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 ability to deliver AI-ready, trusted data depends less on new tools and more on how effectively DataOps are automated, governed, and observed.

With the right automation framework in place, infrastructure can react as conditions change, without human intervention.

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.