Qlik Introduces Semantic Models

Qlik has introduced Semantic Models for defining, governing, and reusing business definitions within data products. The capability helps teams reduce duplicated modeling and gives analytics and artificial intelligence a consistent basis for interpreting enterprise data.

Semantic Models add reusable business logic to Qlik's data-product capabilities, which already bring together curated datasets, ownership, lineage, and data-quality information. Teams can manage the meaning of their data alongside information about its source and fitness for use. Semantic Models let them make intended definitions and relationships explicit and reusable.

With Qlik Semantic Models, users can do the following:

• Give teams a shared business language: Help business users, data teams, and AI systems work from consistent definitions of key business terms and metrics. Semantic Models connects governed business terminology from the business glossary with the measures and dimensions used across analytics and data products.
• Build on existing business logic: Help organizations reuse Qlik analytics applications instead of recreating established definitions and calculations. A new extraction capability will let teams bring semantic definitions from existing Qlik analytics applications into reusable, governed and discoverable data products.
• Make trusted business context available to AI agents: Help AI-powered experiences interpret enterprise data using governed definitions and relationships. Qlik is extending semantic model support to Qlik Answers and MCP tools.
• Advance interoperability across the data ecosystem: Help organizations make semantic definitions more useful across technologies and workflows. Qlik is developing interoperability with Apache Ossie, an open-source initiative at the Apache Software Foundation.

"A number can be mathematically correct and still answer the wrong business question," said Sam Pierson, chief technology officer of Qlik, in a statement. "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."