A semantic layer provides a consolidated view of data that enables everyone across an enterprise to understand the past better and predict the future. It bridges the gap between Business Intelligence (BI) users and data science teams, enabling enterprises to move from simple historical analysis to accurate predictions.
Complex data relationships are mapped to easy-to-understand business terms by the semantic layer. Consequently, business users and data scientists do not have to depend on other teams. This enables teams to collaborate and work transparently with the same information and goals.
A semantic layer simplifies the complexity of underlying raw data using a business model. This allows any consumer data to access quantitative metrics, attributes, features, predictions, business hierarchies, and complex calculations in an intuitive, easy-to-understand interface. A semantic layer solution presents this consumer-friendly interface in the language of their tooling – SQL, MDX, DAX, JDBC, ODBC, REST, or Python – translating queries into the dialect of the underlying cloud platform.
With a standard set of business terms, a semantic layer enables both BI and data science teams to interact with the same data, governance rules, and results, leveraging the tooling of their choice.
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