufficiency of View Transportability: Semantic layers offer the skill to export the views which are organized on it. These views could be exported as .xmlfiles and then gets imported in a semantic layer on the basis of the other databases. This avoids additional human resources and material assets must be spent on generating the similar models and views for various databases.
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