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Four Key Pillars to a Leading Observability Practice

Mimi Shalash
Splunk

Splunk's latest research reveals that companies embracing observability aren't just keeping up, they're pulling ahead. Whether it's unlocking advantages across their digital infrastructure, achieving deeper understanding of their IT environments or uncovering faster insights, organizations are slashing through resolution times like never before.

The companies achieving the most powerful business outcomes are recognized as "observability leaders," excelling across the four critical stages of observability: foundational visibility, guided insights, proactive response, and unified workflows. Achieving "leader" status in observability unlocks more than just visibility because it empowers organizations with a deep, actionable understanding of their digital ecosystems. This translates directly into business returns, delivering a 2.6x annual ROI. In practical terms, that means smoother operations, fewer disruptions, and more time spent innovating rather than firefighting.

Let's explore the four key pillars that form the foundation of a world-class observability practice:

1. AI: A critical observability tool to help remediate issues

Automation is a cornerstone of an effective observability practice and elevates operational efficiency. As IT environments expand and grow increasingly complex, maintaining visibility over the full ecosystem of tools and technologies presents a growing challenge. Combine this with the volume of alerts that ITOps and engineering teams manage daily and the manual intervention becomes unsustainable. AI and machine learning (ML) solutions reduce the cognitive load off teams by dynamically recalibrating baseline metrics and detecting anomalies that static thresholds might overlook.

The latest data from Splunk highlights just how impactful AI and ML solutions can be with 85% of respondents reporting resolving at least half of their alerts. Additionally, 65% rely on AIOps for automated root cause analysis, giving teams the intelligence they need to stay ahead of issues.

Becoming an observability leader requires more than just basic monitoring. It requires advanced solutions that understand the normal patterns of your IT environment to then automatically detect anomalies. Consider a straightforward scenario: an unexpected CPU spike occurs on a cluster node, triggering an alert that requires quick, actionable insights. With an advanced observability tool, the system might recommend redistributing workloads across nodes to prevent service disruptions. Or it could initiate an automated remediation workflow via integrations with orchestration tools, ensuring rapid resolution without manual intervention.

2. Build a dedicated platform engineering team

Integrating AI and ML solutions is just one piece of what sets observability leaders apart. The competitive edge lies in how these technologies are embedded into platform engineering. Platform engineering isn't just about tools and processes, it's about empowering software engineers to do what they do best: create. By adopting standardized toolchains, workflows, and self-service platforms, teams minimize time spent managing tools because they've codified making the right thing to do, the easy thing to do.

The research shows that 73% of organizations are already embracing these practices and 58% of observability leaders recognize that this isn't just a trend. Rather, it's becoming the hallmark for continuous innovation, enabling seamless automation, scalability, and enhanced developer experience.

3. Harness control of the telemetry pipeline

At the heart of platform engineering, beyond automation and scalability, it's about maintaining control. Knowing exactly where your data flows, where it originates, and staying firmly in control of it isn't just important, it's essential. Ownership over data is the lifeline of every organization. Today, over 75% of observability leaders have adopted OpenTelemetry, solidifying it as the industry standard for collecting and controlling critical data. This open framework seamlessly integrates with other tools, bringing together data from multiple sources for complete visibility.

The result? A flexible observability practice that fosters innovation, reduces dependencies, and empowers businesses to grow on their own terms. With OpenTelemetry, you're not just building observability, you're building the freedom to evolve and innovate without limits.

Tapping into OpenTelemetry's full potential isn't just about adopting the technology. It also requires having the right talent in place and that comes with its own set of challenges. The report indicates that many companies are struggling with a shortage of people possessing the expertise on the open source project. To bridge the gap, organizations should prioritize training existing team members on how to configure, manage, and optimize OpenTelemetry pipelines and build this strategy into bullet #2.

4. Remember that observability is a team sport

Lastly, organizations must learn that true observability isn't achieved in isolation. Nearly three-quarters (73%) of observability leaders said they saw an improvement in their mean time to resolve (MTTR) after combining their observability and security workflows and tools. This highlights the importance of keeping observability tools connected across teams. When teams have cross-functional visibility into each other's workflows, they're better equipped to solve issues faster and prevent them from happening again.

While every team — observability and security — share the ultimate goal of business success, they may not share the same immediate objectives. Finding common tools and testing shared data sources can help iron out workflow differences, paving the way for future convergence.

Scaling with observability

As cloud reliance grows and AI tools multiply, IT environments will become even more complex, leaving organizations without full control over every tool interacting with their infrastructure. In other words, organizations must build a world-class observability framework to ensure that no part of their infrastructure operates without the necessary oversight. After all, every security professional knows … what you can't see can haunt you.

Observability practices don't just happen overnight. They require thoughtful planning, the right tools, and a continuous commitment to refining processes and integrating insights across teams. By focusing on enhancing the way people work, businesses can turn complexity into opportunity and maintain control in an ever-evolving digital landscape. Staying ahead of chaos is just good business sense — and the best way to firewall your future.

Mimi Shalash is Observability Advisor at Splunk, a Cisco company

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Four Key Pillars to a Leading Observability Practice

Mimi Shalash
Splunk

Splunk's latest research reveals that companies embracing observability aren't just keeping up, they're pulling ahead. Whether it's unlocking advantages across their digital infrastructure, achieving deeper understanding of their IT environments or uncovering faster insights, organizations are slashing through resolution times like never before.

The companies achieving the most powerful business outcomes are recognized as "observability leaders," excelling across the four critical stages of observability: foundational visibility, guided insights, proactive response, and unified workflows. Achieving "leader" status in observability unlocks more than just visibility because it empowers organizations with a deep, actionable understanding of their digital ecosystems. This translates directly into business returns, delivering a 2.6x annual ROI. In practical terms, that means smoother operations, fewer disruptions, and more time spent innovating rather than firefighting.

Let's explore the four key pillars that form the foundation of a world-class observability practice:

1. AI: A critical observability tool to help remediate issues

Automation is a cornerstone of an effective observability practice and elevates operational efficiency. As IT environments expand and grow increasingly complex, maintaining visibility over the full ecosystem of tools and technologies presents a growing challenge. Combine this with the volume of alerts that ITOps and engineering teams manage daily and the manual intervention becomes unsustainable. AI and machine learning (ML) solutions reduce the cognitive load off teams by dynamically recalibrating baseline metrics and detecting anomalies that static thresholds might overlook.

The latest data from Splunk highlights just how impactful AI and ML solutions can be with 85% of respondents reporting resolving at least half of their alerts. Additionally, 65% rely on AIOps for automated root cause analysis, giving teams the intelligence they need to stay ahead of issues.

Becoming an observability leader requires more than just basic monitoring. It requires advanced solutions that understand the normal patterns of your IT environment to then automatically detect anomalies. Consider a straightforward scenario: an unexpected CPU spike occurs on a cluster node, triggering an alert that requires quick, actionable insights. With an advanced observability tool, the system might recommend redistributing workloads across nodes to prevent service disruptions. Or it could initiate an automated remediation workflow via integrations with orchestration tools, ensuring rapid resolution without manual intervention.

2. Build a dedicated platform engineering team

Integrating AI and ML solutions is just one piece of what sets observability leaders apart. The competitive edge lies in how these technologies are embedded into platform engineering. Platform engineering isn't just about tools and processes, it's about empowering software engineers to do what they do best: create. By adopting standardized toolchains, workflows, and self-service platforms, teams minimize time spent managing tools because they've codified making the right thing to do, the easy thing to do.

The research shows that 73% of organizations are already embracing these practices and 58% of observability leaders recognize that this isn't just a trend. Rather, it's becoming the hallmark for continuous innovation, enabling seamless automation, scalability, and enhanced developer experience.

3. Harness control of the telemetry pipeline

At the heart of platform engineering, beyond automation and scalability, it's about maintaining control. Knowing exactly where your data flows, where it originates, and staying firmly in control of it isn't just important, it's essential. Ownership over data is the lifeline of every organization. Today, over 75% of observability leaders have adopted OpenTelemetry, solidifying it as the industry standard for collecting and controlling critical data. This open framework seamlessly integrates with other tools, bringing together data from multiple sources for complete visibility.

The result? A flexible observability practice that fosters innovation, reduces dependencies, and empowers businesses to grow on their own terms. With OpenTelemetry, you're not just building observability, you're building the freedom to evolve and innovate without limits.

Tapping into OpenTelemetry's full potential isn't just about adopting the technology. It also requires having the right talent in place and that comes with its own set of challenges. The report indicates that many companies are struggling with a shortage of people possessing the expertise on the open source project. To bridge the gap, organizations should prioritize training existing team members on how to configure, manage, and optimize OpenTelemetry pipelines and build this strategy into bullet #2.

4. Remember that observability is a team sport

Lastly, organizations must learn that true observability isn't achieved in isolation. Nearly three-quarters (73%) of observability leaders said they saw an improvement in their mean time to resolve (MTTR) after combining their observability and security workflows and tools. This highlights the importance of keeping observability tools connected across teams. When teams have cross-functional visibility into each other's workflows, they're better equipped to solve issues faster and prevent them from happening again.

While every team — observability and security — share the ultimate goal of business success, they may not share the same immediate objectives. Finding common tools and testing shared data sources can help iron out workflow differences, paving the way for future convergence.

Scaling with observability

As cloud reliance grows and AI tools multiply, IT environments will become even more complex, leaving organizations without full control over every tool interacting with their infrastructure. In other words, organizations must build a world-class observability framework to ensure that no part of their infrastructure operates without the necessary oversight. After all, every security professional knows … what you can't see can haunt you.

Observability practices don't just happen overnight. They require thoughtful planning, the right tools, and a continuous commitment to refining processes and integrating insights across teams. By focusing on enhancing the way people work, businesses can turn complexity into opportunity and maintain control in an ever-evolving digital landscape. Staying ahead of chaos is just good business sense — and the best way to firewall your future.

Mimi Shalash is Observability Advisor at Splunk, a Cisco company

The Latest

In MEAN TIME TO INSIGHT Episode 12, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses purchasing new network observability solutions.... 

There's an image problem with mobile app security. While it's critical for highly regulated industries like financial services, it is often overlooked in others. This usually comes down to development priorities, which typically fall into three categories: user experience, app performance, and app security. When dealing with finite resources such as time, shifting priorities, and team skill sets, engineering teams often have to prioritize one over the others. Usually, security is the odd man out ...

Image
Guardsquare

IT outages, caused by poor-quality software updates, are no longer rare incidents but rather frequent occurrences, directly impacting over half of US consumers. According to the 2024 Software Failure Sentiment Report from Harness, many now equate these failures to critical public health crises ...

In just a few months, Google will again head to Washington DC and meet with the government for a two-week remedy trial to cement the fate of what happens to Chrome and its search business in the face of ongoing antitrust court case(s). Or, Google may proactively decide to make changes, putting the power in its hands to outline a suitable remedy. Regardless of the outcome, one thing is sure: there will be far more implications for AI than just a shift in Google's Search business ... 

Image
Chrome

In today's fast-paced digital world, Application Performance Monitoring (APM) is crucial for maintaining the health of an organization's digital ecosystem. However, the complexities of modern IT environments, including distributed architectures, hybrid clouds, and dynamic workloads, present significant challenges ... This blog explores the challenges of implementing application performance monitoring (APM) and offers strategies for overcoming them ...

Service disruptions remain a critical concern for IT and business executives, with 88% of respondents saying they believe another major incident will occur in the next 12 months, according to a study from PagerDuty ...

IT infrastructure (on-premises, cloud, or hybrid) is becoming larger and more complex. IT management tools need data to drive better decision making and more process automation to complement manual intervention by IT staff. That is why smart organizations invest in the systems and strategies needed to make their IT infrastructure more resilient in the event of disruption, and why many are turning to application performance monitoring (APM) in conjunction with high availability (HA) clusters ...

In today's data-driven world, the management of databases has become increasingly complex and critical. The following are findings from Redgate's 2025 The State of the Database Landscape report ...

With the 2027 deadline for SAP S/4HANA migrations fast approaching, organizations are accelerating their transition plans ... For organizations that intend to remain on SAP ECC in the near-term, the focus has shifted to improving operational efficiencies and meeting demands for faster cycle times ...

As applications expand and systems intertwine, performance bottlenecks, quality lapses, and disjointed pipelines threaten progress. To stay ahead, leading organizations are turning to three foundational strategies: developer-first observability, API platform adoption, and sustainable test growth ...