From BI to Real-Time Intelligence: White Paper

Explore the top smart manufacturing trends for 2026, including AI, IoT, and automation. Learn how manufacturers are using these technologies to reduce costs, improve efficiency, and build resilient, data-driven operations.

In this week’s real-time analytics news: Qlik introduced a sovereign AI initiative and an expansion of its trust and governance capabilities for AI.

In this week’s real-time analytics news: StreamNative introduced Lakestream, a new architectural paradigm for lakehouse-native streaming.

As the pace and complexity of operations accelerate, organizations are rethinking architectures to enable real-time, human-in-the-loop decision-making.

Explore the top smart manufacturing trends for 2026, including AI, IoT, and automation. Learn how manufacturers are using these technologies to reduce costs, improve efficiency, and build resilient, data-driven operations.

Modern enterprise applications, especially those driven by real-time data, AI inference, and latency-sensitive user experiences, are exposing the limitations of a one-size-fits-all cloud strategy, forcing organizations to rethink workload placement across hybrid and distributed environments. The shift is no longer about “cloud vs. on-prem,” but about architecting for performance, cost predictability, and data gravity in a world where systems must operate continuously and adapt dynamically.

The AI revolution isn’t happening in GPUs or model architectures. It’s happening quietly in the data pipelines that feed them.

The future of AI in the enterprise is recognizing that intelligence, whether human or artificial, must be governed by clear, written code.