Countless organizations have adopted modern technologies, from intelligent automation to AI and ML, to increase operational efficiency in the past several years. Indeed many of these approaches have been met with great success. However, as any site reliability engineer (SRE) or DevOps team member knows, forward-thinking changes to IT infrastructure have unintended side effects. As tech stacks expand, platform technologies improve and data becomes more ephemeral, a tenuous relationship with system uptime evolves. Welcome to the availability crunch.
To achieve maximum availability, IT leaders must employ domain-agnostic solutions that identify and escalate issues across all telemetry points. These technologies, which we refer to as Artificial Intelligence for IT Operations, create convergence — in other words, they provide IT and DevOps teams with the full picture of event management and downtime. Instead of handling a myriad of events and state-changes, AIOps tools provide teams with the context for those changes. And with that context, they are empowered to quickly and efficiently resolve issues, maintaining higher availability in the process.
Here is why convergence is critical for all organizations.
Exploring the Close Relationship Between Availability and Convergence
Most IT leaders acknowledge the importance of availability. Case in point: according to Moogsoft’s State of Availability report, engineering teams spend more time on monitoring than any other task (more even than vital responsibilities like automation, cloud adoption and testing/QA). Leaders often understand monitoring as a powerful method to prevent downtime because it allows human technicians to catch errors before they become dangerous. Ostensibly, at least.
Yet 45% of issues are reported by customers, not tools, and one-fourth of teams breach their service level objectives (SLOs) due to extended system downtime. This statistic suggests that monitoring is no longer enough to maintain availability.
But what do these issues have to do with convergence? Many organizations face extended outages at least partly because their data architecture is highly fragmented and complex. Instead of relying on a single domain-agnostic tool to synthesize the nature of data errors, these organizations likely rely on point solutions that only provide part of the necessary context. As a result, their system infrastructure is siloed, and system-breaking issues obscurely take root. Organizations with these issues have yet to achieve convergence.
In fact, most organizations have yet to reach full data convergence. Thanks to the complex nature of modern data, most enterprises inevitably juggle disparate data types gathered from various tools. This complicates the process of data analysis and extrapolation — and as a result, jeopardizes uptime. And yet availability is a key requirement for establishing success. Enterprises with low availability often lose revenue and prevent their consumers/constituents from accessing vital offerings, from goods and services to transportation and healthcare.
How to Prioritize Convergence in Your Organization
In a perfect world, all data would be of the same type, and contextuality would be far less complicated. But as our modern business environment primarily exists in the digital world, it is only natural that supporting system uptime requires a more advanced helping hand.
According to Moogsoft research, the average engineering team deals with a staggering 16 monitoring tools. That equates to an avalanche of complex data capable of tanking a system under the right circumstances. Leaders should prioritize establishing a 360-degree view of their organization’s cloud applications to keep up with these varied data sources. Management tools — especially AIOps — are helpful here because they integrate with large tech stacks and ingest data to create a simulacrum of convergence. In other words, even data from varied sources on different servers can be processed as one.
Here are a few factors leaders should consider when deciding which tool to interpolate into their organization’s tech stack.
Domain agnosticism
Leaders seeking a comprehensive application deployment and management solution should consider the benefits of a domain-agnostic approach to AIOps. Domain agnosticism in AIOps provides a generalized approach to application performance management. Instead of localized control of two or three isolated tools, domain-agnostic AIOps protects system-wide operations, collating data from various sources. This is critical for achieving convergence.
Data analysis > data collection
Monitoring tools are helpful but only go so far. If IT leaders feel their department is wasting time collecting data — or neglecting to enact impactful system-wide changes thanks to said data — they should adopt a data analysis tool, not a data synthesis tool. The difference? Monitoring tools provide information, while data analysis tools provide solutions.
High-quality AI and ML
Management tools that rely on AI and machine learning (ML) provide peace of mind because they quickly adapt to emerging threat patterns and organizational infrastructure. That means administrators do not have to worry about manual algorithm entry. They do not have to trust flawed logic patterns, either — instead of falling back onto pre-programmed, if this, then that patterns of threat detection, AI-based management tools learn and grow alongside an organization’s system and IT environment.
IT leaders who carefully consider leading AIOps solutions will find that convergence can be achieved, but only when all events and incidents are processed and contextualized. Piecemeal solutions jeopardize an IT or DevOps team’s ability to process errors in a timely way, which in turn leads to more downtime. Prioritizing the right toolkit should be an IT leader’s top priority going into the new year. And given the importance of availability in our highly digital world, it is crucial IT leaders start adopting that toolkit today.
