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Every IT team will have many war stories about a 3 a.m. alert that turned out to be a false panic. For years, overloaded dashboards, redundant pings, and disconnected tools have turned engineers into firefighters instead of innovators.
Patrick Lin, SVP and GM of Splunk Observability, told Techopedia that it doesn’t have to be this way. The future may have fewer alerts and a clearer view across systems, allowing tech teams to focus on adding value. Let’s see how to get there.
Key Takeaways
- Shared data context promises to eliminate finger-pointing and accelerate the resolution of real problems.
- ThousandEyes tracing close blind spots across cloud, network, and app layers.
- AI links related alerts, halving troubleshooting time for stressed-out engineers.
- One clear source of truth replaces siloed tools and noisy false alarms.
- Observability, combined with assurance, builds resilience before users ever experience the pain.
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Will Unified Observability Finally Deliver What Monitoring Couldn’t?
Modern IT teams want clarity, yet most face a flood of disconnected signals and unclear warnings. However, before entering solution mode, we need to understand what observability truly means and why it extends far beyond basic monitoring.
Patrick Lin told Techopedia:
“Observability is an evolution of what we used to think of as monitoring. So, infrastructure monitoring, application performance monitoring, and so on.”
Lin believes this is precisely why unified observability has become so important. He said:
“People realized the value in the different signals you see at all these different layers of the technology stack. From how the application is performing to what the server underneath it is doing, the aggregation of various signals and the insights you can glean from them help teams resolve issues before users report them.”
While monitoring once meant a flood of raw data and endless blinking status lights, observability asks questions that monitoring alone cannot answer, such as:
- What caused the dip in speed?
- Where did the request hit a bottleneck?
- Why did one small glitch ripple across multiple services?
Last week’s LinkedIn poll results are in!
50% of you said false or vague alerts are your biggest threat detection challenge. You’re not alone — alert fatigue is serious issue.
Here’s how to fix it with high-fidelity threat signals: https://t.co/VZIRicLe22#CyberSecurity #NDR pic.twitter.com/QgqcQ6Uvp8
— Stamus Networks (@StamusN) May 28, 2025
The Real Culprit Behind Alert Fatigue
Plenty of tools promise to reduce alert storms. But Patrick made the real culprit clear:
“Alert fatigue comes from the fact that oftentimes the alerts are being generated by different siloed monitoring tools that have no idea what’s going on in other parts of the stack.”
Imagine a network glitch triggers five alarms. So does a slow database. If you have separate dashboards for each team, they chase multiple fires, often without realizing that they stem from a single broken link. This spreads teams thin and drains trust in the alerts themselves.
Splunk’s deeper integration with ThousandEyes attacks this mess directly. ThousandEyes has always been strong at watching network health, including bits you do not own, like your provider’s fiber or the public internet itself. Pair that with Splunk’s deep understanding of app performance and backend infrastructure, and suddenly, you see the whole chain.
This is where the promise goes beyond just new dashboards. Patrick Lin puts it plainly:
“The bidirectional integration is about enabling you to have sets of information, but using it in context. So if there is an application transaction that is happening and it traverses some networks, you can see the full path and dig into that from ThousandEyes or the Cisco Observability Platform.”
When things break, blame spreads faster than fixes can be made. This is the reality for most large tech teams. App engineers point at the database. Network teams shrug. Meanwhile, the user wants the service back online.
Lin has heard it all before:
“What you want to do is have a shared source of truth across those different environments. Because otherwise, it ends up being I look at my set of data, but I don’t know how it relates to your set of data.”
This constant game of guesswork slows down recovery and fuels more alerts. The Splunk and ThousandEyes combination aims to eliminate this by providing everyone in app teams, network operations, and infrastructure managers with the same view.
“Once you have that full picture, you eliminate the finger-pointing exercise because the data tells you where the issue is, and you go about fixing it.”
This subtle but powerful cultural shift means fewer turf wars, faster fixes, and more energy for actual innovation.
AI’s New Role in Observability
Beyond integration, Splunk is investing heavily in artificial intelligence (AI) to reduce the human workload further. For Patrick, this is more than marketing hype.
“This area of how do I understand whether I need to pay attention to something, if I do, what’s the problem? How do I fix it quickly? These are things that AI can help a lot with,” he explained.
In practice, this means AI watches for anomalies based on expected behavior. It clusters related warnings into a single incident report. It proposes likely root causes by connecting events that humans might not link on a busy day.
Here’s the catch. While AI helps teams identify problems, companies also need tools to maintain the health of their AI performance. Patrick called this “the flip side.”
As apps incorporate large language models (LLMs), vector databases, and GPUs, blind spots can emerge quickly. Splunk’s move is to treat these as first-class parts of the stack, no different than a traditional server or storage node.
Lin said:
“When AppDynamics first got into the business, it was mostly what we would think of as three-tier or end-tier application traditional architectures. When Splunk delved deeper into observability, a transition toward cloud-native microservice architectures occurred. Now, applications are gonna contain genAI as part of the overall stack.”
The theory is that IT teams who keep a close eye on these AI layers now will avoid tomorrow’s hidden meltdown.
ThousandEyes Distributed Tracing: A Unified Path
Elsewhere, ThousandEyes’ push into distributed tracing enables teams to track a transaction from the user click through backend microservices, across the network path, and up to the cloud provider’s internal infrastructure.
“ThousandEyes can look at not just what happens in the network path… but also inside of the public clouds themselves,” Lin explained.
For companies juggling multi-cloud and hybrid setups, this tracing closes blind spots that used to make root cause analysis a guessing game. It also means better baselines: teams can test typical paths, compare them to live traffic, and catch drift before customers do.
The most substantial practical benefit comes from connecting all this telemetry in one place. Splunk’s IT Service Intelligence ties signals from Meraki routers, Catalyst switches, ThousandEyes network checks, and app logs into a single data fabric.
“The work to bring those together into a unified experience so that people have the single source of truth to go to and a common way of looking at the data right now as part of Cisco,” Lin said.
Fewer Pings, More Action
As we move forward, it’s hoped that this new approach will eliminate the old headaches of switching between tools and manually pasting logs into spreadsheets. Now, engineers stay in flow, follow an issue from top to bottom, and act faster.
Many promises in IT come and go, but one line from Patrick shows the change is real:
“We’ve seen customers have the alerts that they have to respond to go down by orders of magnitude because they had so much noise before, and now they can cut through that and focus on what matters.”
This is the difference between a wall of alarms and a handful of real, actionable issues. For tired ops teams, this is the freedom to focus on prevention, not endless cleanup.
The Bottom Line
Alert fatigue is not going to vanish overnight, but the foundation for a quieter, more innovative system is finally in place. But Patrick left us with a clear sign of where Splunk and Cisco see the future:
“Our ability with AI to get to the root cause and help you fix it faster is what we’re working toward.”
Tech stacks are becoming increasingly layered and complex at a time when businesses are adding swarms of AI agents into the mix. Teams that adopt unified observability will have a better chance of maintaining control and freeing up engineers to focus on building rather than fighting fires. The good news is we are on the right path to silence alert noise for good.
