Observability Is Key to Minimizing Service Outages, but What's Next for the Technology
May 21, 2024

Michael Nappi

Share this

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
Share this

The Latest

June 18, 2024

With the rise of digital transformation and the increasing reliance on applications for business operations, the need for application performance management (APM) has become more critical ... This blog explains what APM is all about, its significance and key features ...

June 17, 2024

Generative AI (GenAI) has captured significant attention by redefining content creation and automation processes. Despite this surge in GenAI's popularity, it's crucial to highlight the continuous, vital role of machine learning (ML) in underpinning crucial business functions. This era is not about GenAI replacing ML; rather, it's about these technologies collaborating to supercharge intelligent automation across industries ...

June 13, 2024

As organizations continue to navigate their digital transformation journeys, the need for efficient, secure, and scalable data movement strategies has never been more critical ... In an era when enterprise IT landscapes are continually evolving, the strategic movement of data has become a cornerstone of maintaining agility, competitive edge, and operational efficiency ...

June 12, 2024

In May, New Relic published the State of Observability for IT and Telecommunications Report to share insights, statistics, and analysis on the adoption and business value of observability for the IT and telecommunications industries. Here are five key takeaways from the report ...

June 11, 2024
Over the past decade, the pace of technological progress has reached unprecedented levels, where fads both quickly rise and shrink in popularity. From AI and composability to augmented reality and quantum computing, the toolkit of emerging technologies is continuing to expand, creating a complex set of opportunities and challenges for businesses to address. In order to keep pace with competitors, avoiding new models and ideas is not an option. It's critical for organizations to determine whether an idea has transformative properties or is just a flash in the pan — a challenge tackled in Endava's new 2024 Emerging Tech Unpacked Report ...
June 10, 2024

The rapidly evolving nature of the industry, particularly with the recent surge in generative AI, can catch firms off-guard, leaving them scrambling to adapt to new trends without the necessary funds ... This blog will discuss effective strategies for optimizing cloud expenses to free up funds for emerging AI technologies, ensuring companies can adapt and thrive without financial strain ...

June 06, 2024

Software developers are spending more than 57% of their time being dragged into "war rooms" to solve application performance issues, rather than investing their time developing new, cutting-edge software applications as part of their organization's innovation strategy, according to a new report from Cisco ...

June 05, 2024

Generative Artificial Intelligence (GenAI) is continuing to see massive adoption and expanding use cases, despite some ongoing concerns related to bias and performance. This is clear from the results of Applause's 2024 GenAI Survey, which examined how digital quality professionals use and experience GenAI technology ... Here's what we found ...

June 04, 2024

Many times customers want to know why their measured performance doesn't match the speed advertised (by the platform vendor, software vendor, network vendor, etc). Assuming the advertised speeds are (a) within the realm of physical possibility and obeys the laws of physics, and (b) are real achievable speeds and not "click-bait," there are at least ten reasons for being unable to achieve advertised speeds. In situations where customer expectations and measured performance don't align, use the following checklist to help determine the reason(s) why ...

June 03, 2024

With so many systems potentially impacting applications performance, it is critical to find ways to separate insights from data that is often white noise. When cross-functional teams have clear alignment on what KPIs matter to them and their users' experiences, they can implement tools and processes that best support them. In the end, there must be collective ownership ...