Qlik, a business intelligence and data integration vendor, transitioned several artificial intelligence tools from preview to general availability. The release introduces components designed to assist engineers in validating and structuring data for production deployments.
The updated platform includes data quality agents that allow developers to establish rules, calculate trust scores for datasets and identify anomalies. The vendor also deployed a catalog to standardize terminology and facilitate the discovery of data assets. A feature labeled Data Products enables teams to curate reusable datasets for subsequent analytics operations.
“This is a good update from Qlik for data engineers using their platform,” Donald Farmer, founder and principal of TreeHive Strategy, told TechTarget. “No big breakthroughs, but very useful AI integration. … For the gap that Qlik correctly identifies -- between ambition and readiness -- this is very helpful.”
To support developers utilizing external tools, Qlik introduced Declarative Pipelines with Coding. This addition allows engineers to operate approved third-party coding agents to construct artificial intelligence pipelines. The release expands Model Context Protocol integration, allowing authorized external applications to access proprietary logic stored within the Qlik environment.
“These capabilities move beyond simply using AI to generate code by embedding agentic AI throughout the data engineering lifecycle,” Stephen Catanzano, an analyst at Omdia, told Techtarget. “Organizations can now discover, validate, govern, and package trusted data products more efficiently, helping reduce engineering backlogs while accelerating delivery of AI-ready data without sacrificing governance or lineage.”
These technical additions follow organizational shifts at the vendor. Mike Capone stepped down from the chief executive role following the annual Connect conference. Saugata Saha assumed the president and CEO positions shortly after.
“As organizations move from AI pilots to operational AI, the bottleneck is increasingly the data engineering work required to make data trusted, timely, governed and usable by both people and AI agents,” Drew Clarke, Qlik's executive vice president of product and technology, told TechTarget. “Customers told us they need more leverage in that layer, but without giving up governance, lineage or choice.”
