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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

Businesses that face downtime or outages risk financial and reputational damage, as well as reducing partner, shareholder, and customer trust. One of the major challenges that enterprises face is implementing a robust business continuity plan. What's the solution? The answer may lie in disaster recovery tactics such as truly immutable storage and regular disaster recovery testing ...

IT spending is expected to jump nearly 10% in 2025, and organizations are now facing pressure to manage costs without slowing down critical functions like observability. To meet the challenge, leaders are turning to smarter, more cost effective business strategies. Enter stage right: OpenTelemetry, the missing piece of the puzzle that is no longer just an option but rather a strategic advantage ...

Amidst the threat of cyberhacks and data breaches, companies install several security measures to keep their business safely afloat. These measures aim to protect businesses, employees, and crucial data. Yet, employees perceive them as burdensome. Frustrated with complex logins, slow access, and constant security checks, workers decide to completely bypass all security set-ups ...

Image
Cloudbrink's Personal SASE services provide last-mile acceleration and reduction in latency

In MEAN TIME TO INSIGHT Episode 13, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud networking strategy ... 

In high-traffic environments, the sheer volume and unpredictable nature of network incidents can quickly overwhelm even the most skilled teams, hindering their ability to react swiftly and effectively, potentially impacting service availability and overall business performance. This is where closed-loop remediation comes into the picture: an IT management concept designed to address the escalating complexity of modern networks ...

In 2025, enterprise workflows are undergoing a seismic shift. Propelled by breakthroughs in generative AI (GenAI), large language models (LLMs), and natural language processing (NLP), a new paradigm is emerging — agentic AI. This technology is not just automating tasks; it's reimagining how organizations make decisions, engage customers, and operate at scale ...

In the early days of the cloud revolution, business leaders perceived cloud services as a means of sidelining IT organizations. IT was too slow, too expensive, or incapable of supporting new technologies. With a team of developers, line of business managers could deploy new applications and services in the cloud. IT has been fighting to retake control ever since. Today, IT is back in the driver's seat, according to new research by Enterprise Management Associates (EMA) ...

In today's fast-paced and increasingly complex network environments, Network Operations Centers (NOCs) are the backbone of ensuring continuous uptime, smooth service delivery, and rapid issue resolution. However, the challenges faced by NOC teams are only growing. In a recent study, 78% state network complexity has grown significantly over the last few years while 84% regularly learn about network issues from users. It is imperative we adopt a new approach to managing today's network experiences ...

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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 ...