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The Value of Using a Semantic Layer
A semantic layer fills a void left in many analytic architectures. It is a centralized place to define business logic, ensure reporting is consistent, accelerate insights and make data more consumable. A semantic model is a key component in a semantic layer platform. The value of semantic models is clear. Nearly twice as many organizations using semantic models (62% vs. 33% overall) report that their analytics capabilities are completely adequate, while twice as many (51% vs. 25% overall) report that their data governance capabilities are completely adequate. In fact, organizations that have successfully implemented a semantic model are more than twice as likely to report satisfaction with analytics (77%) compared with a 33% overall satisfaction rate. But a semantic model should go beyond data. To be truly useful, a semantic model must include calculated pieces of information that are critical to understanding an organization’s operations.
Three Reasons to Use a Semantic Layer
1. Bridge AI & BI Teams to Improve Data-Driven Decision-Making
In employing artificial intelligence and machine learning (AI/ML), data preparation and access to data sources are some of the most significant challenges an organization must overcome. Our research finds that organizations prefer to deliver AI/ML via the business intelligence (BI) and analytic tools they already have in use. Semantic layers bridge the gap between data sources and line-of-business users by establishing a single source of governed analytics that can be self-served from any AI/BI tool. Automating data preparation with a scalable analytics platform that can be used by analysts with varying levels of analytics skill sets improves accessibility, thus allowing more individuals the opportunity to contribute to the process. As a result, more of the workforce is using analytics (43% vs. 23% overall), generating reports without creating IT requests and accelerating data-driven decisions at scale. Organizations are also much more comfortable with self-service when a semantic layer is in place (54% are very comfortable vs. 14% overall). With streamlined access to data analytics, data scientists can experiment more freely, quickly and easily finding pertinent information that can inform and improve decision-making.