The Performance Playbook, Part Two: Why Speed and Focus Define Modern Observability

In the first chapter of this playbook (if you missed it, go here), I shared why business context is the foundation of customer-centric visibility. Knowing what is broken isn’t enough. Teams need to understand who is impacted, which workflows are at risk, and what it means for the business.

But context alone doesn’t win the game.

When customer-facing systems are under pressure, the real differentiator is how fast teams can identify which problems deserve attention first. Because there will always be something broken. What separates high-performing organizations is their ability to direct effort to the issues that matter most—before customers feel the impact.

In modern digital environments, minutes matter. And attention is reserved for the business critical.

When Everything Is Urgent, Nothing Is Clear

Today’s businesses operate at massive scale.

That volume should be a competitive advantage. Too often, it becomes the opposite.

Teams face alert storms where every anomaly looks critical.

Engineers jump into war rooms without a shared understanding of impact.

Signals get buried under noise while customer-facing issues escalate.

Executives hear about incidents after customers already feel the pain. Millions of users. Millions of transactions. Millions—sometimes billions—of telemetry signals flowing every day across applications, infrastructure, networks, and increasingly, AI systems.

Some observability tools surface a lot of data, but not better answers. They detect symptoms, not significance. And when everything demands attention, teams end up reacting instead of anticipating, and very tired.

The cost of continuing to operate like this leads to staff burnout—which leads to missed signals, slower detection and investigation of business impacting issues and ultimately degraded customer trust, revenue loss and brand damage

The New Play: Detect Earlier. Investigate Smarter.

Once you have business context, the next evolution of your observability practice is speed with laser focus on accuracy.

At Splunk, we’ve focused on a simple question: How do we help teams spot business-impacting issues earlier—and get to accurate root cause faster—without burning cycles in endless war rooms?

The answer lies in combining scale, real-time intelligence, and AI that’s designed to compliment how your teams actually work.

Because Splunk’s streaming architecture collects, processes and refreshes telemetry data in real time, teams can spot anomalies as they emerge—not after refresh cycles and rollups have diluted the signal. In platforms that depend on delayed detection or heavy aggregation, anomalies aren’t just detected later; they’re often obscured altogether as telemetry is averaged over time. That loss of fidelity can turn an early warning into a missed opportunity, escalating a minor issue into a major outage.

At the same time, Splunk processes and analyzes massive volumes of telemetry—logs, metrics, traces, user experience data, and security signals—without forcing teams to predefine rigid schemas or sacrifice fidelity. Teams can query and explore data as it arrives, and automatic log parsing surfaces relevant fields quickly, so engineers spend less time preparing data and more time moving toward root cause.

But detection is only the start of the battle.

From Signal to Root Cause—Without the Noise

When issues arise at enterprise scale, speed relies on honing in on what's most critical to the business at the time.

Splunk uses AI to correlate related signals across the stack and collapse alert noise into a clear, prioritized view. Instead of hundreds of disconnected alerts across tools, teams see a single incident with context: which services are involved, how dependencies are behaving, and which business workflows and customer groups are at risk.

From there, AI-guided troubleshooting workflows help teams move quickly from impact to root cause. Engineers can easily see how network, infrastructure and application components form business processes, and then isolate which service, infrastructure, or business logic is causing the problem.

Network blind spots disappear through deep visibility into both owned and unowned network paths. Security signals surface whether vulnerabilities are theoretical or actively exploited—and what the business risk actually is. And as AI-driven applications move into production, teams gain visibility into the performance, cost, quality and behavior of the entire AI stack, from infrastructure to model interactions, to ensure AI models are doing what they’re supposed to given their unpredictable nature.

And vnow, AI agents will make this even simpler—helping teams detect, diagnose, and respond with less manual effort.

The result is fewer big war rooms, less guesswork, and faster, more confident decisions.

What This Looks Like in the Real World

We see the impact of this approach across industries.

• REPAY uses Splunk Observability Cloud and the product’s AI Assistant to gain complete visibility into its system health, cut incident triage time by 50%, and reduce transaction latency by 30%.
• Rappi uses Splunk Observability Cloud to gain end-to-end visibility into its distributed microservices-based architecture, managing 1,000+ microservices, 6,000 hosts, and 15,000 containers while slashing MTTR by over 90% and processing more than 8.8 million orders per month.
• Travelport uses Splunk Observability Cloud and Splunk Assigned Expert Service to achieve full visibility across its entire environment, resulting in a 75% reduction in mean time to detect (MTTD), a 95% reduction in false positives, and the ability to exceed its uptime goal for its core customer-facing product.

Across these stories, the pattern is the same: earlier detection, faster investigation, and less time spent in reactive war rooms.

Observability Should be an Advantage and Not a Fire Drill

For leaders, this shift is strategic.

Modern observability isn’t about staring at dashboards longer. It’s about using intelligence—human and machine—to direct attention where it counts. It’s about turning telemetry into decisions, and decisions into outcomes.

When teams can detect issues early, cut through noise, and resolve problems with business impact in mind, observability becomes more than a safety net. It becomes a performance lever.