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Observability Is Key to Minimizing Service Outages, but What's Next for the Technology

Michael Nappi
ScienceLogic

IT service outages are more than a minor inconvenience. They can cost businesses millions while simultaneously leading to customer dissatisfaction and reputational damage. Moreover, the constant pressure of dealing with fire drills and escalations day and night can take a heavy toll on ITOps teams, leading to increased stress, human error, and burnout.

Observability promises to solve these problems by enabling quick incident identification and understanding, leading to reduced mean-time-to-repair (MTTR). However, while many approaches to observability exist, not all are created equal. Many current observability best practices fail to deliver on the promise of comprehensive hybrid IT visibility, intelligent insights, and a reduction in manual interventions by ITOps teams.

In order to ensure organizations can secure the holistic view of the entire IT environment required to tap into these benefits, they first have to understand observability's role.

What is Observability?

Observability is a concept from operations theory that suggests the internal state of an IT system, including issues and problems, can be deduced from the data the system generates. Unlike infrastructure monitoring, which only tells IT teams whether a system is working or not, observability provides contextual data into why it's not working.

Observability is particularly important in today's modern hybrid IT environments that utilize microservices architectures that span potentially thousands of containers. The ever-increasing level of complexity in such systems means that whenever a problem arises, IT teams may spend several hours or even days attempting to identify the root cause. However, with the right observability tools, engineers can swiftly identify and resolve problems across the tech stack.

Observability tools operate systematically, monitoring user interactions and key service metrics such as load times, response times, latency, and errors. With this data, ITOps teams can pinpoint the location and timing of issues within the system. Engineers then work backward by analyzing traces and/or logs to determine potential triggers and details that could contribute to the problem, such as software updates or spikes in traffic.

Without the holistic visibility afforded by observability, maintenance and MTTR efforts would be significantly hindered, negatively impacting business operations and customer satisfaction. However, organizations looking to reap the benefits of global IT observability may first have to overcome a few challenges prior to implementation.

Barriers to Observability

Despite growing interest in implementing a culture of observability, modern hybrid IT estates still face significant obstacles to achieving effective observability strategies.

1. Manual Processes

For some organizations, observability can still be a highly manual and brute-force process. While certain tools streamline the collection, search, and visualization of data, they still rely on human analysis and understanding to identify the root cause of the issue. This approach can be time-consuming and error-prone, leading to longer resolution times and increased downtime.

2. Data Proliferation

The amount of data generated has increased significantly in recent years, making it harder to observe and analyze. According to IDC's 2017 forecast, worldwide data is expected to increase tenfold by 2025. Although observability tools can help ITOps teams collect and organize this vast amount of data, the main challenge is still the limitations of the human brain. Humans must still make sense of the overwhelming volume of traces and logs coming their way — before service is impacted.

3. Modern Software Delivery

Engineers must also deal with the speed of digitization and the constantly evolving IT landscape.

CI/CD delivery practices mean that software systems are never static. Even if IT teams comprehend what could go wrong today, that knowledge becomes obsolete as the software environment changes from one week to the next.

In the face of these challenges, a new approach to observability is needed. One that combines the power, intelligence, and automation of AI and ML into the observability strategy.

What is AI/ML-Powered Observability?

When organizations use AI and ML for observability, they can benefit from an intelligent and automated system that provides complete visibility of the hybrid IT environment and identifies and flags any issues with minimal to no human intervention.

That's nothing new, but most AI/ML approaches to observability stop there. Next-generation observability leveraging automated insights goes a step further.

This automation-powered observability is like an MRI for the IT estate. It doesn't just detect symptoms of problems but provides an in-depth analysis that accurately identifies the root cause of any issue, exponentially faster and with elevated accuracy. This includes identifying new or novel problems that have never been encountered before — all without human intervention. Think of it as "automated root cause analysis."

Finally, the system can take user-driven or automated action to resolve the problem.

Observability's End Goal: A Self-Healing, Self-Optimizing IT Estate

AI/ML-powered observability provides enriched insights that go beyond just "monitoring" or "observing" the IT estate. These insights allow for more advanced functionalities that work alongside humans to reduce IT complexity and manual effort and ultimately self-heal and self-optimize the environment.

By leveraging automated observability, organizations can confidently build and scale more complex IT infrastructure, integrate technologies with ease, and deliver elegant user and customer experiences — without risks or complications.

Michael Nappi is Chief Product Officer at ScienceLogic

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Observability Is Key to Minimizing Service Outages, but What's Next for the Technology

Michael Nappi
ScienceLogic

IT service outages are more than a minor inconvenience. They can cost businesses millions while simultaneously leading to customer dissatisfaction and reputational damage. Moreover, the constant pressure of dealing with fire drills and escalations day and night can take a heavy toll on ITOps teams, leading to increased stress, human error, and burnout.

Observability promises to solve these problems by enabling quick incident identification and understanding, leading to reduced mean-time-to-repair (MTTR). However, while many approaches to observability exist, not all are created equal. Many current observability best practices fail to deliver on the promise of comprehensive hybrid IT visibility, intelligent insights, and a reduction in manual interventions by ITOps teams.

In order to ensure organizations can secure the holistic view of the entire IT environment required to tap into these benefits, they first have to understand observability's role.

What is Observability?

Observability is a concept from operations theory that suggests the internal state of an IT system, including issues and problems, can be deduced from the data the system generates. Unlike infrastructure monitoring, which only tells IT teams whether a system is working or not, observability provides contextual data into why it's not working.

Observability is particularly important in today's modern hybrid IT environments that utilize microservices architectures that span potentially thousands of containers. The ever-increasing level of complexity in such systems means that whenever a problem arises, IT teams may spend several hours or even days attempting to identify the root cause. However, with the right observability tools, engineers can swiftly identify and resolve problems across the tech stack.

Observability tools operate systematically, monitoring user interactions and key service metrics such as load times, response times, latency, and errors. With this data, ITOps teams can pinpoint the location and timing of issues within the system. Engineers then work backward by analyzing traces and/or logs to determine potential triggers and details that could contribute to the problem, such as software updates or spikes in traffic.

Without the holistic visibility afforded by observability, maintenance and MTTR efforts would be significantly hindered, negatively impacting business operations and customer satisfaction. However, organizations looking to reap the benefits of global IT observability may first have to overcome a few challenges prior to implementation.

Barriers to Observability

Despite growing interest in implementing a culture of observability, modern hybrid IT estates still face significant obstacles to achieving effective observability strategies.

1. Manual Processes

For some organizations, observability can still be a highly manual and brute-force process. While certain tools streamline the collection, search, and visualization of data, they still rely on human analysis and understanding to identify the root cause of the issue. This approach can be time-consuming and error-prone, leading to longer resolution times and increased downtime.

2. Data Proliferation

The amount of data generated has increased significantly in recent years, making it harder to observe and analyze. According to IDC's 2017 forecast, worldwide data is expected to increase tenfold by 2025. Although observability tools can help ITOps teams collect and organize this vast amount of data, the main challenge is still the limitations of the human brain. Humans must still make sense of the overwhelming volume of traces and logs coming their way — before service is impacted.

3. Modern Software Delivery

Engineers must also deal with the speed of digitization and the constantly evolving IT landscape.

CI/CD delivery practices mean that software systems are never static. Even if IT teams comprehend what could go wrong today, that knowledge becomes obsolete as the software environment changes from one week to the next.

In the face of these challenges, a new approach to observability is needed. One that combines the power, intelligence, and automation of AI and ML into the observability strategy.

What is AI/ML-Powered Observability?

When organizations use AI and ML for observability, they can benefit from an intelligent and automated system that provides complete visibility of the hybrid IT environment and identifies and flags any issues with minimal to no human intervention.

That's nothing new, but most AI/ML approaches to observability stop there. Next-generation observability leveraging automated insights goes a step further.

This automation-powered observability is like an MRI for the IT estate. It doesn't just detect symptoms of problems but provides an in-depth analysis that accurately identifies the root cause of any issue, exponentially faster and with elevated accuracy. This includes identifying new or novel problems that have never been encountered before — all without human intervention. Think of it as "automated root cause analysis."

Finally, the system can take user-driven or automated action to resolve the problem.

Observability's End Goal: A Self-Healing, Self-Optimizing IT Estate

AI/ML-powered observability provides enriched insights that go beyond just "monitoring" or "observing" the IT estate. These insights allow for more advanced functionalities that work alongside humans to reduce IT complexity and manual effort and ultimately self-heal and self-optimize the environment.

By leveraging automated observability, organizations can confidently build and scale more complex IT infrastructure, integrate technologies with ease, and deliver elegant user and customer experiences — without risks or complications.

Michael Nappi is Chief Product Officer at ScienceLogic

Hot Topics

The Latest

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...

An overwhelming majority of IT leaders (95%) believe the upcoming wave of AI-powered digital transformation is set to be the most impactful and intensive seen thus far, according to The Science of Productivity: AI, Adoption, And Employee Experience, a new report from Nexthink ...

Overall outage frequency and the general level of reported severity continue to decline, according to the Outage Analysis 2025 from Uptime Institute. However, cyber security incidents are on the rise and often have severe, lasting impacts ...