Bengaluru: Qlik has introduced Qlik Semantic Models, a capability for defining, governing and reusing business definitions within data products.
According to the company, Qlik Semantic Models is meant to reduce duplicated modeling and give analytics and AI a consistent basis for interpreting enterprise data.
Qlik gave an example: a question about net revenue needs a company’s rules for returns, discounts and reporting periods. When those rules differ across applications, plausible answers can mean different things. Qlik Semantic Models let teams make the intended definitions and relationships explicit and reusable.
Qlik Semantic Models add reusable business logic to Qlik’s existing data-product capabilities, which already bring together curated datasets, ownership, lineage and data-quality information.
“A number can be mathematically correct and still answer the wrong business question,” said Sam Pierson, Chief Technology Officer at Qlik.
Explaining the purpose of Qlik Semantic Models, Pierson said: “Teams need to understand which definition is being used and apply it consistently.
With Semantic Models, we are making that work reusable within governed data products, so customers can build on the business logic they establish as their use of analytics and AI grows.”
Qlik Semantic Models connect governed business terminology from the business glossary with the measures and dimensions used across analytics and data products
Qlik says Qlik Semantic Models are designed to help business users, data teams and AI systems work from consistent definitions of key business terms and metrics.
A new extraction capability for Qlik Semantic Models will let teams bring semantic definitions from existing Qlik analytics applications into reusable, governed and discoverable data products.
Qlik says this is meant to let organizations reuse their earlier investment in Qlik applications instead of recreating established definitions and calculations.
Qlik is also extending semantic models support to Qlik Answers and MCP tools, so AI-powered experiences can interpret enterprise data using governed definitions and relationships. It is planning AI assistance to help teams create and maintain semantic models.
On interoperability, Qlik is developing Qlik Semantic Models support with Apache Ossie (incubating), an open-source initiative at the Apache Software Foundation. A public preview is planned as work progresses.
Henri Rufin, Head of Data & Analytics at Radiall, said of Qlik Semantic Models: “AI is only as useful as the data and business context behind it.”
Rufin added: “With Qlik Semantic Models, we can bring our business definitions, metrics and relationships together in a governed, reusable way, giving our teams a consistent foundation for analytics and AI.
Instead of recreating context for every new use case, we can build on a shared understanding of our business and make that context available, wherever it is needed.”
Qlik says Semantic Models work wherever Data Products is available: Qlik Cloud Analytics and Qlik Talend Cloud Premium and Enterprise editions, and Qlik Sense Enterprise SaaS.
Also Read: Qlik And Starburst Partner To Turn Fragmented Enterprise Data Into Governed, AI-Ready Intelligence
