Qlik launches data engineering tools to aid AI development

Preparing for AI production

Although agentic AI development has been a major focus for many enterprises over the past couple of years, most AI pilots still don't make it into production. Problems with the data that informs agents are not the only reason more AI projects fail than succeed, but they are among the more frequent causes.

This is a good update from Qlik for data engineers using their platform. No big breakthroughs, but very useful AI integration. … For the gap that Qlik correctly identifies -- between ambition and readiness -- this is very helpful.

Agents need properly prepared data to perform, and they need it to be ready the instant they call on it to inform an action. Without AI-ready data, agents will make inferences based on the information they do have, which, if not complete, current or correct, leads to poor outputs. Left undetected, these outputs can have severe consequences, including lost revenue and regulatory noncompliance.

With agents so reliant on AI-ready data, many data management providers' recent product development initiatives have focused on making data available for AI. Most -- including AWS, Databricks, Microsoft and Snowflake just last month -- have centered on making data discoverable for AI.

Qlik, along with vendors such as Alteryx, is taking a different approach by focusing on preparing data for AI in a move informed by customer feedback in conjunction with watching market trends, according to Drew Clarke, Qlik's executive vice president of product and technology.

"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," he told TechTarget. "Customers told us they need more leverage in that layer, but without giving up governance, lineage or choice."

In addition to data quality agents and a catalog to organize data assets, specific Qlik data engineering tools that are now generally available include the following:

• Data Products, a feature that aids teams as they build, manage and govern data products so that curated datasets and other assets are easy to operationalize and reuse for analytics and AI.
• Declarative Pipelines with Coding, a feature that allows data engineers to work with approved third-party coding agents and development environments to build and manage AI pipelines.
• Expanded Model Context Protocol capabilities that enable authorized agents and other AI tools to access proprietary data and business logic stored in Qlik's secure environment, so they have the context to properly perform.

Perhaps the most valuable of the new features is Data Products because it enables enterprises to deliver reusable, trusted data for analytics and AI, according to Catanzano.

"Rather than recreating datasets for every initiative, organizations can establish governed data products that become a reliable foundation for multiple AI and business use cases," he said.

However, while significant for existing Qlik customers, tools such as Data Products and data quality agents are not unique, Catanzano continued.

"Many data platform vendors are adding AI-assisted development and governance features," he said. "Where Qlik differentiates itself is by combining agentic workflows, governance, open architecture and MCP-enabled interoperability, allowing customers to work with their preferred AI assistants and existing technology stack instead of locking them into a single ecosystem."

Farmer likewise highlighted the value of the agents that assist teams in building and managing data products so that developers and engineers don't have to create new datasets every time there's a new AI or analytics project.

"Qlik has always struggled somewhat with data reusability, especially as the common solution was to create 'data marts' stored in proprietary QlikView Data files," he said. "Data Products are a more mature and agile way of managing that scenario."

Regarding Qlik's competitive standing, like Catanzano, Farmer noted that other vendors are offering similar capabilities. However, Qlik stands out by combining capabilities in a unified layer, he continued.

"These features don't really differentiate Qlik because every data platform is shipping something similar, [but] Qlik has a defensible position in the combination of capabilities across a single governed platform," Farmer said.

A peek into the future

Over the second half of 2026, Qlik's continued focus will be to provide capabilities designed to help users move AI initiatives beyond experimentation and into production, according to Clarke.

Specifically, the vendor plans to address strengthening the data foundation for AI, building more agents that aid data integration and analytics, and enabling customers to combine Qlik's capabilities with those of other platforms they use for aspects of their data and AI workflows.

"The common thread is making AI more operational and reliable by connecting it to trusted data, business context and the controls enterprises need," Clarke said.

Farmer suggested that Qlik add more capabilities that monitor AI usage, such as agent-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 would enable users to track data quality issues from the inception of a pipeline, assess their impact on AI agent outputs, and analyze their effects on decisions.

"Adding such a feature would significantly distinguish the platform and speak to Qlik's strengths," Farmer added.

Likewise, Catanzano named adding operational monitoring tools as a means 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 would complement Qlik's strong data governance foundation and further differentiate the platform," he said.

Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than three decades of experience. He covers analytics and data management.