Qlik

Qlik sells data integration and analytics together rather than analytics alone, and that combination is the basis of its argument. Its stated proposition is delivering the AI organisations were promised by pairing agentic analytics with data integration on trusted data, on the reasoning that AI initiatives fail on data quality and timeliness rather than on model capability. Three things are named as setting it apart: an analytics engine providing context-rich AI-driven intelligence, an agentic architecture described as open, interoperable and AI-native, and a trusted data foundation delivering timely, accurate and governed data.

The data integration side is substantial enough to be evaluated on its own. It covers real-time integration for AI-ready data, agentic data engineering, data movement and change data capture, data transformation, data quality and governance, data products, an open lakehouse, data warehouse modernisation, and application and API integration. Change data capture in particular is a long-standing Qlik strength for keeping downstream systems current without full reloads.

On the analytics side sit the analytics engine, conversational AI, predictive AI, AI anomaly detection, visualisation, dashboards, embedded analytics, enterprise reporting, and automation and orchestration. The platform layer covers MCP support, connectors, integrations, cloud regions, security certifications and cloud migration, with MCP for Enterprise AI positioned specifically to make data available to AI systems. Cloud partners include AWS, Azure, SAP, Cloudera, Google, Snowflake, Databricks and Confluent. Qlik states commitments to security and compliance and to responsible, transparent and explainable AI, and offers expert services alongside a partner network organised by industry. Pricing pages are published for data integration and for analytics separately, and a free trial is available.