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The future of data analytics and AI education has just become even more accessible. The Qlik Academic Program is excited to announce a major upgrade to Qlik Product licenses available for educators and students worldwide. Participants in the program will now receive access to Premium Qlik Cloud Analytics and Premium Qlik Talend Cloud, providing an even more powerful environment for teaching, learning, and innovating with data.

We are excited to share that Qlik has been recognized as a Leader in the 2026 Gartner Magic Quadrant for Analytics & Business Intelligence Platforms, recognized for both our Completeness of Vision and Ability to Execute. Download the complimentary report here. Sixteen years. Our Sweet Sixteen, if you're counting. I'd argue it's less a milestone for us and more a proof point.

One line from Snowflake Summit 2026 stood out above everything else. Christian Kleinerman, EVP of Product at Snowflake: "We do not want any of you spending money with Snowflake, in any use case, if you are not getting more value in return." It's a refreshing commitment, and it points directly at the cost efficiency conversation we've been having with customers around open lakehouse architectures. Here's the core argument: data movement doesn't directly generate value.

There's a pattern we see repeatedly in enterprise AI projects. A team identifies a compelling use case. They build the model. They staff the project. Then they spend the next six to eighteen months trying to solve a problem that was never on the roadmap: their data isn't ready. Not because it doesn't exist. It exists everywhere: in cloud warehouses, on-premises databases, SaaS platforms, and data lakes across multiple regions.

There’s a moment in every data team’s journey when a bill arrives with unexpected costs. It might be an egress charge you didn’t see coming, a query cost that spiralled when usage spiked, or the quiet realization that migrating your data to a different platform would take months of work and a significant budget. If you’ve been there, you know what vendor lock-in really costs, and it’s not just dollars.

At Qlik Connect, one question kept coming up in conversations with leaders: “Which AI vendor should we pick?” But I think that’s the wrong starting point. The better question is: What kind of company do you want to become over the next three years? Right now, most organizations are heading down one of two paths with AI. Some are bolting it onto existing workflows to improve efficiency.

What marketing mastered years ago, product teams are only now beginning to understand. For decades, marketing has operated on a simple but powerful principle: don't make your customers come to you, go to them. Meet them on the channels they already use, speak in the language they already speak, and show up where they already spend their time. The result was omni-channel marketing, a discipline that transformed how brands engage with the world.

At Qlik Connect, one of the big messages we’re putting in front of customers is this: you’re closer to agentic AI than you think. I believe that because a lot of our customers already have more of the foundation in place than they may realize. If you have been working to improve data quality, strengthen governance, connect data across the business, and move analytics beyond reporting into real decision support, you are already building the conditions agentic AI needs to deliver value.

“The data integration landscape is currently undergoing a seismic shift. We are moving away from an era of human-led data pipelines and entering the age of Agentic Data Management.

One of the things I have learned spending time with enterprise data and analytics teams is that insight without proximity to action is only half the job. You can build a beautiful dashboard, surface a critical pattern, or flag a risk in real time, and still have the insight die on a slide before it ever changes what happens next. The gap between "we know this" and "we did something about it" is one of the most persistent problems in enterprise software.