I spend a lot of time with customers and partners, and the pattern is consistent. Everyone wants the benefits of AI, faster decisions, more automation, better productivity. But the thing that slows them down is not the model.
It’s the data underneath it. Not just any data, but trusted data to drive trustworthy business outcomes.
As soon as you move from AI that explains to AI that influences workflows, ambiguity stops being an inconvenience. It becomes a liability. If the inputs are unclear, inconsistent, or unowned, the output will be inconsistent too, and in an agentic world that inconsistency turns into real decisions and real actions that cannot be trusted.
That’s why today, we are bringing Trusted Data Products capabilities into Qlik Analytics, powering the next generation of outcome-driven, intelligent workflows.
Trusted Data Products are now generally available in Qlik Cloud Analytics Premium and Enterprise and Qlik Sense Enterprise SaaS. It delivers a practical capability customers have been asking for - the ability to take their existing datasets including QVDs, and turn them into governed, discoverable, reusable data products within the Qlik analytics environment, complete with built-in quality, context, and ownership.
Why this is important for AI, and critical for agentic AI
Most organizations have lived with “good enough” for years. If definitions were fuzzy, someone would reconcile later. If data quality issues surfaced, the analyst would add a caveat. If ownership wasn’t clear, teams would work around it.
That operating model breaks down as AI becomes part of everyday workflows.
AI assistants and agents need a foundation that is consistent and explainable, because they work at speed and scale. They don’t stop to ask whether a dataset is the right one, whether a field definition changed, or whether the logic behind a metric is aligned across teams. If we don’t engineer trust into the foundation, trust becomes a constant manual tax, and that’s where adoption stalls.
Trusted Data Products helps remove that tax. It makes trust and context part of the asset itself, so analytics teams, and AI workflows, start from the same reliable base. It is a core and essential component of our agentic experience.
What can analytics consumers do with Trusted Data Products
This release lets you package existing datasets as data products that teams can confidently use and reuse, because the details that normally live in someone’s head are made visible and governed.
With data products, teams can quickly see,
Business context and intended usage, so the data is understandable, not just accessible
Quality indicators and Trust Score visibility, so readiness is clearer upfront
Ownership and lifecycle management, so there is accountability
End-to-end lineage, so you can trace where data came from and what changed
Discoverability and reuse, so people can find the right asset and stop rebuilding the same datasets
It also fits into daily workflow. Data products and quality capabilities are accessible from the activity center, and with just a few clicks, teams can build analytics assets from trusted data products. That means less preparation, faster app development, and a stronger foundation for agent-driven and AI-powered workflows, with fewer downstream surprises.
Where Qlik stands apart
A lot of vendors talk about being “AI-ready.” What customers need is a way to make it operational.
Qlik’s approach is different in a few important ways.
We bring governance into the analytics experience. Quality, ownership, and context are not something you bolt on somewhere else, they’re part of how teams build and use data in analytics.
We make context usable, not theoretical. It’s not enough to store static metadata. Metadata needs activation which the Qlik platform can deliver. People need business meaning, usage context, and clear readiness signals so they can move quickly without creating risk.
We make reuse the default. When trusted data is easy to find and reuse, teams avoid duplicated effort, reduce inconsistency, and accelerate delivery across the organization.
We connect the foundation to AI responsibly. Through Qlik MCP Server, AI assistants and agents can access trusted data products through a governed layer, so you can scale AI without creating new shadow versions of the truth.
What this means for customers
If you lead data, this helps you standardize trust and make it visible.
If you lead analytics, this helps you deliver faster and trustworthy business outcomes with fewer rework cycles.
If you’re building AI and agentic workflows, this helps you reduce ambiguity at the source, so AI operates on datasets that are validated, explainable, and aligned with the way your business works.
That’s what we mean by Making Data Work for AI, a foundation that is trusted, contextual, and ready for what comes next.
Learn more
Watch Mike Tarallo’s explainer video
Read the Community blog for the full walkthrough