Systems | Development | Analytics | API | Testing

Looking back on 2025, one idea anchors everything we set out to accomplish: make data accessible and trusted so organizations can generate better decisions for their business, their customers, and the world.

For more than a decade, I have argued that the most valuable skill in a data-driven organisation is not access to information. It is the judgment to know when that information is wrong. AI has made that skill more important, not less. When I wrote about AI literacy in 2023, the pushback I heard most often was that the technology was not yet good enough for the question to matter. Now it is. AI can generate answers, summaries, recommendations, code, analysis, and increasingly, actions.

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.