When embarking on a digital transformation journey, organizations often end up with complex infrastructure—the opposite of the initial goals of these projects.
This is as teams update existing legacy applications and infrastructure while adding multi-cloud, virtual and cloud-native capabilities. Ultimately, IT professionals find themselves managing diverse, complex, and distributed networks across cloud, system, application, and database infrastructures.
According to a recent IDC report, a myriad of barriers hinder the ability of IT teams to successfully contribute to business goals, in large part because of the efficiency of the tools to manage IT infrastructure low.
To deal with the resulting complexity, organizations tend to accumulate monitoring and management tools with the goal of simplifying systems overnight, says Sascha Giese, lead geek at SolarWinds. “In contrast, using a variety of tools to manage a network or infrastructure can lead to the development of silos that only further hinder IT teams,” he explained.
These silos exacerbate operational blind spots, delay problem resolution, and increase security risks. “Ultimately, this can overwhelm IT professionals, unable to keep up with application modernization or infrastructure dynamics,” he added. “The long-term solution to the challenge facing IT pros is observability.”
Integrated observability solutions measure the internal state of a system by examining the output of various layers. These tools view applications and systems holistically—from end-user experience to server-side metrics and logs.
“Not only does it show what’s going on with the IT tool, it helps the team understand the ‘why’,” says Giese. “A well-built observability system uses AI/ML to quickly identify course corrections or provide fundamental insights that allow IT professionals to take immediate action.
Through observability, service is predictable, and downtime is significantly reduced. “Furthermore, teams can become more proactive in problem and anomaly detection—enabling them to achieve optimal IT performance, compliance, and resiliency. ”
IT complexity hinders infrastructure management
William Morgan, The CEO and founder of co-Buoyant agree that complexity is the biggest challenge for anyone trying to manage infrastructure.
“As our infrastructure becomes more capable, it also tends to specialize and become more complex,” Morgan said. “Unfortunately, the tools to manage it tend to be just as complex, especially when the tools are still very new.
He explained that this is most evident in the field of service meshes, notorious for its complexity.
“Everything in computing is very human It’s hard to see, simply because humans are so much slower than any computer,” Morgan said. “Almost anything we can do to provide visibility into what’s really going on inside an application can be a huge help in understanding. “
This means not only fixing what’s broken, but also improving what’s working, or explaining them to users and new developers.
He pointed out The oldest observability tool, ad hoc logging – still in use today – but the addition of tools like distributed tracing can provide a standard layer to see the entire application without changing the application.
which in turn reduces the burden on developers (less code to write) and support staff (different things to learn).
“As an industry, we have created many Observability tools, from printed reports to distributed tracing,” Morgan said. “Network analytics brings a welcome consistency to observability. “
To a certain extent, network traffic is the same no matter what the application is doing, so you can easily get equal transparency for each application,” he added. Services in a particular service.
At the same time, by observing the network from the outside (especially in an encrypted world), it is impossible to know the details of what is happening inside a particular service.
” Network analysis is a useful tool in your toolbox, but not a panacea,” he said.
Bringing IT observability to the entire team
The entire technical organization, from developers to platform engineers, to customer support and top management, requires application-wide observability.
Morgan pointed out that developers need detailed information about how each part of the application is performing, while platform engineers need to easily see areas where the infrastructure is limiting the overall performance of the application.
“Again, in a microservices architecture, it’s critical that these stakeholders have the visibility they need anywhere in the application, no matter which service goes down, how well or deeply hidden in the call graph it is from the end user From his perspective, being able to quickly see failures in front-end services is not enough, which is why observability tools are important Unified application across all services within the application.
Collaboration is essential for observability projects
Giese adds that when significantly updating the IT environment, collaboration between the IT team and the C-suite is critical, especially when implementing observability solutions within budget and time constraints Scenarios can be a challenge.
Therefore, there must be a strategic discussion between IT professionals and senior leadership—a discussion focused on priorities and the need for both parties to invest time and money.
He has often said that the lack of alignment between IT professionals and the wider business is rooted in a disconnect in goals.
“To successfully demonstrate that in order to improve observability, IT pros must be prepared with unassailable advice that speaks the language of the business and aligns IT goals with overall goals,” he said. “Only then will this critical solution become a key part of an IT pro’s digital transformation toolkit.
Giese adds that using AI to automate repetitive actions, observability tools can also improve IT capacity. “Without spending time responding to false alerts or easy fixes, IT professionals are free to resolve They are interested in problems and move the organization forward,” he said.
However, the more advanced capabilities that observability brings, such as automation and machine learning, require preparation of the environment .
In contrast, AI does not require much preparation, as the system will within a few days learn what it is looking at and provide so-called actionable intelligence – the system will independently observe the current state, create Baseline and spot anomalies.
“Others call it intelligent automation, but whatever the name, it’s a way for IT to outsource tasks to machines, and the engine makes decisions based on data,” Giese “We get this data using deeper analytics like from the network or infrastructure layers. “
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