Four Key Pillars to a Leading Observability Practice

Mimi Shalash
Splunk
Learn more about Cisco 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
Learn more about Cisco 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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AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

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Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

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Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...

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Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...

MEAN TIME TO INSIGHT Podcast - Episode 27: Observability Unification

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