Continuous Intelligence platforms leveraging multiple data sources can surface performance issues immediately, whenever they emerge.
Once upon a time, IT operations teams could get away with taking hours or even days to identify and resolve performance issues in their on-prem data centers.
For companies building cloud-native applications as part of their digital transformation efforts, those days are gone. Modern, cloud-native applications powering today’s digital experiences are complex software environments that change constantly. To ensure their reliability and security, developers and operations professionals need real-time analytics and insights to monitor, troubleshoot, and address security threats to ensure the digital experiences remain available, performant, and secure.
That’s why Continuous Intelligence has become the cornerstone of modern IT functions, migrating workloads to the cloud, and building and operating revenue-generating, cloud-native applications. Here’s a primer on what Continuous Intelligence means, why it’s so critical in modern environments and how to add Continuous Intelligence to your team’s management strategy.
The challenges of modern performance management
There are two main reasons why IT organizations need Continuous Intelligence: very high digital experience user expectations and very complex cloud-native application environments.
Steep user expectations
The first stems from the steep performance expectations of today’s employees, customers, and other users. Consider data points such as:
40 percent of users will abandon a website that takes longer than three seconds to load.
53 percent of users will abandon a mobile app that fails to load in three seconds.
IoT devices and applications typically require latency rates no higher than 50 milliseconds and sometimes as low as 10 milliseconds.
Availability rates above 99.1 percent have become the norm in the modern cloud.
The list could go on, but the point is hopefully clear enough: the margin of error for meeting modern user expectations is exceedingly thin. In some cases, you have just fractions of a second to identify and fix a problem before it turns into a critical application or service disruption.
IT environment complexity
Meanwhile, IT environments have grown so much more complex over the past decade that managing performance issues is considerably more difficult than it once was.
If you’re a modern organization today, you operate a microservices application architecture that runs each application’s components inside containers. These containers are managed by orchestration services like AWS EKS or Kubernetes. They are hosted in a public cloud (e.g., AWS, Azure, or Google Cloud), where you also have an object storage service that houses your application’s data. There are dozens of layers and moving pieces within a stack like this, and they all interact with and depend on one another in complex ways. Since each of these layers represents a level of abstraction given they are operating in virtualized environments with automated processes, visibility in the environment is very difficult. The benefits here are more flexibility and more scalability for those applications, as they can scale up to meet customer demand
quickly.
Compare that to the type of environment you might have run a decade ago. Your application was almost certainly a monolith and could well have been running on bare-metal servers. Maybe you used virtual machines, but even that would have been considered complex by the standards of the time. Ten years ago, almost no one was using containers, Kubernetes was just a random Greek word, and cloud computing was considered state of the art, not the de facto way to deploy infrastructure. Making any change to an application meant taking the entire app out of service, and scaling up meant buying bigger hardware.
