Five common misconceptions about observability

The rise of cloud-native environments has meant that traditional approaches to monitoring and managing application performance are becoming less and less effective. As a result, digital teams have begun leaning towards new approaches, built on the three pillars of observability – metrics, logs, and traces. Rather than trying to ‘monitor’ their environments, the idea is to design them to be inherently more ‘observable’, so they produce data organizations can gather and analyze to understand application performance and manage user experience effectively.

There has been growing interest in the topic of observability in the last 12 months, but as with any development in IT, the truth can often be shrouded by myths and misconceptions. As a result, there’s a lot of confusion about what observability is and what it isn’t. Here are the five most common misconceptions.

“Monitoring and observability are one and the same”

Monitoring and observability work hand in hand, but they are two very different things. Observability refers to the collection of measurements, better known as telemetry, produced by applications and digital services. These have historically been defined by three key pillars of observability – metrics, logs, and traces. Monitoring is the use of these metrics to understand and manage digital performance and user experience.

As organizations work to build more observability into their applications and services, the telemetry produces more data that can be picked up by monitoring tools. This data makes it possible to measure the performance and health of an application or digital service. If observability is built into everything, operations teams can see everything happening within their IT environment. The more observability they have into their environment, the better and more effective monitoring can be.

Unfortunately, the number of monitoring tools has no bearing on how much observability teams have into an IT environment. Conversely, adding more tools is usually counterproductive because it only results in more alerts, creating noise and making it harder to understand, prioritize and manage what is happening in an IT environment.

Research suggests the average IT and cloud operations team gets nearly 3,000 alerts from monitoring and management tools every day. As a result, they spend approximately 15 percent of their time identifying which alerts should be focused on and which are irrelevant, costing organizations an average of $1.5m a year.

Throwing more monitoring tools at this problem is asking for trouble, which is why almost three-quarters (72 percent) of CIOs say they can’t keep plugging more monitoring tools together in an attempt to maintain observability. Instead, they want a single platform that covers all use cases and offers a consistent source of ‘the truth’.

“Having more observability will automatically solve all my problems”

Unfortunately, achieving observability into environments is just the beginning. Think about it this way – just observing a crime in progress doesn’t mean the police will come and prevent it. Someone must take action to call the police to either stop the crime from happening or investigate the cause. The same is true for observability in IT environments. Just because digital teams can see what is happening in their environments including any problems doesn’t mean they understand why it is happening and that those problems will fix themselves.

Having better observability provides crucial context to the data, which allows IT teams to identify and understand the problems that need to be addressed in their environment. However, the complex nature of today’s multicloud ecosystems means that teams usually have more data than they can deal with. That’s why the use of AI is becoming increasingly critical, providing context that helps teams to understand why performance problems matter, which in turn helps them to prioritize what issues they need to focus on and how to fix them.

“We already have full observability into our environments”

Most organizations lack adequate observability. As they accelerate their digital transformation and embrace cloud-native environments, true observability will become harder for them to achieve. This is largely due to the scale and dynamic nature of the architectures that these environments are built on, including microservices and containers, Kubernetes, serverless, and service mesh infrastructure.

These environments are constantly changing, generating huge volumes of data, which can be used to understand IT performance, optimize services, and create better experiences for customers and users. However, the manual instrumentation required by conventional monitoring approaches means the average organization has full observability into just 11 percent of its applications and infrastructure. This is leaving organizations with huge blind spots that makes managing these environments and the user experience extremely difficult. However, AI can eliminate these blind spots by auto-discovering applications, infrastructure and dependencies within complex environments.

“Observability is too hard to automate”

Common wisdom for IT efficiency says any manual task that can be automated, should be. Typically, observability into dynamic multicloud environments is a task that requires developers to manually instrument cloud infrastructure and application code to supply metrics, logs, and traces for monitoring tools to collect. This is a time-consuming and inefficient process that considers only a handful of observability data sources and distracts developers from the work that matters.

More than two-thirds of CIOs recognize the need for a radically different approach to observability to unlock the potential of their cloud environments. Automating the process reduces the need to manually instrument observability into applications, services, and code. This gives developers time back to focus on the tasks that matter and enables organizations to continuously discover and instrument their environments and understand the dependencies within them. This means observability is ‘always-on’ and can scale with today’s dynamic cloud-native ecosystems.

Observability goes beyond IT

A final point about observability is the impact it can have on the wider organization. At a basic level, having observability means teams can see how applications and services are performing. By leveraging this technical data alongside business metrics, IT teams can go much further to understand the wider impact that digital performance can have, including revenue, customer conversions, and churn. As a result, organizations can stop worrying about what they can’t see, and can instead use AI-powered insights to improve the customer experience and drive greater value.

Abdi Essa, Regional Vice President, UK&I, Dynatrace

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