What is the Semantic Layer?
With the increased number of AI proof of concepts being run, you may be seeing the term ‘semantic layer’ cropping up more often in your research or in news pieces. But what exactly is it?
Clekt’s Associate Solution Architect Elliott Fairhall, describes the semantic layer as a method which collates the language used across an organisation. The word ‘language’ in this context could refer to regional language, industry jargon, acronyms, informal slang or simply the word preferences of a team or cohort. The semantic layer is custom built (by companies like Clekt) to enable technical infrastructure to understand the ‘true’ asks of teams, and help people to use data more effectively via a universal language without individuals having to change the way that they work.
In a nutshell, the semantic layer is the equivalent of an organisational glossary.
Why invest in a Semantic Layer?
- Enable Self-Service: If you’re on a mission to create data democracy across your business, a semantic layer will help to create a single source of truth and produce results in language that all teams understand.
- Create AI-Ready Foundations: Any business on an AI journey will know that good quality data is the most crucial first step. However, ensuring consistent and trusted results from AI powered initiatives is equally as important. If you want to empower teams to ‘Talk to the Data’ or implement tools that will generate insights with accuracy, you will first have to address language differences and a semantic layer can help you to tackle this.
- Eliminate Guesswork: By translating data with clear definitions, calculations, and relationships that everyone can understand, your C-suite and business leaders can be empowered to make data-driven decisions with confidence.
In Summary
The semantic layer provides common language that can be used across the Enterprise, creating a feeling of trust and unity amongst users. It also creates a core foundation stone for introducing AI into business processes by bridging the gap between technical infrastructure and raw data with intuitive business friendly descriptions of the data.
If you’d like to find out more, you can take a look at our episode of Clektive Thinking which dives into the future of BI here.
