Discoverability and accessibility at the point of data consumption

Why the Semantic Layer is the perfect place for a user-focused catalog

I’ve always thought the semantic layer is the perfect place for a catalog. Semantic layers are in production so their metadata remains fresh automatically as engineering work drives change. Semantic layers are at the point of consumption, so are the most relevant abstractions for data users to know about. Other data catalogs seem more geared towards data engineers who are wading through the swamp instead of business users who are looking to see what data they can get hold of to answer their questions.

Released today by Cube, Semantic Catalog is centered around the semantic layer. The semantic layer contains the analytics vocabulary that your business users know about; the semantic layer is where data engineering meets business. This inherently makes this catalog more useful for business users. Yes, like other catalogs we have lineage from data source to consumer, but Cube’s Semantic Catalog contains the abstractions understood by business users: entities, metrics and dimensions. They can start at these abstractions, the analytics vocabulary they know about, and go backwards to understand their definition and forwards to understand where they are used in BI tools, data products, and elsewhere. This expands the use of the semantic layer to semantic intelligence.

This is not a data catalog, focused on a data engineer’s need to understand ELT, orchestration and data provenance. Semantic Catalog is focused on data analyst and business user needs, met by their data and analytics engineers.

Allowing users to discover both data and metadata using AI

On top of the Semantic Catalog, we are also launching our new AI Assistant. This builds upon our launch of the AI API last month and further enhances with the metadata stored in Semantic Catalog.

Business users can come here and ask questions in natural language. They don’t need to know any special syntax or terms. They don’t need to learn how to use a complex user interface. They can ask questions about what objects are in Semantic Catalog but also ask data questions and receive answers and results as line or bar charts here. They can ask these questions in the analytics vocabulary they already know. They are line-of-business experts; they don’t need to be part-time data analysts too.

This is not a BI tool. It is not our aim to build all the features required to deliver a modern BI experience - this is well served already by our partners. This is a data intelligence experience - a way to discover your data and use it in the same place with no technical knowledge required.

Concluding Thoughts

With Semantic Catalog and AI Assistant, we have enabled users to have semantic intelligence - knowledge of how data flows through their stack into their semantic layer, what data is trusted and available in their Cube semantic layer, what downstream assets it relates to and also the ability to query it with natural language and AI.

Semantic Catalog and AI Assistant make Cube Cloud more useful to business users than ever before as well as our core engineering users.