New capability embeds governed business definitions, metrics and relationships into reusable data products, helping enterprises eliminate duplicated modelling and improve AI readiness.
As enterprises expand their use of artificial intelligence, inconsistent business definitions are emerging as a significant barrier to reliable analytics and AI-driven decisions. Different applications can interpret the same metric in different ways, producing answers that may be mathematically correct but commercially misleading.
Qlik is addressing this challenge with the introduction of Semantic Models, a new capability that enables organisations to define, govern and reuse business logic within data products. The offering is designed to establish a common business context across analytics applications, data teams and AI-powered workflows.
A metric such as net revenue, for instance, depends on how an organisation accounts for returns, discounts and reporting periods. When these definitions vary across systems, business users and AI tools can arrive at different conclusions from the same underlying data. Qlik Semantic Models make these definitions and relationships explicit, reusable and governed.
The capability extends Qlik’s existing data-product framework, which brings together curated datasets, ownership, lineage and data-quality information. By adding business meaning to this foundation, organisations can manage not only where data comes from and whether it is reliable, but also how it should be interpreted.
“ A number can be mathematically correct and still answer the wrong business question. Teams need to understand which definition is being used and apply it consistently.” — Sam Pierson, Chief Technology Officer, Qlik
Sam Pierson, Chief Technology Officer at Qlik, said: “A number can be mathematically correct and still answer the wrong business question. 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.”
A key element is the ability to connect business glossary terminology with the measures and dimensions used in analytics and data products. This creates a shared language for business users, analytics developers and AI systems, reducing the need to recreate calculations and reconcile competing definitions across applications.
Qlik is also introducing an extraction capability to bring semantic definitions from existing Qlik analytics applications into reusable data products. Initially available as a public preview from October 28, the feature is intended to help organisations preserve years of investment in analytics models, with general availability planned for later in the fourth quarter.
The company is extending semantic-model support to Qlik Answers and Model Context Protocol (MCP) tools, enabling AI-powered experiences to draw on governed business definitions and relationships. AI assistance for creating and maintaining semantic models is also on the roadmap. Qlik is further developing interoperability with Apache Ossie, an open-source initiative under the Apache Software Foundation.
For enterprises moving towards AI-driven decision-making, the significance of semantic models goes beyond simplifying analytics development. They provide a consistent layer of business context that can help AI systems interpret enterprise data more accurately and make their outputs easier to trust.
Qlik Semantic Models is targeted for general availability on October 28, 2026, wherever Data Products is available, including Qlik Cloud Analytics, Qlik Talend Cloud Premium and Enterprise editions, and Qlik Sense Enterprise SaaS.
