Real-time analytics is often approached as a technology upgrade. In practice, it is an organizational outcome. Organizations that succeed treat real-time analytics as the final stage of a deliberate progression.

In this week’s real-time analytics news: IBM announced IBM Sovereign Core, AI-ready sovereign-enabled software for enterprises, governments, and service providers.

In this week’s real-time analytics news: NVIDIA launched its new NVIDIA Rubin platform that accelerates AI workloads.

Autonomous BI represents a significant step toward truly intelligent enterprises where insights are continuously generated, contextualized, and delivered at the speed of business.

Retailers cannot meet modern consumer expectations through procurement alone. They need a holistic technology management strategy that prioritizes maintenance, security, compliance, resiliency, and visibility.

Businesses using self-service BI often find themselves in a tug of war between too much and too little control. Smart governance can empower enterprise teams to fearlessly derive the insights they need at the speed their business demands.

Dashboards helped us see our business, but they won’t run the future of robotic, algorithmic, sensor-powered enterprises now emerging. The companies that embrace this shift early will accelerate.

In this week’s real-time analytics news: Sovereign Cloud efforts in the EU are heating up. SUSE launched a Cloud Sovereignty Framework Self Assessment to help businesses understand gaps in their digital strategy.

The new Mplify Carrier Ethernet certifications provide independent, third-party validation of network performance for demanding AI workloads.

In this week’s real-time analytics news: The EuroHPC Joint Undertaking focuses on AI Gigafactories and sovereign AI.

Organizations that embrace event-driven architecture early will build more resilient, scalable, and maintainable AI systems. EDA enables greater resilience, scalability, and observability, which are essential for robust, enterprise-ready Agentic AI.

An AI security standard founded on the principles and practices outline in this article address all key risk areas. Such a standard also enables practical implementation of AI security best practices.