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Data Convergence is Critical to Achieving Maximum Availability

Phil Tee

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.

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Data Convergence is Critical to Achieving Maximum Availability

Phil Tee

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.

Hot Topics

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Industry experts offer predictions on how AI will evolve and impact technology and business in 2025. Part 3 covers AI's impact on employees and their roles ...

Industry experts offer predictions on how AI will evolve and impact technology and business in 2025. Part 2 covers the challenges presented by AI, as well as solutions to those problems ...

In the final part of APMdigest's 2025 Predictions Series, industry experts offer predictions on how AI will evolve and impact technology and business in 2025 ...

E-commerce is set to skyrocket with a 9% rise over the next few years ... To thrive in this competitive environment, retailers must identify digital resilience as their top priority. In a world where savvy shoppers expect 24/7 access to online deals and experiences, any unexpected downtime to digital services can lead to significant financial losses, damage to brand reputation, abandoned carts with designer shoes, and additional issues ...

Efficiency is a highly-desirable objective in business ... We're seeing this scenario play out in enterprises around the world as they continue to struggle with infrastructures and remote work models with an eye toward operational efficiencies. In contrast to that goal, a recent Broadcom survey of global IT and network professionals found widespread adoption of these strategies is making the network more complex and hampering observability, leading to uptime, performance and security issues. Let's look more closely at these challenges ...

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Broadcom

The 2025 Catchpoint SRE Report dives into the forces transforming the SRE landscape, exploring both the challenges and opportunities ahead. Let's break down the key findings and what they mean for SRE professionals and the businesses relying on them ...

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Catchpoint

The pressure on IT teams has never been greater. As data environments grow increasingly complex, resource shortages are emerging as a major obstacle for IT leaders striving to meet the demands of modern infrastructure management ... According to DataStrike's newly released 2025 Data Infrastructure Survey Report, more than half (54%) of IT leaders cite resource limitations as a top challenge, highlighting a growing trend toward outsourcing as a solution ...

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Gartner revealed its top strategic predictions for 2025 and beyond. Gartner's top predictions explore how generative AI (GenAI) is affecting areas where most would assume only humans can have lasting impact ...

The adoption of artificial intelligence (AI) is accelerating across the telecoms industry, with 88% of fixed broadband service providers now investigating or trialing AI automation to enhance their fixed broadband services, according to new research from Incognito Software Systems and Omdia ...

 

AWS is a cloud-based computing platform known for its reliability, scalability, and flexibility. However, as helpful as its comprehensive infrastructure is, disparate elements and numerous siloed components make it difficult for admins to visualize the cloud performance in detail. It requires meticulous monitoring techniques and deep visibility to understand cloud performance and analyze operational efficiency in detail to ensure seamless cloud operations ...