After announcing a number of features at its annual user conference in April, Qlik is now offering some of its data engineering tools designed to prepare data for AI, which have been in preview.
New features that are generally available include a data quality agent that allows users to create and edit data quality rules, measure the quality of data points and datasets using confidence scores and other metrics, and detect or report anomalies. Additionally, Qlik has launched a catalog that helps users standardize terminology and discover data assets, among other new features.
Collectively, Qlik’s capabilities are aimed at helping companies prepare data for AI, making it easier for enterprises to bridge the gap between the desire to build large multi-agent networks and the reality of building trusted agents and other AI applications that provide accurate output in production environments.
“This is a good update from Qlik for data engineers using Qlik’s platform,” Donald Farmer, founder and principal at TreeHive Strategy, told TechTarget. “It’s not a huge advance, but it’s a very useful AI integration.… This is very helpful for the gap between ambition and readiness that Qlik has pinpointed.”
Stephen Catanzano, an analyst at Omdia, a division of Informa TechTarget, similarly noted that Qlik’s new features are valuable because they integrate agents to improve data engineering efficiency.
“These capabilities go beyond just using AI to generate code by incorporating agent AI throughout the data engineering lifecycle,” he told Techtarget. “Organizations can now more efficiently discover, validate, manage, and package trusted data products to accelerate the delivery of AI-enabled data while reducing engineering backlogs without sacrificing governance or lineage.”
King of Prussia, Pennsylvania-based Qlik has been a business intelligence and data integration vendor for years, evolving as AI becomes the means to generate insights and leverage BI.
Mike Capone, Qlik’s CEO since January 2018, abruptly resigned after the vendor’s annual Connect user conference. Saugata Saha, who came to Qlik from S&P Global where she led market intelligence, was named president and CEO of Qlik a month after Capone’s departure, and will officially begin her new role on July 31.
Preparation for AI production
Agent AI development has become a major focus for many companies in recent years, but most AI pilots have yet to be deployed in production. Problems with the data that informs agents is not the only reason why AI projects fail more than they succeed, but it is one of the more frequent reasons.
This is a great update from Qlik for data engineers using Qlik’s platform. It’s not a huge advance, but it’s a very useful AI integration. … This is very useful for the gap that Qlik pinpoints: the gap between ambition and readiness.
Donald FarmerFounder and Principal of TreeHive Strategy
Agents need well-prepared data to perform, and the data must be ready the moment you invoke it to inform an action. Without AI-enabled data, agents will make inferences based on the information they have, but if that information is incomplete, up-to-date, or inaccurate, the output will be poor. If these outputs are left undetected, they can have serious consequences, including lost revenue and regulatory violations.
As agents rely heavily on AI-enabled data, many data management providers’ recent product development efforts have focused on making data available for AI. Most companies, including AWS, Databricks, Microsoft, and last month’s Snowflake, are focused on enabling AI to discover data.
Qlik, along with vendors like Alteryx, is taking a different approach by focusing on preparing data for AI based on customer feedback while monitoring market trends, said Drew Clark, Qlik’s executive vice president of products and technology.
“As organizations move from AI pilots to operational AI, the data engineering work required to ensure data is trusted, managed in a timely manner, and available to both humans and AI agents is increasingly becoming a bottleneck,” he told TechTarget. “Customers told us they needed to do more at that layer without giving up governance, lineage, and choice.”
In addition to data quality agents and catalogs for organizing your data assets, specific Qlik data engineering tools that are currently generally available include:
data products. Features that help teams build, manage, and manage data products, making curated datasets and other assets easily operational and reusable for analytics and AI.
Declarative pipeline with code. A feature that enables data engineers to build and manage AI pipelines in conjunction with approved third-party coding agents and development environments.
The Model Context Protocol has been extended to give authorized agents and other AI tools access to their own data and business logic stored in Qlik’s secure environment, giving them the context to execute properly.
Perhaps the most valuable of the new features, Catanzano said, are data products, as they enable companies to provide reusable and trusted data for analytics and AI.
“Rather than recreating datasets for every initiative, organizations can establish a managed data product that serves as a reliable foundation for multiple AI and business use cases,” he said.
But while important to existing Qlik customers, tools such as data products and data quality agents are not unique, Catanzano continues.
“Many data platform vendors are adding AI-assisted development and governance capabilities,” he said. “What sets Qlik apart is its combination of agent workflows, governance, open architecture, and MCP-enabled interoperability, allowing customers to work with their favorite AI assistants and existing technology stacks rather than being locked into a single ecosystem.”
Farmer similarly emphasized the value of agents assisting teams in building and managing data products so that developers and engineers don’t have to create new datasets every time a new AI or analytics project arises.
“Qlik has always struggled a bit with data reusability, especially since the common solution has been to create ‘data marts’ that are stored in their own QlikView Data files,” he said. “Data products are a more mature and agile way to manage that scenario.”
Regarding Qlik’s competitiveness as well as Catanzano, Farmer pointed out that other vendors offer similar capabilities. But Qlik stands out by combining features in an integrated layer, he continued.
“These features are not really distinguishable from Qlik because all data platforms offer something similar. [but] Qlik is in a defensible position because it combines capabilities across a single, managed platform,” Farmer said.
Let’s take a look into the future
Through the second half of 2026, Qlik will continue to focus on delivering features designed to help users move their AI initiatives from experimentation to production, Clarke said.
Specifically, the vendor will work to strengthen the data foundation for AI, build more agents to help integrate and analyze data, and enable customers to combine the capabilities of Qlik with those of other platforms they use for data and AI workflow aspects.
“The common thread is making AI more operational and reliable by connecting it with trusted data, business context, and the controls that enterprises need,” Clark said.
Farmer suggested adding capabilities to Qlik to monitor AI usage, including agent and question interactions, consumption patterns, usage frequency and results.
“Qlik’s strengths in data quality, cataloging, and analytics could make this a unique selling point,” he said, noting that adding monitoring capabilities will allow users to track data quality issues from the beginning of the pipeline, assess their impact on the output of AI agents, and analyze their impact on decision-making.
“Adding such capabilities would significantly differentiate the platform and highlight Qlik’s strengths,” Farmer added.
Similarly, Catanzano cited the addition of operational monitoring tools as a way for Qlik to better serve existing users and perhaps appeal to new ones.
“As enterprises deploy more AI agents and production AI applications, capabilities such as AI observability, model and agent monitoring, policy enforcement, and business outcome tracking will complement Qlik’s strong data governance foundation and further differentiate the platform,” he said.
Eric Avidon is a senior news writer at Informa TechTarget and a journalist with more than 30 years of experience. He is responsible for analysis and data management.
