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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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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

Hot Topics

The Latest

From growing reliance on FinOps teams to the increasing attention on artificial intelligence (AI), and software licensing, the Flexera 2025 State of the Cloud Report digs into how organizations are improving cloud spend efficiency, while tackling the complexities of emerging technologies ...

Today, organizations are generating and processing more data than ever before. From training AI models to running complex analytics, massive datasets have become the backbone of innovation. However, as businesses embrace the cloud for its scalability and flexibility, a new challenge arises: managing the soaring costs of storing and processing this data ...

Despite the frustrations, every engineer we spoke with ultimately affirmed the value and power of OpenTelemetry. The "sucks" moments are often the flip side of its greatest strengths ... Part 2 of this blog covers the powerful advantages and breakthroughs — the "OTel Rocks" moments ...

OpenTelemetry (OTel) arrived with a grand promise: a unified, vendor-neutral standard for observability data (traces, metrics, logs) that would free engineers from vendor lock-in and provide deeper insights into complex systems ... No powerful technology comes without its challenges, and OpenTelemetry is no exception. The engineers we spoke with were frank about the friction points they've encountered ...

Enterprises are turning to AI-powered software platforms to make IT management more intelligent and ensure their systems and technology meet business needs for efficiency, lowers costs and innovation, according to new research from Information Services Group ...

The power of Kubernetes lies in its ability to orchestrate containerized applications with unparalleled efficiency. Yet, this power comes at a cost: the dynamic, distributed, and ephemeral nature of its architecture creates a monitoring challenge akin to tracking a constantly shifting, interconnected network of fleeting entities ... Due to the dynamic and complex nature of Kubernetes, monitoring poses a substantial challenge for DevOps and platform engineers. Here are the primary obstacles ...

The perception of IT has undergone a remarkable transformation in recent years. What was once viewed primarily as a cost center has transformed into a pivotal force driving business innovation and market leadership ... As someone who has witnessed and helped drive this evolution, it's become clear to me that the most successful organizations share a common thread: they've mastered the art of leveraging IT advancements to achieve measurable business outcomes ...

More than half (51%) of companies are already leveraging AI agents, according to the PagerDuty Agentic AI Survey. Agentic AI adoption is poised to accelerate faster than generative AI (GenAI) while reshaping automation and decision-making across industries ...

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Real privacy protection thanks to technology and processes is often portrayed as too hard and too costly to implement. So the most common strategy is to do as little as possible just to conform to formal requirements of current and incoming regulations. This is a missed opportunity ...

The expanding use of AI is driving enterprise interest in data operations (DataOps) to orchestrate data integration and processing and improve data quality and validity, according to a new report from Information Services Group (ISG) ...