Business analytics has changed. Now, it answers back. Meet Databox AI, AI-powered analytics for teams that need answers now. Ask your data anything with Genie, your AI analyst. Don’t just see numbers—understand what changed with AI Performance Summaries. Bring your data into your favorite AI tools with Databox MCP.

Most marketing teams track traffic and leads, but rarely connect the two to understand whether their content is attracting the right audience. In this walkthrough, Rick Kranz, Director of the AI Marketing Lab, demonstrates a powerful weekly growth system that cross-references website traffic, Google Search Console data, and CRM leads to identify which content truly drives ideal customer profile (ICP) engagement.

Banks and credit unions face mounting pressure to modernize operations, elevate member experiences, and keep pace with regulatory demands. Discover how banks are delivering an agile data strategy using Qlik and Snowflake and improve member service, streamline operations, and manage compliance changes. Download our joint ebook and learn:→ How to build a future-ready data architecture→ Ways to drive accuracy and faster decision-making→ How to deliver timely, personalized member experiences→ Practical strategies you can implement right away.

This week we are bringing you the QlikView to Qlik Sense Converter tool. The QlikView to Qlik Sense converter tool is a breakthrough new offering from Qlik — it's the first tool to deliver true 1-to-1 automated conversion of QlikView application UIs directly into Qlik Cloud Analytics.

What happens when AI stops advising and starts acting? Agentic AI promises autonomy, speed, and a new level of intelligence in how systems operate. But as these systems begin to pursue goals and make decisions, the risks become harder to predict. In this episode of The AI Forecast, Paul Muller sits down with futurist Nell Watson, AI ethics expert and co-author of “Safer Agentic AI: Principles and Responsible Practices,” to explore what safe, responsible AI looks like in this new era.

In this video, Dipankar breaks down how Apache Iceberg works under the hood - starting from the limitations of Hive-style tables to why Iceberg was built in the first place. He covers: Why Hive-based tables break at scale (Netflix example) How object storage changes the problem (S3 behavior, listing, throttling) Iceberg architecture (catalog, metadata, snapshots, manifests, data files) How query planning works step by step Why Iceberg is a specification — not an execution engine.