At the advent of the semantic layer, it was most sought after for the deployment and programing of any complicated and advanced business intelligence system. The current advancements in the universal semantic layer have started finding more profound applications post-deployment of a business intelligence system. Here are the primary needs of a semantic layer for modern businesses.
What is a Semantic Layer?
The world of sophisticated and technologically advanced business is governed by many business objects. A business object can be a metric, an attribute, a dimension. These business objects are deployed to make understanding complex data like tables, schema, and columns easier for an end-user by storing them in a back-end database.
These predefined business objects that break down the data complexity for the users of the data are called a semantic layer. Building a semantic layer can be tedious and time-consuming. All businesses considering deploying a business intelligence system need to revolve around the awareness that implementing a semantic layer is time-consuming and will hamper the implementation process.
Need for Semantic Layer:
A massive amount of data is not processed only by the technical IT team in a large enterprise. Even the non-technical teams are also exposed to complex data sets like ad hoc queries. If a business plans to make each user of these complex data self-sufficient to understand the data and derive conclusions, then the non-technical personals would need a support system to simplify and assist them with these data. A semantic layer provides this vital support. Semantic layers are most valuable to the following set of users:
Top Decision Makers: A top decision-maker or a superuser is generally a high-level business professional who deals with a large amount of data and is not from a technical background. These users may have a keen inclination to business intelligence tools and want to create ad-hoc reports with high accuracy to assist them in making business decisions that perfectly align with their business goals.
The semantic layer provides technical assistance and guidance for such users by exporting all the data complexity to the back-end and providing a simplified system to create a parameterized report or build an interactive dashboard.
Federated Business Intelligence Architecture: A semantic layer can be advantageous if the business has deployed a federated business intelligence architecture. With such a system, a data analyst or a typical power user has to frequently query a fixed data set from more than one source to cater to a specific business application—for example, query historical data from an e-commerce inventory or any other syndicated data feed. For quick and accurate data and reporting of such data, a semantic layer can be helpful and can provide an optimal return on investment.
Unanimous Development Platform: After a business intelligence tool is deployed and the teams have adapted well to the system, naturally, few technical bottlenecks will appear. These bottlenecks are addressed by the technical IT professionals of each team, who are in charge of constructing data marts and simplified reports for the team.
Before addressing these bottlenecks across the corporation, the business intelligence team must define certain data standards, procedures, metric definitions, and project management, which will remain standard and unanimous across all the development teams. A universal semantic layer ensures that these predefined parameters are unchanged and consistent when the development teams are working on the bottlenecks.
A semantic layer can be a versatile facility in a business intelligence environment where the focus is on ad-hoc queries and custom reporting. The semantic layer is the best way to export all the data complexity to the back end and facilitate large and complex data understanding to non-technical users. A semantic layer ensures data accuracy in reporting when dealing with fixed set data pulled from multiple sources.