Corporate entities have large-scale infrastructures to host business applications, software systems, and microservices. Software systems, business apps, and other elements of IT infrastructure are crucial for organizations to complete their operations timely. Organisations must manage the performance of these software systems to maintain continuity. The performance of software applications degrades over time, and it is important to track them. Without applied observability, gaining insights into system performance is not possible. It is the reason why enterprises use new-age solutions like AIOps platforms for performance monitoring. These platforms offer enhanced observability of the internal states of software applications. Read on to understand applied observability and its impact on system reliability, resilience, and performance.

Are you familiar with the concept of applied observability?

Applied observability is defined as tracking the internal states of software applications over time. It helps enterprises understand the performance of software applications over time within the IT infrastructure. It includes collecting system data regularly and analyzing it to gain insights into system health and performance status.

The data relating to software applications can be logs, incidents, events, KPIs (Key Performance Indicators), etc. Besides data related to software applications, details of the underlying infrastructure are also used. The collected data is analyzed to generate observability charts, reports, and metrics.

Applied observability is a holistic approach to using data across different business areas. It is crucial to note that software systems aren’t observable on their own. Software applications are made observable with high-end analytics solutions like AIOps platforms. By combining AI-led analytics practices, applied observability leads to faster and more informed decisions across different business areas.

Three foundations of applied observability

It is essential to understand the three pillars that lead to applied observability. The entire concept of applied observability is based on collecting and analyzing system data. The three pillars of applied observability are also based on different data types collected from systems and the underlying infrastructure. Here are the foundations of applied observability:

Logs are the first pillar of applied observability and help us determine system performance. They are a record of events that occur within systems within the infrastructure. Applied observability solutions can collect log data from different software applications. They can analyze logs to understand system performance. They can chart patterns and relationships to determine abnormalities.

Metrics are the second pillar of applied observability. These are numerical elements that help us understand a system’s health and performance status. For example, start-up time can be a metric that can be used to determine the time taken to start a software application. Downtime can be another metric that determines the time a system cannot perform any activity due to an error. Metrics can be used to correlate events and identify abnormalities. They can help system administrators identify an error before it becomes too serious.

Traces are the third and final pillar of applied observability. Traces are records of user requests within different systems from start to finish. For example, traces can be a customer’s journey on an e-commerce platform from product visit to checkout. It helps IT teams to gain insights into digital customer journeys and identify common errors. Enterprises can improve user experiences and identify bottlenecks faster.

Now that you understand the three pillars of applied observability, let us understand its impact on service reliability and performance.

How applied observability improves system reliability and performance?

Applied observability has a direct relationship with system reliability. System reliability is the time of non-failure within a software application. MTTF (Mean Time Between Failures) is another metric used to determine system reliability. It denotes the average time between two failures within a crucial software system.

With applied observability, enterprises will be aware of the internal states of software systems. If the performance of a system deviates a little, IT teams can start their investigation. Why wait for a shutdown to occur when you can act early? Since IT teams will begin investigation even on a little hint of performance degradation, complete failure will not occur. It will result in increased system reliability.

Applied observability helps enterprises in the journey from reactive to proactive performance management. Consider an organisation that takes action after a performance issue has degraded service availability. What’s the point of making amends when system failure has already occurred? It is a reactive approach that will take you nowhere. An applied observability approach can generate insights to know about degradation beforehand. Concerned IT teams will take action before an incident occurs and disrupts the system’s reliability. A proactive approach will save funds for an enterprise that would have been used for fixing system failures.

Another benefit for enterprises is improvement in system resilience. No matter how well a software application is designed, sooner or later, its performance will degrade. The system might face adverse scenarios and even cyberattacks. Can the system offer critical services even when the conditions are adverse? Can the system retain a part of its capabilities even under a cyberattack? With the help of observable data, systems can be made more resilient. A software system must resist failure or harm in critical situations. However, it can only happen when IT experts know the internal states of the system.

When performance issues are fixed in real-time, the system maintains its longevity. A system finds it hard to maintain longevity after multiple complete failures. With the help of logs, traces, and metrics, IT experts can fix performance issues faster. Also, the MTTF for systems will increase, and downtime can be avoided. Systems performance can be enhanced further by using AIOps-based analytics platforms. They can provide you with observable data in real-time to respond to incidents faster.

In a nutshell

Applied observability is an essential approach for organisations looking to enhance system performance and resilience. Enterprises often lose revenue when critical software systems face downtime. With observable data, system performance can be always known. IT teams can identify the causes of incidents and failures faster. Enterprises will become more proactive in detecting and resolving bottlenecks and threats. Use observable data for system performance monitoring now!