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News/

10 mistakes in IT environments that compromise operations

July 2nd, 2025
AI Operations Intelligent Monitoring and Observability
By Edge UOL
10 mistakes in IT environments that compromise operations

Edge UOL highlights the main failures that companies of different sizes and segments commit and that can be fatal for cybersecurity and reputation

Poorly executed or even non-existent monitoring can leave companies vulnerable to cyber-attacks, causing several losses. According to the Cost of a Data Breach report, released by IBM in 2024, a data leak in Brazil, usually caused by this gap, can have an average cost of R$6.5 million. The damages are not limited to the financial scope, putting the company’s reputation and efficiency at risk.

In a survey conducted by Edge UOL, a Brazilian company specializing in IT infrastructure services, 10 common errors in the monitoring of information technology environments were identified. The study was based on Edge UOL’s practical experience with companies of different sizes and segments and identified recurring failures ranging from the technical scope to the training of professionals.

Keeping the focus only on monitoring without adding observability is one of the main mistakes that were identified. This is because monitoring, which is more focused on the present, only answers what is happening, while observability brings, through a more complete view, the reasons for a certain event, with faster diagnoses that help to enable predictive actions. By combining these two fronts, operations become more resilient and centered, dealing not only with the problem, but also its causes.

“Today, it’s not enough to just monitor. You need to observe, correlate and act intelligently,” says Rodrigo Rangel Lobo, COO of Edge UOL. “ Observability is a key element in detecting anomalies, predicting failures and anticipating risks – all with a focus on the user’s journey and the impact on the business.”

Despite what Lobo suggests, many companies do not consider customer experience in IT operations, which, according to the executive, is a mistake, since the end user is the most realistic thermometer of whether something is working or not, just as their reactions to what is received constitute a guide for many technical decisions.

End user at the center of monitoring

“An environment can be ‘green’ in all technical metrics, but still generate frustration for the customer,” warns the specialist. Therefore, one of Edge UOL’s recommendations is to always place the end user at the center of monitoring through UX metrics, APM (Application Performance Monitoring), journey tracking and behavioral analysis.

Integrating tools is also essential to avoid gaps in the monitoring environment, breaking down silos and providing a unified view of environments. When brought together, these platforms allow for automatic event correlation, reducing response time (MTTR) and avoiding rework between teams.

At first, this integration can be complex in terms of configuration and governance, but the long-term results are very positive, such as increased productivity, better use of data, consistency in actions and ease of team training,” says Lobo.

Keeping the digital environment up to date helps ensure that the company’s digital area does not become vulnerable. At Edge UOL, for example, the priority is to review processes and technologies and look for ways to implement new features without impacting the operation. To do this, the company encourages other organizations to invest in the application of evolutionary observability practices with short review cycles, controlled tests and impact analysis.

By using agile methods, continuous integration and approval environments that simulate the real thing, Edge UOL ensures that each step in modernization is safe and sustainable, making it possible to validate new technologies or adjustments without compromising the current operation.

Check out the complete list of 10 errors in monitoring IT environments, according to Edge UOL:

  1. Excessive use of metrics without prioritization
  2. Lack of integration between tools
  3. Poorly configured alert
  4. Focus only on monitoring without the context of observability
  5. Ignoring proactive and predictive monitoring
  6. Disregarding the end user experience
  7. Low automation capacity
  8. Segmented and siloed monitoring
  9. Lack of continuous review of new environments, technologies and processes
  10. Complexity in training the team in tools and processes
Tags:
AI OperationsIntelligent Monitoring and Observability

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