RTInsights Team

Learn how AI-powered Automation can transform the integration lifecycle and why it makes sense to deploy it in your own organization.

Joe McKendrick, RTInsights Industry Insights Editor, and David Linthicum, Managing Director and Chief Cloud Strategy Officer at Deloitte, discuss the increased need for continuous intelligence in today’s tech-ladened modern world.

Investments in analytics and data management companies continues to be strong with many realizing significant late-stage funding rounds.

RTInsights editors Joe McKendrick, Lisa Damast, and Salvatore Salamone discuss why continuous intelligence is needed to address today's evolving cyberattacks.

RTInsights editors Joe McKendrick, Lisa Damast, and Sal Salamone discuss why continuous intelligence is needed to address the complexity of modern applications and operations.

RTInsights editors Joe McKendrick, Jim Connolly, Lisa Damast, and Sal Salamone discuss continuous intelligence (CI). What is it? Why is it getting so much attention now?

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.

As fintech companies disrupt the staid world of traditional banking, financial services institutions (FSIs) are trying to keep pace, be competitive, and become relevant by modernizing operations by leveraging the same technologies as the aggressive fintech startups. Such efforts include a move to cloud and the increased use of data to derive real-time insights for decision-making and automation.

In today’s dynamic marketplace, new applications that use data from multiple sources and deliver rapid insights constantly need to be created on very short notice. The challenge is how to have the flexibility to
rapidly develop and deploy new applications to meet fast-changing business requirements. The only way to ensure success is to use a dynamic architecture that delivers access to data, processing power, and analytics (including artificial intelligence and machine learning models) on demand.