The Race for a Unified Analytics Warehouse (White Paper)

The shift toward real-time decision-making at the edge is an evolution in how businesses operate. Closing the latency gap means smarter, safer, and more resilient operations.

AI agents represent an opportunity to go beyond isolated analytics projects and move toward self-improving, autonomous workflows that directly drive operational performance.

Scaling AI in industry requires the right technology, a practical understanding of industrial operations, and open interoperability with an organization’s existing data infrastructure.

The enterprise data stack is at an inflection point. Storing and streaming data is no longer enough. Businesses need a combination of streaming technologies, high-performance in-memory databases, and adaptive intelligence to support real-time enterprise operations.

In this week’s real-time analytics news: Oracle announced new agentic AI features for Oracle AI Database.

A look beyond the artificial intelligence hype: What will it take for real value to start showing up as companies implement AI?

AI agents are transforming Order Management Systems (OMS) from a static rules-based engine to a dynamic intelligence-based strategist.

Networking will play a more strategic role in enabling real-time digital operations and supporting the next generation of AI-powered applications.