In the era of AI-ready data, Qlik Trust Score redefines how we build trust in data.
Qlik is developing the Trust Score to support the transition from trusted data to AI-ready Data, effectively addressing the needs of AI-driven organizations.
AI is only as good as the data that fuels it. Yet AI use cases introduce new challenges beyond traditional data quality. It’s no longer enough for data to be complete and documented—it must also be accurate, timely, and diverse enough to drive meaningful, bias-free outcomes. Whether you’re training machine learning models or powering real-time decision systems, AI-ready data needs a different lens for trust.
Earlier this year, Qlik introduced Qlik Trust Score in Qlik Talend Cloud — a quality indicator that consolidates critical data quality metrics such as completeness, discoverability, and usage into a single, intuitive score. The Six principles of AI-ready data were also introduced, providing organizations with a practical framework & associated services to assess not just the reliability of their data, but its readiness to drive AI-powered outcomes.
Then, in May 2025, at Qlik Connect, Qlik took the next leap forward within the product, introducing Qlik Trust Score for AI as a capability integrated within Qlik Talend Cloud. As organizations double down on AI adoption, the definition of trusted data must expand to meet the specific demands of machine learning and intelligent automation. Qlik Trust Score for AI builds on our original framework by introducing new, purpose-built dimensions — empowering teams to evaluate whether their data is not only trustworthy but truly fit for AI.
In the video below, you can watch an interview during Qlik Connect 2025, with Dan Potter, VP Product Marketing at Qlik and Dr. Michael Bronstein, DeepMind Professor of Artificial Intelligence at University of Oxford & Qlik AI Council Member.
Building AI confidence through Diversity, Accuracy, and Timeliness
To help organizations operationalize data for AI, Qlik has introduced three new dimensions into Qlik Trust Score, rooted in our six principles for AI-ready data.
AI-specific dimensions of Qlik Trust Score
Diversity
Evaluates how representative and well-distributed the dataset is relative to expectations. In AI, biased or narrow data leads to skewed outcomes and poor generalization. A high diversity score indicates that the dataset covers a wide range of relevant scenarios, populations, or types, ensuring that AI models are trained on broad, balanced, and inclusive information. It is calculated as a function of content evenness and expected volume—measuring how well values are balanced across the rows and columns, and whether enough data is present.
Configuring the diversity dimension of Qlik Trust Score for AI
For example, A customer churn model trained only on data from urban regions may underperform for rural customers. A high diversity score ensures the dataset includes varied geographies, age groups, and customer types, reducing blind spots in prediction.
Accuracy
Measures how well data aligns with known or expected truths using customizable validation rules. Inaccurate inputs can lead to compounding errors in AI systems, where even small mistakes can scale into significant misjudgments. By defining custom data quality rules under the “Accuracy” category, these rules can affect the accuracy dimension, allowing teams to quickly identify and address the most critical issues impacting model performance.
Categorizing the quality rule for the Qlik Trust Score
For example, in a predictive maintenance system, incorrect temperature readings from sensors (e.g., due to miscalibration) can cause false alarms or missed failures. Accuracy checks can flag such outliers or mismatches against expected ranges, preventing faulty model behavior.
Timeliness
Ensures that data is timely and reflects the most current state of the business or environment. For AI applications—especially those involving real-time predictions or automation—stale data can mean outdated or irrelevant outputs. This is calculated using an increasing function based on data pipeline updates. Thresholds can be defined to specify how recent data should be —once data falls outside that freshness time window, the score decreases
Configuring the timeliness threshold window
For example, in a fraud detection model, transaction data that is even a few hours old may miss flagging a fraud as it happens. A high timeliness score ensures your model uses the freshest available data to catch data issues in real time.
Trust Over Time: Monitoring Data Quality Trends for AI Confidence
AI doesn’t just require high-quality data on a one-off basis — it demands consistent, high-quality data. That’s why Trust Score Historization has been introduced in Qlik Talend Cloud, transforming trust from a one-time evaluation into a continuous practice that can be monitored over time.
With this capability, you can:
- Track trends in data quality across weeks or months to catch emerging issues early
- Correlate performance drops with changes in key quality dimensions like freshness or accuracy
- Audit how changes to configurations or quality rules impact trust over time
Viewing the events and dimension updates to Qlik Trust Score over time
Conclusion
Qlik Trust Score for AI goes beyond a simple quality check — it’s a way to evaluate whether your data is truly fit for AI use-cases. With added AI-specific dimensions like accuracy, diversity, and timeliness, and the ability to monitor trust over time, it helps organizations deliver data that drives reliable, responsible AI outcomes.
For more information about Qlik Trust Score, click here: https://qqinfo.ro/en/qlik-trust-score-elevating-data-trustworthiness-in-qlik-talend-cloud/.
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