As defined in my first blog in this series, continuous intelligence applies streaming and historical analytics to both internal and external data, to adjust operational processes in real-time. It often integrates with application or machine data architectures, and assists ITOps, DevOps and customer engagement processes.

- Kevin Petrie in Five Steps to Succeed with Your Continuous Intelligence Strategy

November 11, 2020 (Blog)

As defined in my last blog, CI refers to a set of functions – leveraging homegrown code or vendor products – that together automate real-time and historical analytics in order to directly adjust operations. It is not full CI until you assemble all the pieces. You can extend many types of architectures to achieve CI. Two primary types are architectures focused on business application data, and those focused on machine-generated data.

- Kevin Petrie in The Art and Science of Architecting Continuous Intelligence

October 14, 2020 (Blog)

Continuous Intelligence (CI) integrates historical and real-time analytics to operate, monitor and tune systems of all types - cellular networks, theme parks, factories, banking infrastructure, you name it. CI generally encompasses data ingestion, transformation, analytics and decision recommendations. It casts a wider net than traditional BI because it incorporates contextual data to understand factors such as market behavior, social media trends and economic indicators. It also shortens time to action by linking directly to operational processes, which in turn generate more data to analyze.

- Kevin Petrie in Continuous Intelligence: the Nexus of Data Integration, Analytics and Operations

September 8, 2020 (Blog)