Knowledge base

Conversational BI in Microsoft Fabric replaces static dashboards with natural-language insights, letting users ask questions directly through Microsoft Copilot and get faster access to the right reports and data. For reliable, data-driven answers, the Fabric Data Agent—built on a Power BI semantic model—delivers accuracy, business logic reuse, and controlled Conversational BI at scale. For advanced scenarios, MCP Orchestration layers intelligent reasoning on top of the Data Agent, enabling complex analysis and decision support beyond simple Q&A.

Vena Solutions has announced its intention to acquire Acterys, a leading provider of Power BI–based planning, analytics, and application development solutions. This strategic move marks an important step forward in the evolution of enterprise performance management—bringing together finance and IT in a more connected and actionable way.

This insight explores how QVD and Parquet compare in terms of file size and load performance, helping you make informed decisions based on your data architecture needs.

Choosing your organization’s cloud analytics platform is not only a question of available features and performance. It is also a question of governance fit, security and maintainability.  Qlik Cloud provides a strong baseline with audited controls, encryption by default, and layered governance that is administered with relative ease. Optional features such as Customer Managed Keys and IP allowlists are available should your policies or business context demand additional control.

In the last two years, most organisations have successfully deployed their first "Chat with your Data" pilots. These systems, built on Traditional RAG (Retrieval-Augmented Generation), are excellent librarians. If you ask, "What is our travel policy?", they can instantly find the PDF, read page 12, and summarise the allowance for dinner expenses. But what happens when you ask a question that requires reasoning, not just reading? "How will the delay in the supplier shipment from Shanghai impact our Q3 production targets for the Alpha product line, considering our current inventory levels?" A traditional AI will fail here. It will find documents mentioning "Shanghai," "Q3," and "Alpha," but it won't understand the causal chain. It sees words; it doesn't see the relationships between your supplier, your inventory, and your production schedule. To move from "AI that reads" to "AI that thinks," we need to upgrade from Vector Search to GraphRAG.