Why Point, Reactive Monitoring & Automation Tools Don’t Work for Today’s Digital Businesses
Across industries and markets, personal interactions continue to be supplanted by the digital. Now, applications are where battles for customer loyalty can be won or lost. In the digital economy, it’s application quality that separates market victors from laggards. For today’s businesses, there’s a premium on delivering optimized user experiences—all the time and every time. While optimizing service levels and experience is critical, it seems to be getting more challenging to do every day.
Most enterprise-class business services now rely not only on traditional systems, including on-premises mainframes and distributed systems, but on a plethora of new, dynamic technologies, such as containers, cloud delivery models, virtual and software-defined components and more.
The volume, variety and velocity of data that needs to be managed, correlated and analyzed continues to grow dramatically. In the wake of initiatives like multi-cloud deployments, microservices development and Internet of Things (IoT) implementations, teams continue to see explosive growth in the operational data being generated. Ultimately, internal team members simply can’t keep pace.
Reactive, disjointed tools fuel more complexity
Exacerbating matters is that, as IT teams looked to manage their increasingly diverse environments, they’ve had to add more point monitoring tools and automation capabilities to the mix. These disjointed tool sets compound the complexity and challenges:
- Point monitoring tools result in reactive issue identification and alert fatigue. Working with dozens of tools, teams struggle with hundreds of thousands of alerts that feature a high rate of inaccuracy and redundancy. Lacking unified visibility that spans their hybrid environments, staff spend too much time inspecting various systems and domains in order to identify the root cause of issues. As a result, customer experience suffers while triage calls run for hours.
- Point automation capabilities don’t scale or work in complex environments. When organizations employ limited automation that is connected to domain-specific tools, they encounter a number of challenges. First, with these one-to-one integrations they can’t easily automate complex workflows that span multiple technology platforms and domains. Second, these integrations don’t work well in most cases. For example, an alert from a server monitoring tool can trigger server-related remediation, while the actual issue may stem from a network device.
Today’s IT teams can’t simply try to do the same things better. To ensure their complex, hybrid, interrelated and highly dynamic environments deliver an optimized user experience, operations teams must achieve fundamental breakthroughs in scale and efficiency. It’s no longer enough to just react a little faster when issues arise. Teams must gain the visibility needed to identify potential issues—and address them before they affect service levels. To contend with the explosive growth in data, complexity and user demands, IT teams need to adopt an Artificial Intelligence for IT operations (AIOps) platform that provides service-driven, autonomous remediation.
With these new AIOps platforms, teams can leverage machine-learning-based algorithms to predict potential issues that could affect service levels, perform automated root cause analysis and quickly run effective remediation across diverse, hybrid environments.
In my next blog post, I’ll cover the three key essentials of service-driven, autonomous remediation. If you’d like to learn more about how CA can help in your journey to autonomous remediation, check out this recent press announcement on the availability of CA Digital Experience Insights now combined with the power of CA Operational Intelligence and CA Automic Service Orchestration.