Observability is Transforming ITOM Landscape as Next Generation Monitoring
Tags: ai, aiops, mlops, observability
Published: 2021-04-15 05:45:20

![image.png](https://cdn.steemitimages.com/DQmTM185eMb8J8hLhGoBoqr5yxsMCcEgoqYgvSGNyjt2ywT/image.png)

First things first. Observability is inherent as a principle to a system and not something that is instilled. Here, we are addressing observability as an open source-based solution in the context of insightful monitoring within the ITOM landscape.

ITOM is now in the middle of addressing the needs of the expanding and dynamic nature of IT infrastructure as a function. It is no longer about being a monolithic computing stack. It is now beyond monitoring discrete infrastructure elements. 

Simple systems are smart enough to run a diagnosis and correct it on their own. But, native-cloud businesses with <a href="https://middleware.io/blog/microservices-architecture/?swcfpc=1">micro service architecture</a> and distributed systems are complex. 

System admins and ITops analysts were the eyes of systems, services and apps. It circled around ticketing, resolution, and people dependency. Complex systems depending on people increased the level of failure.

Now, ITOM is about observability that creates a proactive and transparent among complex and modern ecosystems.

<h1>Why Observability for your ITOM?</h1>
Observability provides a deeper operational visibility that brings an organization-wide sense of customer experience.  For digital transformation and cloud adoption, observability is one of the essential and critical pillars. The monitoring workload is also shifted to development. Apart from these, a few trends make observability a necessity for complex structures.
<ul>
<li>Businesses are living with the pressure to innovate fast. The development teams launch something every other hour, and they need to talk to internal systems for insights. </li>
<li>Apps and software have to deliver a <a href="https://www.channels.app/blog/customer-service-oriented" target=”_blank”>high customer experience.</a> </li>
<li>To meet these business needs, technology stacks is adding more and more tools. It is getting bigger and complex.</li>
<li>To put all these in place, there is a lot of demand for devops and automation. Creating intelligent autonomous systems is the ITOM’s new skill to be added.</li>
</ul>
Observability has been able to respond to these trends. Teams love it: Observability is gaining attention in the software world. It is able to effectively enable engineers to <a href="https://www.liveagent.com/academy/customer-experience/">deliver excellent customer experiences</a> with software despite the complexity of the modern digital enterprise. It is helping <a href="https://crustlab.com/blog/before-you-start-what-to-discuss-with-your-software-development-agency/">modern software teams</a>. On the whole, it have proven to:
<ul>
<li>Deliver high-quality software at scale</li>
<li>Build a sustainable culture of innovation</li>
<li>Optimize investments in cloud and modern tools</li>
<li>See the real-time performance of their digital business</li>
</ul>
Observability, across different use cases is able to provide full visibility into rapidly scaling infrastructure and applications, anomaly detection and metrics. It is not yet another monitoring tool. 

<h1>Observability, the future of systems and app monitoring</h1>
Monitoring as a tool has detected problems and anomalies in applications. Troubleshooters used to also gain insights into capacity requirements and performance trends over time. But, monitoring would be useless if these systems are not externalized enough. 

Observability as a next generation monitoring mechanism measures how a system’s internal state can infer external outputs. It develops organizational capabilities of monitoring and analyzing events, along with KPIs and other data. Observability yields actionable insights.

CloudFabrix’s <a href="https://www.cloudfabrix.com/observability/?utm_source=dev.to&utm_medium=referral&utm_campaign=aiops">Observability</a> solution is built using open source and open telemetry components. It draws data from across different data streams coming from servers, databases, applications, tools and services through turn-key integrations across the full DevOps stack. It also includes a lightweight agent deployed that gets automatically provisioned on the host operating systems.


![image.png](https://cdn.steemitimages.com/DQmTkTNXicSEfMpubnVyCsHqJF55puW4m7Ciccjgonvj5a4/image.png)

<h2>Observability is applied in the following use cases: </h2>
<ul>
<li>To reduce visibility gaps for modern applications architecture and dynamic workloads since the existing monitoring tools are not designed to support.</li>
<li>Provide context to the monitoring data to explain why and where questions.</li>
</ul>
Get a <a href="https://www.youtube.com/watch?v=L6-wlH0N8dk">full tutorial of observability-in-a-box.</a>

<h1>Observability in a box in the AIOps context</h1>
With growing complexity, the above use cases hint the need for inclusion of observability with AIOps. 

Traditional AIOps could not be applied for all use cases that challenged ITOM landscape. It served the immediate purpose but with high investment of resources and longer time to value.

Observability in a box in the AIOps context
With growing complexity, the above use cases hint the need for inclusion of observability with AIOps. 

Traditional AIOps could not be applied for all use cases that challenged ITOM landscape. It served the immediate purpose but with high investment of resources and longer time to value.

AIOps 2.0 with observability in a box provides quick turnaround with easy implementation. It brings an outcome-driven approach for metrics, events, logs, traces, and alerts.

Traditional ITOM comes with data centers geographically distributed. This adds to the alert noise and ticket volume. The problem resolution time is extended with reactive operations and lack of ability to preempt outages. 

<h2>AIOps 2.0 by CloudFabrix with observability enables: </h2>
<ul>
<li>Close identification of specific problems in a switch.</li>
<li>Dependency recognition, where the problem has actually triggered from.</li>
<li>Identification of other observability gaps</li>
<li>Asset intelligence</li>
<li>Reduced MTTR (mean time to repair)</li>
</ul>
It just gets better with more trained data and resolution models. It uses asset and model intelligence to correlate alerts where connected data relies on feeding information. Recommendations about adding new servers reduces the workload while leveraging the capabilities of Edge AI.

<h1>Why Observability is key for AIOps journey? </h1>
Observability is about providing a unified view across IT operations and systems which is tied to your business KPIs. <a href="https://www.cloudfabrix.com/">AIOps</a> is a part of Observability. AIOps 2.0 is all about intelligence. This element of intelligence is impossible without the insights drawn by observability. 

<h2>We are referring to: </h2>
<ul>
<li>Cost reduction recommendations rather than matrices analysis</li>
<li>Delay avoidance rather than processing delay intimation</li>
<li>Real-time metric data analysis and trends with aligned actionable insights and anomaly detection.</li>
</ul>
So, rather than telling you where the problem is, <a href="https://www.cloudfabrix.com/aiops-solution/">AIOps solution</a> with observability will tell you where the problem is, how it is solved, and what should be done to avoid it later. This continuous feed of intelligence is only possible when AIOps come together with observability.
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