Building a Clinical Decision Engine Platform with Red Hat Decision Manager (Case Study)

Companies that develop or support products in the field need to transition from reactive to predictive maintenance and service. Such a transition is possible using ubiquitous connectivity and predictive analytics on machine data. Here’s how.

Although edge architectures differ fundamentally from conventional cloud and on-premises environments, edge isn’t an alternative to traditional infrastructure strategies as much as it is an augmentation of them.

The relationships banks have with their customers can last for decades, but too many banks fail to use the data they have to provide personalized digital banking services. 

Every transaction, order, customer interaction, and internal operation depends on decisions. At a time when businesses are under pressure to improve efficiencies, cut costs, and be more responsive, automating decisions becomes essential. While many businesses have picked off the low-hanging fruit when automating, most need to add more. Specifically, businesses want to automate complex decisions, involving multiple steps, multiple data sources and many rules.

The next real shift with agentic AI will come from changing how work is done. Humans will move away from clicking through systems and toward commanding outcomes, with agentic layers coordinating execution across tools on behalf of the user.

The link between marketing and revenue is clearer than it used to be. That makes data issues harder to ignore, because the impact shows up directly in results. The data you need is already there. The question is whether it’s connected, consistent, and trusted enough to act on. That’s the work worth doing now.

Shadow AI isn’t going away. Enterprise that get out in front of it can convert the shadow AI problem into a competitive advantage with demonstrable ROI.

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