Date: 14th Nov, 2022
No one will disagree that customer experience is now of paramount importance in an enterprise. All business processes, workflows, and technologies in modern businesses are adopted and implemented, keeping the customer in mind. The tacit belief is that the better the customer experience, the better the business.
And this level of customer experience is directly related to the ease of user interaction experienced by the customer while interacting with enterprise applications, software, and IT infrastructure that is interfacing with the customer. The speed, responsiveness, uninterrupted service availability, and the ease of use are critical factors that customer experiences are rated on.
To that end, digital transformation is being adopted across the entire enterprise to enhance the customer experience being facilitated. This means that all applications being used across the enterprise must maintain a level of performance and service prescribed as a “minimum” by the enterprise.
It’s here that Observability and Application Performance Monitoring are introduced into the enterprise to ensure that all digital infrastructure is tuned towards the best performance at all times, thus ensuring the required customer experience.
Application Performance Monitoring (APM) – A Primer
Application performance monitoring or APM is the process where tools and technologies are used to ensure that the applications deployed in an enterprise function as per expectations in terms of performance,reliability, and user experience.
These expectations are predefined norms laid down by the enterprise and applied to employees, vendors, and customers that are considered users of the software in the enterprise.
This monitoring focuses on application performance, unit and component performance, user interactivity, transaction speeds, etc. APM can be done in-house or by forming strategic partnerships with to implement the same.
Observability – A Primer
Observability is the process of collecting data, including that from APM systems, endpoint performance tracking tools, and other such digital assets in an enterprise, to deduce the state of an entire system. Observability aims to get a holistic view of the whole system and its functioning across the entire enterprise. This view enables teams to monitor solutions in their entirety and make decisions that will enhance customer experience.
Observability also includes tracking system event management, cybersecurity, and the software development and deployment cycles within the enterprise. Log files, metrics, and trace data are collected for this process of observability from every possible IT asset across the enterprise.
With the process of observability, data on performance, management, fault detection, and mitigation is collected across distributed computing environments regardless of complexity. This overall observability process enhances root cause analysis undertaken in an organization, thus, making failures and anomalies easier to detect and mitigate.
The importance of observability is not restricted to IT asset management and performance. The process of observability has a direct impact on business decisions and workflow management as well.
Connecting Monitoring and Observability
APM and Observability are similar in the sense that for both, data is collected to get a better picture of the performance of applications and a system. Data collected for both processes can be similar and be used for either process. The difference, however, lies in the scope of the process of observability and application performance management.
Monitoring Is a Process Within Observability
Different data is monitored and collected by monitoring systems across the enterprise. This data can then be analyzed for performance and error detection at the individual entity or component level or used for observability of the bigger picture at the enterprise level. Metrics, logs, and trace data collected by APMs are one of the sources of analysis of observability.
Monitoring vs. Observability
Monitoring is a process within the framework of observability. Though both processes look at how to increase performance and productivity, the scope of each is different.
Observability looks at a system in its entirety in a top-down approach, implementing root cause analysis for detection, mitigation, and enhancement of an enterprise as a whole. Observability is about the state and health of a system as a whole.
Monitoring is more at the entity level and individual components within the IT enterprise. Monitoring will give performance data of an application, and performance management will ensure its enhancement, but it will take observability to view the state of the entire complex system where the application is just one of the entities.
Why is Observability needed even when Monitoring is enabled
1:- Complexity overrides monitoring
Adopting digital transformation usually entails the increasing complexity of the IT infrastructure. The fusion of legacy and newly developed software, adoption of a multi-cloud environment along with the native cloud and edge computing, and their interdependencies are just some of the complexities of an ever-growing digital transformation process. Organizations tend to use a variety of monitoring tools to attempt to gain a better view of the enterprise as a whole. This strategy however often ends up in failure as the monitoring systems are of individual entities of the system and do not observe the interdependencies within the entities nor treat the system as a whole. This is where observability tools, techniques, and strategies are adopted to look at components, services, applications, and systems at an enterprise level.
2:- Plugging into AI and ML
While monitoring and its dashboard are at the individual IT entity level, Observability with its top-down approach has a better view of the enterprise systems in its entirety. Since observability adopts a holistic view of a system rather than a collection of individual entities or microservices, the models that are built of performance data across a life cycle are tuned in for more accurate insights and analytics, root cause analysis, event monitoring, and pattern recognition with AI adoption with ML. This allows for early detection of unpredictable losses and failures with proactive AI-based Observability strategies and processes.
3:- Root Cause Analysis and Context Propagation
It is not possible to work out a real and actual root analysis based only on monitoring data. Monitoring will give data on individual entities or microservices. Observability is required for accurate Root Cause Analysis as it has models based on data across all the microservices or systems in a distributed system. Since in Observability data assimilated is across the microservice, context propagation is possible along a path that might traverse across multiple microservices in the form of a transaction or a user request. This context propagation can be correlated to the transaction behavior and can help in further root causes analysis of application performance.
4:- The Bottom Line
Observability looks at a system in its entirety in a top-down approach, whereas Monitoring is a process within the framework of observability. Furter, observability brings-in higher capability to the enterprise to deliver better performance and reliability.