Qlik launches AI Trust Score to boost confidence in data for AI development
Qlik’s AI Trust Score helps teams assess data readiness for AI by measuring quality factors like diversity and timeliness. It reduces risks from biased or outdated data, improving AI reliability.

Qlik Introduces AI Trust Score to Strengthen Data Preparation for AI
Qlik has launched its AI Trust Score, now available within the Qlik Talend Cloud data integration platform. This tool helps IT and development teams assess whether their data is suitably prepared to support AI models and applications. By focusing on AI-specific data quality dimensions, it provides a clear indicator of data readiness, helping reduce risks associated with unreliable or biased data.
Extending Traditional Data Quality Metrics
The AI Trust Score builds upon Talend's original Trust Score, which Qlik acquired in 2023. While the original Trust Score measured traditional data quality factors like completeness and usage, the AI Trust Score introduces new dimensions such as data diversity and timeliness. These additions specifically address concerns around bias in training data and the need for up-to-date information, both critical to reliable AI outcomes.
Mike Leone, an analyst at Enterprise Strategy Group, notes that trust scores like Qlik’s help organizations catch issues such as bias and inaccuracy early. This proactive approach prevents flawed data from influencing AI models, closing the gap between technical teams and business stakeholders by establishing a common language and metrics around data readiness.
Why AI Trust Score Matters
With AI investments growing, many organizations struggle to verify the quality of their data inputs. Poor data quality can lead to AI hallucinations and degraded model performance. The AI Trust Score provides a single, easy-to-understand measurement of how trustworthy data is for AI purposes. It highlights specific breakdowns, allowing teams to address problems during development rather than after deployment.
Drew Clarke, Qlik’s executive vice president of products and technology, explains that many teams lacked reliable signals for AI readiness, especially as generative AI tools became embedded in workflows. The AI Trust Score fills this gap by focusing on AI-related risks and data quality beyond traditional checks.
Monitoring Data Quality Over Time
Alongside the AI Trust Score, Qlik offers historicization features that track data quality trends. This allows users to identify when and why AI models may start to fail by correlating performance issues with data quality shifts. Such transparency helps reduce the "black box" effect often seen in AI systems.
Qlik also plans to introduce an AI-native data stewardship environment later this year. This environment will include automated rules and governance tools to detect and fix data problems earlier in the lifecycle, improving overall data quality and governance.
Looking Forward: Expanding AI Data Readiness
Qlik aims to enhance the AI Trust Score by adding assessments for data security and large language model (LLM) readiness within the next six months. The vendor also plans to support data engineering workflows tailored to agentic AI, bridging the gap between trusted data and responsible AI execution.
Experts suggest expanding the Trust Score to cover additional phases of the AI lifecycle, such as MLOps and LLMOps, to provide continuous monitoring of data quality. Demonstrating real-world use cases will be key to showcasing its value in improving AI model reliability.
Conclusion
For IT and development professionals working with AI, ensuring data is AI-ready is essential to reducing risk and improving model outcomes. Qlik’s AI Trust Score offers a practical way to quantify data trustworthiness with a focus on AI-specific factors. Coupled with ongoing monitoring and upcoming stewardship tools, it helps teams maintain high-quality data throughout AI development and deployment.
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