A semantic layer is an abstraction layer that sits between the end-user and the underlying data sources. Its main purpose is to simplify the process of data access and analysis by providing a logical view of the data that is more closely aligned with the business perspective.

The semantic layer achieves this by defining a set of business-oriented concepts, such as customers, products, and sales, and mapping these concepts to the corresponding data elements in the underlying data sources. It also provides a unified and consistent view of the data, regardless of the underlying data source, which helps to avoid confusion and inconsistencies that can arise from different data models and structures.

The semantic layer is typically implemented using a combination of tools and technologies, such as data integration, data modelling, and metadata management tools. It can be designed and developed by data architects and business analysts, who work together to define the business concepts and rules that will be used to create the semantic layer. See our enterprise class modelling tool, Boston. 

Once the semantic layer is in place, end-users can access the data through a variety of reporting and analysis tools, such as dashboards, scorecards, and ad-hoc reporting tools, such as FactEngine. They can interact with the data using the business concepts and terminology that are familiar to them, without having to worry about the underlying data structures and technical details.

Semantic layer technology is becoming a key component of a modern data warehousing architecture, helping to bridge the gap between the technical and business worlds, enabling organizations to make better use of their data to drive business insights and decisions.