This guide looks at several technical approaches to implementing a semantic layer for your data and analytics stack. Included is an implementation checklist, technology scorecard, and chart of pros and cons with several example scenarios.
As a data and analytics leader, either on the business or tech side, reading this guide will help you adopt a semantic layer approach for your data assets. This guide explains where a semantic layer fits into modernizing your data and analytics infrastructure. It will help you:
- Drive consistency
- Reduce compute costs
- And improve ease of use for a wide variety of consumption types and use cases
The Top Signs You Need a Semantic Layer
Watch out for multiple analytics tools, complaints about data access, and inconsistent reports – along with other indicators.
- Business units or groups have strong preferences for different analytics tools
- Business analysts and/or data scientists complain about a lack of data access
- The slow pace of data integration drives the business to build their own Solutions
- Reports from different BI tools use similar terms but show different results
- Business executives express doubts about their confidence in the numbers
- And improve ease of use for a wide variety of consumption types and use cases
Key Considerations When Implementing a Semantic Layer
To start, you’ll want to cover all of your use cases, leverage data virtualization, and future-proof your technology choices.
- Business units or groups have strong preferences for different analytics tools
Your semantic layer must work across a variety of BI and ML consumers. It should be decoupled from a single consumption style. - Offers Tabular and Multidimensional Views
Your semantic layer must offer both tabular and multidimensional views to cover the widest range of use cases. - Supports Data Platform Virtualization
Your semantic layer must leverage data virtualization capabilities to abstract away data platform differences and minimize platform lock-in. - Easy Model Development and Sharing
Your semantic layer should provide a multi-user design environment and markup language to promote re-use and enforce standardization. - Ability to Express Business Concepts and Functions
Your semantic layer must support business constructs and core analytics requirements around time intelligence and hierarchical rollups. - Query Performance & Caching
Your semantic layer should include a comprehensive performance management system that goes beyond simple caching techniques. - Support for Business Intelligence and Data Science Workloads
Your semantic layer needs to support a variety of workloads including business intelligence and data science. - Security & Governance
Your semantic layer should integrate with your single sign-on (SSO) standards and support column-level security, row-level security and impersonation.