Move Over Siloed IT Workflows, Intelligent Observability Is the Hub of Context and Collaboration

Adam Frank
Moogsoft

In the era of observability, systems across your organization accumulate vast amounts of data about themselves — too much for IT teams to manage at the pace which containerized and cloud IT changes. And as data sources increase, silos emerge in the form of various telemetry and monitoring tools meant to aggregate that telemetry. These systems don't talk to each other, causing alerts to run amok. For SREs, the mental aerobics of correlating these alerts into insights constitutes toil — tedious, manual work spotting, deciphering and resolving events. Ultimately, this toil eats away at productive ways of working, stealing SREs' valuable time and resources that could be dedicated to building new, innovative services.

But intelligent observability can eliminate this toil by seamlessly integrating data across silos and automating the detection of contextual insights, actionable information and a platform for learning to create a unified view of all data. After all, you need all of this data to understand your customers' experience.


Integration Across IT Data Sources

In current workflows, SREs must examine telemetry from across silos — logs, metrics, traces, individual monitoring tools and more — and manually spot anomalies or system change events in the data. Because they're working off siloed data sources, they then need to de-dupe the same event appearing across different tools and forms of telemetry and correlate those related events into individual incidents. And it doesn't end there. Next, they must determine the cause of those incidents and take action on them, working alongside other teams to resolve the issues.

As you can imagine, doing this across an endless amount of data takes a great deal of time and effort — keeping your backlog full of untouched innovative projects that increase customer value. But, with intelligent observability providing a unified view of all IT data, SRE teams can quickly see correlations and pluck the needle (the root causes of incidents and alerts) from the haystack (non-critical event noise), then move on to the work they want to do.

Activate AI and Automation to Unify Data

So, this all sounds like a dream — but how do we practically unify data at scale? AI allows the automation of collecting, filtering, organizing and analyzing data. This not only reduces event noise so SRE teams can operate more efficiently, but also creates context and actionability from that data.

Integrating with CMDBs, asset management DBs and discovery systems yield bits of information useful in deriving context — like location, department, business criticality, service relationships, owner and more. This context offers situational awareness so that SREs can get a handle on interdependencies and relationships that allow them to resolve big incidents faster — ultimately automating away the toil with AI.

For example, if someone makes a change within system A that triggers an issue in system B, it's generally a very manual and cumbersome process to determine why the issue in system B is taking place. But, with a unified data source and added context from AI, SREs have visibility into how system A influences system B, giving them a complete picture to quickly pinpoint the root cause of the issue.

Clean Up Data for Actionability

Not every event is created equal. Not only does context allow situational awareness for SREs, but it also offers space for deep learning algorithms to assess priorities for event alerts to help decipher what is important and what is not. Noise reduction with an algorithmically-developed entropy threshold separates the wheat from the chaff. Out of previously siloed data and contextual insights, SRE teams will recognize events that need action and take immediate steps to resolve what matters most — like issues directly impacting the end-user experience. On top of that, intelligent observability platforms allow for quick action by including integrations for collaboration between teams to resolve incidents quicker and more effectively.

Leverage a Platform for Learning

Contextualizing and correlating alerts puts SRE teams in action, but they need a platform to manage this process. Processed data placed into a unifying hub becomes a platform to discover the real issues plaguing systems and the ability to preempt the next issue. This means SREs can not only fix problems that are currently bogging down their systems, but avoid similar issues in the future for better system performance.

More efficient IT workflows rely on the ability to defeat data silos. Intelligent observability platforms do this at scale, crossing silos, and using context and actionable information to best direct SRE teams' efforts. Without the toil of juggling data from across various tools and putting meaning to the data, SREs can look forward to delivering innovation, high-impact projects instead of diagnosing and fixing the same issues over and over.

Adam Frank is VP, Product & Design, at Moogsoft

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Adam Frank
Moogsoft

In the era of observability, systems across your organization accumulate vast amounts of data about themselves — too much for IT teams to manage at the pace which containerized and cloud IT changes. And as data sources increase, silos emerge in the form of various telemetry and monitoring tools meant to aggregate that telemetry. These systems don't talk to each other, causing alerts to run amok. For SREs, the mental aerobics of correlating these alerts into insights constitutes toil — tedious, manual work spotting, deciphering and resolving events. Ultimately, this toil eats away at productive ways of working, stealing SREs' valuable time and resources that could be dedicated to building new, innovative services.

But intelligent observability can eliminate this toil by seamlessly integrating data across silos and automating the detection of contextual insights, actionable information and a platform for learning to create a unified view of all data. After all, you need all of this data to understand your customers' experience.


Integration Across IT Data Sources

In current workflows, SREs must examine telemetry from across silos — logs, metrics, traces, individual monitoring tools and more — and manually spot anomalies or system change events in the data. Because they're working off siloed data sources, they then need to de-dupe the same event appearing across different tools and forms of telemetry and correlate those related events into individual incidents. And it doesn't end there. Next, they must determine the cause of those incidents and take action on them, working alongside other teams to resolve the issues.

As you can imagine, doing this across an endless amount of data takes a great deal of time and effort — keeping your backlog full of untouched innovative projects that increase customer value. But, with intelligent observability providing a unified view of all IT data, SRE teams can quickly see correlations and pluck the needle (the root causes of incidents and alerts) from the haystack (non-critical event noise), then move on to the work they want to do.

Activate AI and Automation to Unify Data

So, this all sounds like a dream — but how do we practically unify data at scale? AI allows the automation of collecting, filtering, organizing and analyzing data. This not only reduces event noise so SRE teams can operate more efficiently, but also creates context and actionability from that data.

Integrating with CMDBs, asset management DBs and discovery systems yield bits of information useful in deriving context — like location, department, business criticality, service relationships, owner and more. This context offers situational awareness so that SREs can get a handle on interdependencies and relationships that allow them to resolve big incidents faster — ultimately automating away the toil with AI.

For example, if someone makes a change within system A that triggers an issue in system B, it's generally a very manual and cumbersome process to determine why the issue in system B is taking place. But, with a unified data source and added context from AI, SREs have visibility into how system A influences system B, giving them a complete picture to quickly pinpoint the root cause of the issue.

Clean Up Data for Actionability

Not every event is created equal. Not only does context allow situational awareness for SREs, but it also offers space for deep learning algorithms to assess priorities for event alerts to help decipher what is important and what is not. Noise reduction with an algorithmically-developed entropy threshold separates the wheat from the chaff. Out of previously siloed data and contextual insights, SRE teams will recognize events that need action and take immediate steps to resolve what matters most — like issues directly impacting the end-user experience. On top of that, intelligent observability platforms allow for quick action by including integrations for collaboration between teams to resolve incidents quicker and more effectively.

Leverage a Platform for Learning

Contextualizing and correlating alerts puts SRE teams in action, but they need a platform to manage this process. Processed data placed into a unifying hub becomes a platform to discover the real issues plaguing systems and the ability to preempt the next issue. This means SREs can not only fix problems that are currently bogging down their systems, but avoid similar issues in the future for better system performance.

More efficient IT workflows rely on the ability to defeat data silos. Intelligent observability platforms do this at scale, crossing silos, and using context and actionable information to best direct SRE teams' efforts. Without the toil of juggling data from across various tools and putting meaning to the data, SREs can look forward to delivering innovation, high-impact projects instead of diagnosing and fixing the same issues over and over.

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Related Links

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The Latest

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Cloud outages are usually described as technical failures. When a service goes down, a dependency breaks, or a region has issues, the focus immediately shifts to infrastructure. But if you look closely at how these incidents actually unfold, the root cause is rarely the technology itself. It is almost always tied to decisions made earlier, during design, implementation, or day-to-day operations. The system behaves the way it was built. The real question is how it was built ...

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77% of leaders say their teams need AI skills urgently. 64% say their organization plans to train current employees rather than hire new ones. So far, so reasonable. The part that surprised me is who's been put in charge: 34% of those leaders say IT and engineering own the AI skills mandate. Learning and Development or HR own it at 7% of organizations. That's roughly five-to-one in favor of the people who understand the tools, over the people whose actual job is teaching adults how to learn new ones ...

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