By Gerardo Flores | Director of Operations, Honne
In many organizations, something curious happens: they have never had so much data about their technology… and yet, they have never been so confused when something goes wrong.
Dashboards full of charts, hundreds of metrics collected every second, alerts constantly going off. Everything suggests that the system is “monitored.” But when the user complains, when the application slows down, or when the business stops billing for a few minutes, a blunt question emerges:
what is really happening?
That is where an uncomfortable truth is revealed: having metrics is not the same as understanding the system.
The illusion of traditional monitoring
For years, monitoring was built around infrastructure: CPU, memory, disk, server availability. If those indicators were “green,” we assumed everything was fine.
That approach worked when systems were simple and monolithic. Today, in distributed architectures, microservices, and hybrid clouds, that model has become obsolete.
A server can be at 20% CPU and still deliver a slow user experience. A database can be “up” while an external dependency introduces enough latency to affect the entire experience.
Traditional monitoring answers a limited question:
is the component alive?
But the business needs to answer a very different one:
is the system working the way the user expects?
More data, less clarity
One of the most common mistakes I see in operations is confusing volume of information with understanding. When something fails, teams often have access to thousands of metrics, endless logs, and overlapping alerts… but no clear narrative.
The result is operational chaos:
- Dashboards are checked at random.
- Hypotheses are tested without order.
- Time is wasted correlating disconnected signals.
And meanwhile, the clock keeps ticking.
In these scenarios, the problem is not technical. It is cognitive. The system generates more information than the human team can process under pressure.
From seeing components to understanding systems
This is where the concept of observability comes in, which is not a tool or a pretty dashboard, but a different way of thinking about operations.
Observability starts with a fundamental question:
can I understand what is happening inside my system just by observing its outputs?
To achieve this, isolated metrics are not enough. Context is needed. And that context is built with three types of signals working together:
- Metrics, to know what is happening.
- Logs, to understand why it happened.
- Tracing, to follow a complete transaction across all the services involved.
When these signals are integrated, the team stops guessing and starts reasoning.
The key shift: from thresholds to experience
Another important conceptual leap is to move away from monitoring based solely on technical thresholds (“CPU > 80%”) and focus on indicators that reflect the real user experience.
This is where SLIs and SLOs come in:
- How long does a critical transaction take?
- What percentage of users experience errors?
- How many operations successfully complete their business flow?
When operations are measured with these indicators, something powerful happens:
the conversation stops being technical and becomes strategic.
The discussion is no longer about whether a server is “stressed,” but whether the system is delivering what the business promised the customer.
The invisible cost of noise
Without real observability, organizations fall into another silent problem: alert fatigue.
If everything generates alerts, nothing is urgent.
If the team receives hundreds of notifications a day, it learns to ignore them. And when a truly critical incident occurs, it can get lost in the noise.
This phenomenon not only increases operational risk; it also wears people down. Tired teams make worse decisions, avoid change, and become defensive.
Paradoxically, a “heavily monitored” system can be more dangerous than one with fewer, well-designed signals.
Understanding in order to operate
Observability does not aim to prevent all failures. It aims for something more realistic and more valuable: reducing the time it takes to understand what happened.
When the team understands quickly:
- Business impact is lower.
- Decisions are more accurate.
- Learning accumulates.
- Operations become predictable.
This is especially critical on what is known as “Day 2,” when the system is already live, changing, and aging. What cannot be clearly observed becomes fragile over time.
At Honne, we see this pattern over and over again: organizations do not fail because of a lack of technology, but because of a lack of understanding of the system they have already built.
Observability is not a luxury or a trend. It is the foundation for operating with confidence in a complex environment.
Because you cannot improve what you do not understand,
and you cannot understand a modern system by only checking whether its parts are turned on.
Having metrics is a good start.
Understanding the system is what truly makes the difference.
Gerardo Flores is Director of Operations at Honne, with more than 20 years of experience managing teams and complex environments. His approach combines strategic vision with practical execution to deliver sustainable results. He believes in leadership grounded in collaboration, open communication, and team empowerment, fostering cultures of trust that enable organizations to successfully navigate change.