There’s a particular kind of organisational delusion that’s become standard practice in enterprise IT. It goes something like this: we have observability tools. We have dashboards. We have alerts. Therefore, we have observability. It feels true. It looks true. Until something breaks and everyone is staring at the same screens, drowning, wondering why nobody saw it coming. What’s meant to be a proactive discipline is still, in most organisations, treated as a reactive tool.
Most organisations don’t actually have an observability problem. They have a confidence problem. Data and trusted data are not the same thing. If your GPS says you’re on the right route but you keep ending up in the wrong place, you would eventually stop trusting the signal entirely. That’s exactly where most enterprise IT teams are right now, technically instrumented, practically uncertain.
The dashboard illusion
Ask most IT teams whether they have visibility into their systems and the answer is yes. They’ll point to dashboards, alert pipelines, and sprawling telemetry stacks as evidence. Technically, they’re not wrong. The data is there. But in many cases, what they are looking at are out of the box views designed for a generic environment and not dashboards built for their specific organisation, architecture, and business model.
When your monitoring environment generates thousands of alerts per day, when your dashboards have seventeen panels that nobody checks unless something is already on fire, when engineers spend more time correlating noise than investigating actual problems, you don’t have observability.
You have instrumented chaos.
Observability is what allows you to move beyond reaction and actually understand what is happening inside your systems. At amasol, we believe observability is having the right tools with a proactive mindset. Because when it is properly embedded across an organisation, it enables teams to solve problems before they fully emerge and cause cascading problems. A great benefit we see from organisations after adopting observability as a mindset is reducing the amount of reactive work required over time as your observability adoption matures.
Alert fatigue is a symptom, not the disease
The industry has talked about alert fatigue for years, as if it’s a settings problem. Tune your thresholds. Add suppression rules. Clean up your runbooks. Those things help at the margins. But they treat the symptom. The actual disease is a lack of shared definition about what good looks like, and no agreed standard for when to act.
When that standard is missing, every team draws its own lines. Infrastructure monitors for availability. Application teams monitor for latency. Business units track conversions. None of those layers speak to each other in a language everyone trusts. So when an API starts degrading, infrastructure sees nothing critical, application teams see elevated response times but within threshold, and business teams notice a dip in checkout completions but assume it’s a Tuesday. By the time everyone compares notes, the window to act has closed.
This is reactive IT at its most costly: not the dramatic outage, but the slow accumulation of missed windows, delayed decisions, and incidents that could have been caught earlier.
More telemetry, more uncertainty
Here’s the counterintuitive truth most vendors won’t put in their sales material: adding more data sources to a poorly governed observability environment doesn’t increase clarity. It increases uncertainty.
When engineers don’t trust their alerts, they sanity-check everything manually. Every incident becomes archaeology. Every on-call shift involves establishing baselines that should already be documented. Every deployment comes with collective breath-holding because nobody is confident they’ll see a problem early enough to act on it. That’s an organisation stuck in permanent reactive mode, unable to shift because the signals it depends on aren’t trusted enough to drive decisions.
The organisations that have broken out of this aren’t necessarily the ones with the most tools or the most data. They’re the ones who have done the harder work: deciding what they actually need to see, building genuine trust in those signals, and connecting them to outcomes that matter beyond the engineering team.
What “good” actually looks like
Good observability is quiet. Not because nothing is happening, but because what does surface is meaningful, contextualised, and actionable. Engineers aren’t wading through noise. Leaders aren’t asking for a translation every time something requires a decision. More importantly, the absence of alerts is itself trustworthy. When nothing is firing, nobody is quietly wondering whether the monitoring is actually working.
Getting from reactive to proactive isn’t a tooling problem. It’s a discipline problem. It requires deciding what you’re measuring and why, aligning telemetry to business outcomes, establishing governance around signal quality, and building the organisational muscle to act on what you see before it becomes an incident. That’s the work most organisations skip because the tools are already running and it feels like enough.
It isn’t.
That’s the conversation we’re hosting in June
At the Business Observability Forum in Munich on June 11th, we’re bringing together IT practitioners and business leaders to work through exactly this. Not to introduce another platform. To help you define what good observability actually means for your organisation, close the gap between the data you’re collecting and the confidence you need to act on it, and start building the operating model that makes proactive control possible.
The motto for this year’s forum is Trust Your Signals. Because until you do, you’re not running on observability. You’re running on hope.
Click on the link to learn more and register if you are able to attend.
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