Qlik Announces Trust Score for AI in Qlik Talend Cloud
As enterprises accelerate AI adoption, many face a fundamental blind spot: they don't know if the data feeding their models is trustworthy or fit for purpose. Qlik Trust Score for AI solves that problem with a single, intuitive score that shows teams where trust breaks down. This helps prevent bias, drift, or faulty outcomes from taking hold.
'Most companies still treat data trust like an IT hygiene issue. It's not. It's the foundation of every AI decision a business makes,' said Drew Clarke, EVP of Product and Technology at Qlik. 'If you can't measure trust, you're gambling with outcomes, compliance, and customer experience. With Qlik Trust Score for AI, we're giving leaders a living signal, not a gut check, that their data is fit for purpose. That's how you close the gap between AI ambition and AI impact.'
Qlik Trust Score for AI builds upon Qlik's original Trust Score framework by introducing three new dimensions purpose-built for AI readiness: Diversity : Measures how representative and balanced the data is, helping to reduce bias in AI training.
: Measures how representative and balanced the data is, helping to reduce bias in AI training. Timeliness : Captures the freshness of data flowing into AI models, ensuring relevance for more accurate decision-making.
: Captures the freshness of data flowing into AI models, ensuring relevance for more accurate decision-making. Accuracy: Flags values that fall outside user-defined business rules or unreliable quality expectations that can erode organizational trust in AI.
Combined with existing metrics like Discoverability and Usage, Qlik Trust Score for AI offers a practical way to validate datasets for use in AI training, RAG pipelines, or intelligent automation, with Security and LLM Readiness dimensions to follow.
As a part of Qlik's vision for data quality and governance for AI initiatives, Qlik is also rolling out additional features, including Qlik Trust Score historization, which allows users to monitor trends over time and correlate shifts in trust with downstream impacts, including model drift or performance degradation.
In addition, Qlik will also roll out an early access program for an AI-native Data Stewardship experience within Qlik Talend Cloud, aimed at proactively detecting and resolving data issues earlier in the lifecycle. Targeted to launch this fall, this capability will combine automated rules, human-in-the-loop workflows, and platform-wide governance. This enables data teams and AI personas to collaborate more effectively on data quality remediation.
'AI initiatives are still stumbling at alarming rates—only a fraction succeed in delivering enterprise-scale value,' said Ritu Jyoti, Group VP/GM, AI, Automation, Data and Analytics at IDC. 'The missing link isn't the model; it's the data. Without visible metrics for data trust, organizations risk costly failures, unchecked bias, and stalled adoption. A unified trust signal like Qlik's Trust Score for AI gives teams the concrete insight they need to make AI reliable and repeatable.'
'Reworld is one of many Qlik customers focused on operationalizing trust in their AI strategy. 'The hardest part of AI is rarely the model. It's trusting the data behind it. In our business, if
we can't stand behind the data, we can't stand behind the decisions,' said Charles Link, Senior Director of Data and Analytics at Reworld. 'A clear, continuous signal that tells you whether data is truly ready for AI creates a new standard. It brings trust out of the shadows and into the conversation, where it belongs.'
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