7 Observability Challenges Every IT Team Faces (And How to Fix Them)

One thing we’re always told in IT is that “visibility is everything.” You can’t fix what you don’t know, after all—and on the surface, that statement makes absolute sense.

But it doesn’t really explain the entire philosophy behind it. Visibility is everything, but only if you understand what it is you’re seeing. Think about this way: Imagine you’re in a war room watching dashboards light up like a Christmas tree. Sure, you know something is wrong—you can see it—but you and your team are suddenly scrambling to find out what broke and why. 

This is where you encounter observability challenges. And sadly, these have only grown more significant in the last few years. This may explain why a recent Gartner study found that in 2026, 50% of enterprises are attempting to adopt more robust data observability tools, up from less than 20% in 2024. And this trend is only expected to ramp up in the near future.

For this article, we break down the most common reasons observability fails, explain why they happen in plain language, and walk you through practical ways to turn things around.

What is observability, really?

We’ve explained observability in this guide, but stated simply: It tells you why something is wrong and helps you trace the path from symptom to root cause.

It often gets confused with “monitoring,” which tracks predefined metrics and alerts you and your team when something crosses a threshold. However, as you can see quite clearly, monitoring is inherently reactive, whereas observability takes it a step further.

There are three pillars of observability to take note of, as well:

• Logs: Think of them as a detailed diary of your IT environment, since they include timestamped records of events that happened in your system.
• Metrics: These are numerical measurements of system performance over time, like CPU usage or error rates.
• Traces: These follow a request as it travels through different services in a distributed system, showing you exactly where it slowed down and failed.
• User experience*: Some IT teams include this pillar to measure how real users interact with the system, but these are not considered standard. 

True observability occurs when all three (or four, depending on your enterprise) pillars work together seamlessly and are mapped to real business outcomes. When any one of those elements is missing or broken, the whole system suffers.

Having this basic understanding of what observability is makes it easier to see what challenges there could be and why things go wrong. We’ve listed 7 of the most common challenges in the sections below.

At a glance: 7 challenges of observability

7 Observability challenges

Challenge 1: Data overload

Let’s start with the most obvious one: There can be too much of a good thing. Observability tools are designed to give you clarity, but modern environments generate enormous volumes of telemetry data. Without a deliberate strategy, all of that data can just become noise and bury the signals you actually need.

The fix: The goal is not to collect less data, but to collect smarter. It’s a good idea to prioritize anomaly detection over static threshold-based alerting. Machine learning in RMM can help surface unusual patterns and relate those to real business outcomes. When your observability best practices include clear objectives, you know exactly what to look for, which leads to collecting more relevant data.

A practical starting point: Audit your current telemetry sources and ask, for each one, “If this data caught a problem, would we know what to do with it?” If the answer is no, it may be generating noise rather than insight.

Challenge 2: Tool fragmentation

This is a common challenge among scaling enterprises. Over the years, your team may have decided to adopt several observability tools as needed, which inevitably led to tool sprawl. Unfortunately, siloed data (and teams) hinder true observability because there is a lack of… well, visibility. No one really knows who is doing what, and this results in slow response times and unresolved performance issues.

The fix: Move towards a centralized IT management platform, such as NinjaOne. A robust solution can integrate with existing tools and ensure that you can share data across systems. This single pane of glass approach improves observability since data is shared across your entire IT architecture.

Challenge 3: Missing context

This is what we call “data without meaning.” Raw data, even a lot of it, does not automatically translate into understanding. Observability data needs to be linked: logs tied to the specific transactions that generated them, metrics correlated with the traces that explain them, events mapped to the service dependencies that connect them. Without those links, your team is interpreting isolated data points rather than reading a coherent story.

Missing content means your IT team cannot trace the sequence of events that led to an issue. Without a centralized system (with good reporting), it becomes nearly impossible to identify the blast radius of a failure, especially during a high-pressure incident.

The fix: Create service dependency maps so your team has visual representations of how components interact with each other and anticipate the effects of changes. This also helps focus any troubleshooting efforts.

Challenge 4: Distributed system complexity

For growing enterprises, observability challenges naturally increase. This is an expected outcome, but one that is also easily avoided. As you scale, it is important that you consider how your networks and applications evolve from static, on-premises environments to dynamic, distributed systems.

The fix: Heterogeneous IT environments require a holistic approach that combines cloud, on-premises, and edge infrastructures. This means acquiring and implementing tools that are capable of ingesting telemetry from different sources, such as NetFlow, syslog, or SNMP, into a unified correlated model. It is also a good idea to look for tools with auto-discovery capabilities so that they detect new services and infrastructures as they appear.

Challenge 5: Alert fatigue

Alert fatigue is what happens when your monitoring systems are configured too aggressively, and your team starts ignoring alerts because they’ve learned that most of them don’t require immediate action (Think: The girl who cried “wolf,” but the forest is your IT environment). It’s one of the most dangerous failure modes in observability, because it means that when a genuinely critical alert fires, it gets treated the same way as the dozens of noise alerts that came before it.

Alert fatigue also works hand in hand with challenge 3. Even well-intentioned alerts can fail if they don’t include enough context. For example, an alert that says “high CPU usage on server X” does not tell you which application was affected, what changed recently, or what the normal baseline looks like. This forces your IT admins to do a lot of investigative work, which increases the risk of human error.

The fix: First, your alerting should involve anomaly detection and dynamic baselines rather than static thresholds. This helps you catch subtle issues that static rules can miss—without flooding your team with false positives. Second, we recommend that each alert should carry enough context to enable immediate triage: what is affected, why it matters, what changed, and what the likely next steps are.

Challenge 6: Organization or skills gaps

The next two challenges are not as “critical” as the previous ones, but that doesn’t make them any less important. In fact, it could be argued that these challenges are the most insidious because they are the most underestimated.

Specifically, a skills gap offers an underappreciated hurdle. Observability engineers need expertise in data analysis, distributed systems architecture, and tool configuration. This means that current team members may be expected to perform certain tasks without the training to do it effectively, which leads to misconfigured systems, poorly defined metrics, and observability setups that don’t deliver the value they should.

The fix: Create a culture of IT learning in your enterprise, including setting up an internal program where certain members of your team (who understand both the technology and the organizational context well enough) help their teams adopt better practices. It’s also a great idea if you establish shared terminology, shared SLOs, and shared processes for incident response within your team so observability becomes a practice that the whole organization participates in.

Challenge 7: Cost and scalability

Observability can get expensive fast, and you need to set up a system that can match costs as you grow, or you risk finding yourself in a situation where your observability set up is either no longer sustainable or only affordable by cutting corners.

The fix: This is where knowing what data to present to your investors becomes crucial. The initial investment for observability tools can be significant, but it would be justifiable with a great ROI. Getting the IT budget from the C-suite requires treating observability as a business capability rather than just a simple technology expense. Ask yourself, “What visibility do we need to meet our service reliability goals?” and work backward to the infrastructure required. This framing tends to produce better outcomes because it ties observability investments to outcomes that matter to the business.

What good observability looks like

Now that we understand the common observability challenges, it’s worth sketching out what good observability looks like in practice.

1. It starts with purposeful data collection. Telemetry is gathered because it serves as a specific monitoring or diagnostic tool, not because it’s available.
2. It has intelligent alerting. Alerts are triggered when something genuinely requires attention, and contain enough context to enable rapid triage. These alerts use dynamic baselines and anomaly detection rather than relying solely on static thresholds.
3. It operates from a unified platform. Your enterprise may not necessarily need a single tool (especially if you’ve worked with several completely acceptable ones in the past). However, it’s good practice to have an integrated ecosystem where data flows across boundaries and teams can see the same picture.
4. It is aligned with business outcomes. The metrics that matter most are tied to service level objectives that reflect what users actually experience and what your business needs to thrive.
5. It is backed by organizational commitment. Observability is not a one-stop thing. Teams must be trained, processes must be documented, and there must be genuine cross-functional collaboration to ensure that things work as smoothly as possible.

A practical roadmap: Where to start

If your organization is struggling with observability challenges or doesn’t know how to implement observability best practices, the following sequence is a reasonable starting point. Take note, of course, that these are not fixed rules; adjust as needed to fit your own needs.

1. Start with an honest audit: Before adding new tools, understand what you currently have. Which systems are instrumented? What data are you actually using versus just collecting? Where are the gaps in visibility that have caused real problems? This audit will tell you where to focus first.
2. Consolidate before you expand: We want to avoid tool sprawl at all costs. It may sound tempting, but if you already have several tools doing various jobs, it may be more effective to integrate a few of them than to add a new tool to the list.
3. Define what “good” looks like: Work with application owners, business stakeholders, and operations teams to define SLOs that describe acceptable performance from a user perspective. Use those SLOs to guide what you observe and how you alert.
4. Fix your alerting before you add telemetry: You can easily avoid challenge 5 by reducing noise first.
5. Invest in skills alongside tools: Buying a world-class observability platform and handing it to a team that hasn’t been trained on it is a reliable way to waste money. Budget for training as part of every observability initiative, not as an afterthought.
6. Measure and iterate: Track your mean time to detection (MTTD) and mean time to resolution (MTTR) as baseline metrics of observability effectiveness, and revisit your setup regularly as your environment evolves.

Overcoming challenges in observability

Observability challenges can be widespread and costly, but thankfully, they don’t require a complete overhaul to fix. The seven common observability challenges can be improved simply by having clarity about what you’re trying to achieve, discipline about what you collect and how you alert, integration between the systems you already have, investment in the people who need to use them, and a commitment to treating observability as an ongoing practice rather than a one-time deployment.

Related topics:

• What Is Full Stack Observability & Why Is It Important?
• What Is Infrastructure Monitoring? How It Works and Best Practices
• 6 Network Monitoring Best Practices