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AI-Powered Data Quality Agent in Qlik

Qlik
09/01/2026
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Fixing those issues at the source can be difficult, especially when the underlining pipelines are complex and require significant engineering effort. What if AI could help automate that entire process? With the Data Quality Agent in Qlik, AI can guide data engineers through a full dataset quality assessment using natural language prompts. The agent can identify trusted catalog definitions, generate intelligent data quality validation rules, and provide transparent reasoning behind every recommendation. Before anything is applied, the agent also provides action confirmation, ensuring teams stay in control of every change. The result is intent-driven action, improved trust scores, faster identification of data quality issues, and more accurate, reliable data for business decision making. Ready to see more? Watch the on-demand webinar for demonstrations of each Adjentic Data Engineering Agent.

TL;DR

  • Qlik's Data Quality Agent uses AI to guide data engineers through full dataset quality assessments via natural language prompts, reducing manual correction effort.
  • The agent identifies trusted catalog definitions, generates validation rules, and explains its reasoning transparently before any changes are applied.
  • An action confirmation step ensures human oversight is preserved, keeping engineering teams in control of every automated change.

Summary

This short product introduction showcases Qlik's Data Quality Agent, an AI-powered capability designed to help data engineers automate dataset quality assessments through natural language prompts. The video opens by framing a common enterprise pain point: analysts repeatedly encountering errors in weekly reporting data that require manual correction before insights can be trusted. Fixing these issues at the source is often difficult when underlying pipelines are complex and demand significant engineering effort. The Data Quality Agent addresses this by guiding engineers through a full quality assessment workflow — identifying trusted catalog definitions, generating intelligent validation rules, and providing transparent reasoning behind every recommendation. Critically, the agent includes an action confirmation step before any changes are applied, ensuring teams retain control over every modification. The promised outcomes include intent-driven automation, improved data trust scores, faster identification of quality issues, and more reliable data for business decision-making. The video closes with a call to action directing viewers to an on-demand webinar for deeper demonstrations of Qlik's broader Agentic Data Engineering suite.

Chapters

0:00 - The Data Quality Problem
0:21 - AI as the Solution
0:34 - Agent Capabilities Overview
0:51 - Outcomes and Next Steps

Key Quotes

0:12 "Fixing those issues at the source can be difficult, especially when the underlining pipelines are complex and require significant engineering effort."
0:25 "With the Data Quality Agent in Qlik, AI can guide data engineers through a full dataset quality assessment using natural language prompts."
0:43 "Before anything is applied, the agent also provides action confirmation, ensuring teams stay in control of every change."
0:51 "The result is intent-driven action, improved trust scores, faster identification of data quality issues, and more accurate, reliable data for business decision making."

FAQ

Does the Data Quality Agent apply changes automatically without human review?

No. Before anything is applied, the agent provides an action confirmation step, ensuring teams stay in control of every change made to their data pipelines.

Where can I see a full demonstration of the Data Quality Agent?

Qlik directs viewers to an on-demand webinar at pages.qlik.com/ADELaunchWebinar.html, which includes demonstrations of each Agentic Data Engineering Agent.


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