While the term “semantic layer” is seeing a surge in popularity, it’s hardly a new term and certainly not a new concept. In fact, the semantic layer is solving a persistent challenge: giving individual business domains control over data without leading to chaos.
A semantic layer is an ideal solution to ensure performance and consistency, while enabling autonomy at the same time. As a result, this can turn data into a product that’s accessible to business users, effectively democratizing data throughout the organization.
The Semantic Layer: Why It’s Critical to Analytics Success and Always Has Been dives into the development of the semantic layer and its role in facilitating a hub-and-spoke data analytics model. We explore:
- The evolution of OLAP systems, data lakes, and cloud data warehouses
- Why semantic modeling is critical for both effective business analytics
- Delivering actionable insights at scale with a semantic layer
This whitepaper will help you understand how to leverage a semantic layer for improved analytics performance, consistency, and autonomy.
About the Author
Andrew Brust is Founder/CEO of Blue Badge Insights, providing strategy and advisory services to data, analytics, BI and AI companies, as well as their partners and customers. Andrew covers the data and analytics world for VentureBeat and The New Stack, and is a lead analyst for GigaOm in that same space. He also co-chairs the Visual Studio Live! series of developer conferences, is a Microsoft Regional Director and Data Platform MVP, an entrepreneur and consulting veteran.