The semantic layering for enterprise data has existed for as long as BI products themselves. Individual teams first employed business intelligence tools, and each tool within an organization had its semantic layer. Data silos and modest data volumes dominated during that time.
Need for a universal semantic layer:
Over time, however, the data volume increased dramatically, creating a new BI system that could handle large amounts of data and serve multiple users at once.
In this system, all data must be translated into a single language called semantic that both humans and machines can understand. As such, it’s very important to know a semantic type to understand how it works in practice.
As enterprises grew in size and complexity, they realized they needed a common layer that would allow them to integrate disparate data sets into one unified view of their business. This led to the creation of ETL (extract, transform, load) tools that helped businesses move data from one area to the other so that it could be analyzed across different perspectives.
However, these ETL tools were insufficient for enterprises with multiple systems with different data formats and schemas. To solve this problem, companies started building proprietary connectors between systems that allowed them to move data from one system to the other without having to modify it in any way.
But this approach led to an explosion in cost because each company had its own connectors that needed maintenance and support staff; also, these connectors were limited in terms of functionality and scalability.
This is where Universal Layer comes into play; it provides a single point where all ETL processes are executed. Universal Layer connects all the pieces of your data, making it possible to see what’s happening with your data in real-time. By providing a single point where all ETL processes are executed, Universal Layer allows you to see what’s happening with your data in real-time and make decisions based on that information.
Benefits of using a universal semantic layer:
A semantic model is required to resolve the above-mentioned issue. It’s a method of classifying and arranging data so that users can quickly discover it and formulate queries without being aware of the data’s underlying structure.
A semantic model is used to define the structure of your data. It provides an ontology that describes the meaning of each field in your database. A semantic model makes it easier for users to understand what they are looking at and helps them formulate queries that get the necessary information.
It enables you to make changes more easily because you can change how the data is organized without having to update each individual query or application that uses your data source.
One of the key challenges in building a BI solution is ensuring consistency across all data sources. The problem is that different business users in your organization tend to use different BI tools and create semantic models of their own such as measures, dimensions, and hierarchies.
You can ensure consistency across all data sources by giving users the freedom to create semantic models of their own on top of a universal semantic layer. This will allow you to build a unified governance framework for your BI environment while still giving business users full control over their own data.