Qlik updates talend cloud with AI and governance tools

Qlik has added new Talend Cloud features that enable faster API-based data sharing, automated documentation, AI-driven data-quality rules and collaborative issue resolution for data, business and AI teams.

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Voice&Data Bureau
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QLIK

Qlik, global provider in data integration, data quality, analytics, and artificial intelligence (AI), has announced new features in Qlik Talend Cloud intended to make governed and trusted data easier for organisations to use. The updates allow data teams to publish secure, standards-based API endpoints for governed data products with a single click, automatically generate field-level documentation at scale, and use an AI assistant to propose data-quality rules. 

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The release introduces several practical improvements for data and analytics teams. Governed data can be published more quickly through secure, open APIs, enabling its use in tools such as Power BI, Tableau, Excel, Salesforce and internal applications. Dataset fields can be automatically described at scale, improving discoverability and reducing the need for manual documentation. 

Data quality can be strengthened through an AI assistant that recommends and creates data-quality rules based on data profiles, offering broader coverage with less effort. In addition, agentic-enabled, sprint-style remediation and validation workflows support collaborative issue resolution by bringing together data, business and AI teams to identify and address problems. 

“Customers want flexibility. If the most reliable data is restricted to a single tool, it becomes a bottleneck,” said Drew Clarke, EVP, Product & Technology, Qlik. “Open, standards-based APIs support the use of governed data wherever work takes place, while AI-driven stewardship helps reduce repetitive work and improve outcomes.”

“Qlik has made stewardship more collaborative,” said Eva Chrona, CEO at Climber. “Sprint workflows and shared validation brought our domain experts into the process, increasing trust and speeding up remediation. Automated field documentation and suggested quality checks are already reducing workload, and we look forward to further developments in AI-driven stewardship.”

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