Systems | Development | Analytics | API | Testing

There's a conversation happening in a lot of data teams right now. It goes something like this: the lakehouse is clearly where things are heading, Apache Iceberg is becoming the format everyone wants to land in, and the AI use cases the business is asking for need clean, open, queryable data.

One message coming out of Dreamforce caught my attention: context is moving to the center of the enterprise AI conversation. Good. We’ve been arguing for a while that context is what turns AI from an impressive interface into something genuinely useful for the enterprise. Salesforce’s AIforce approach is about bringing its data, workflows, business logic, semantics, permissions, security, and governance into the places people increasingly want to work, including Claude and Slack.

Every data platform decision has a default setting, and the default is wait. Not because leaders think the current stack is great. Because “we’ll modernize next year” feels responsible. It sounds like discipline. It reads like you’re protecting the budget. Here’s the part that never makes it into that conversation: waiting is not the free option. It’s a spending decision, and it renews every month whether or not anyone signs off on it.

Two questions stall almost every agentic AI rollout: Is it secure? And what is this going to cost us? Both get harder to answer the longer you wait to ask them, and easier to answer than most teams assume. If you've already invested in a robust data analytics platform like Qlik, you're closer to a secure, governed, and cost-effective agentic AI deployment than the market's “rip and replace” narrative suggests.

Qlik has been named a Leader in the IDC MarketScape: Worldwide Data Intelligence Platform Software 2026 Vendor Assessment. I'll say the obvious part first: we're proud of this. Independent recognition matters, and this one reflects work happening across every team at Qlik. But analyst recognition on its own doesn't help you run your data estate.

Gartner predicts that 70% of today's data engineering tasks will be fully automated by 2030. I put that number to Tim Garrod, Qlik's Head of Product Management for data integration and quality, on a recent Qlik Insider session, and his answer is the one every CIO, CDO, and VP of IT should sit with: automation doesn't make the data engineer obsolete, it makes the good ones ten times more valuable. AI amplifies skilled judgment. It doesn't replace it.

Bob Dylan wrote those words in 1964 about a world in flux - where the old rules were being quietly rewritten, and the people who hadn't noticed yet were about to find out the hard way. He wasn't thinking about enterprise data architecture. But if you've been following SAP's moves on data access, extraction, and platform strategy over the past two years, those words might be landing a little closer to home than usual.

Qlik’s new CEO discusses why he joined the company, the opportunity he sees at the intersection of data and AI, and what customers and partners can expect from his leadership. Qlik CEO Saugata Saha recently sat down with Jessica Dubois, Senior Director of Global Tech Partners, for his first external conversation since joining the company.

When was the last time you made a major business decision and were completely certain the data behind it was accurate, complete, and trusted? For most organizations, that certainty is less common than it should be. Qlik and Amazon Quick together solve one of the biggest obstacles to AI adoption: knowing whether you can trust the output.

Earlier this month I spent time with a group of senior executives discussing the economics of AI: what it actually costs, where the value is and is not materialising, and what the organisations that are getting returns are doing differently from the ones that are not. That conversation encapsulates why this series is called Beyond the Budget. Not because cost does not matter. It does. But because the budget is where the consequences show up.