DevOps and AIOps: Developing A New Culture

Will Cappelli
Moogsoft

In today's competitive landscape, businesses must have the ability and process in place to face new challenges and find ways to successfully tackle them in a proactive manner. For years, this has been placed on the shoulders of DevOps teams within IT departments. But, as automation takes over manual intervention to increase speed and efficiency, these teams are facing what we know as IT digitization. How has this changed the way companies function over the years, and what do we have to look forward to in the coming years?

While this all began with the introduction of the Internet in the late 90s, it took a turn after the economic crash in 2007. At this time, automation became the main driver of innovation and a key focus to support revenue. Now, in today's landscape, if automation is not a top concern for IT teams and doesn't sit at the heart of IT strategies, companies risk losing their competitive edge, ultimately resulting in failure.

There has also been an increase in demand over the last decade for IT to have more of a proactive approach to technological pivots, and the pressure to respond quickly has grown. Because of this, development teams have taken the lead to change the role of the IT department within the business by positioning IT as a strategic revenue driver. DevOps within IT departments has also come a long way. Specifically, there are three major changes within these teams worth spotlighting.

For starters, departments were adopting agile development practices to speed up the delivery of creation changes into the production environment. Then, DevOps introduced automation into change delivery.

The final step after accepting the two previous changes, is the alignment of development and operations teams. In the past, DevOps and operations have worked separately: DevOps managed the development, while operations handled the environment. But, as automation takes a more prominent role in companies, it becomes essential that these two teams align. It's no longer feasible to have them in two siloed playing fields.

Joining these teams hasn't been easy. There's been resistance in aligning efforts and daily communication between the two continues to be an issue. It has been a challenge for IT operations to interact with the development team so closely. DevOps has struggled to see the value of managing the production environment, as they often believe the task to be low-level and straightforward. In addition, the perspective that DevOps has on infrastructure is narrow-minded and they're typically only invested in direct projects that relate to them. What this logic fails to address is that fact that no application is completely isolated. Every application is living in an environment of shared resources that all influence each other. Unfortunately, because of these lack of understandings, DevOps and operations remain largely siloed. The collaboration that needs to happen hasn't yet happened.

So, what role does automation have in this struggling relationship? For starters, we know that in order for a new module or application to move from development environment to the real-world production environment, certain steps need to be taken. In the past, these steps have been completed manually. But, with today's automation, humans are taken completely out of the equation, presenting an opportunity for AIOps technologies to execute the process from development to production much faster, smarter, and more efficiently.

With the proper tools in place, algorithms can take data from the production environment, understand the disposition of resources within that environment, and ensure the new application or change being delivered has enough of the right resources to support itself, rather than pulling resources from other applications. This is feasible with automation.

There are two stages to the automation process.

The first stage is automating the path from development to production. This could include AIOps features like pattern discovery, anomaly detection, and causal analysis. In this case, however, AIOps features are applied when allocating resources and understanding when new will be delivered into the production environment.

The second stage of automation comes into play when there is a new element in the production environment. What started as three changes a week has now reached three thousand because of the number being delivered into the environment through automation. Additionally, automation causes an increase in the modularity, ephemeralness, and IT systems are more distributed, making it nearly impossible to predict what kind of impact a new change will have on a production environment. With the proper AIOps technology in place, it becomes easier to foresee these implications.

The amount of data in today's business landscape only continues to increase. Without an analytical or diagnostic tool, development and operations teams are finding it nearly impossible to comprehend the performance of the production environment and to action events. This is when the role of AIOps becomes incredibly important and can save teams from severe consequences. Without the proper automation tools and strategy, companies will collapse as they become increasingly blind to system performance.

Will Cappelli is Field CTO at Moogsoft

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DevOps and AIOps: Developing A New Culture

Will Cappelli
Moogsoft

In today's competitive landscape, businesses must have the ability and process in place to face new challenges and find ways to successfully tackle them in a proactive manner. For years, this has been placed on the shoulders of DevOps teams within IT departments. But, as automation takes over manual intervention to increase speed and efficiency, these teams are facing what we know as IT digitization. How has this changed the way companies function over the years, and what do we have to look forward to in the coming years?

While this all began with the introduction of the Internet in the late 90s, it took a turn after the economic crash in 2007. At this time, automation became the main driver of innovation and a key focus to support revenue. Now, in today's landscape, if automation is not a top concern for IT teams and doesn't sit at the heart of IT strategies, companies risk losing their competitive edge, ultimately resulting in failure.

There has also been an increase in demand over the last decade for IT to have more of a proactive approach to technological pivots, and the pressure to respond quickly has grown. Because of this, development teams have taken the lead to change the role of the IT department within the business by positioning IT as a strategic revenue driver. DevOps within IT departments has also come a long way. Specifically, there are three major changes within these teams worth spotlighting.

For starters, departments were adopting agile development practices to speed up the delivery of creation changes into the production environment. Then, DevOps introduced automation into change delivery.

The final step after accepting the two previous changes, is the alignment of development and operations teams. In the past, DevOps and operations have worked separately: DevOps managed the development, while operations handled the environment. But, as automation takes a more prominent role in companies, it becomes essential that these two teams align. It's no longer feasible to have them in two siloed playing fields.

Joining these teams hasn't been easy. There's been resistance in aligning efforts and daily communication between the two continues to be an issue. It has been a challenge for IT operations to interact with the development team so closely. DevOps has struggled to see the value of managing the production environment, as they often believe the task to be low-level and straightforward. In addition, the perspective that DevOps has on infrastructure is narrow-minded and they're typically only invested in direct projects that relate to them. What this logic fails to address is that fact that no application is completely isolated. Every application is living in an environment of shared resources that all influence each other. Unfortunately, because of these lack of understandings, DevOps and operations remain largely siloed. The collaboration that needs to happen hasn't yet happened.

So, what role does automation have in this struggling relationship? For starters, we know that in order for a new module or application to move from development environment to the real-world production environment, certain steps need to be taken. In the past, these steps have been completed manually. But, with today's automation, humans are taken completely out of the equation, presenting an opportunity for AIOps technologies to execute the process from development to production much faster, smarter, and more efficiently.

With the proper tools in place, algorithms can take data from the production environment, understand the disposition of resources within that environment, and ensure the new application or change being delivered has enough of the right resources to support itself, rather than pulling resources from other applications. This is feasible with automation.

There are two stages to the automation process.

The first stage is automating the path from development to production. This could include AIOps features like pattern discovery, anomaly detection, and causal analysis. In this case, however, AIOps features are applied when allocating resources and understanding when new will be delivered into the production environment.

The second stage of automation comes into play when there is a new element in the production environment. What started as three changes a week has now reached three thousand because of the number being delivered into the environment through automation. Additionally, automation causes an increase in the modularity, ephemeralness, and IT systems are more distributed, making it nearly impossible to predict what kind of impact a new change will have on a production environment. With the proper AIOps technology in place, it becomes easier to foresee these implications.

The amount of data in today's business landscape only continues to increase. Without an analytical or diagnostic tool, development and operations teams are finding it nearly impossible to comprehend the performance of the production environment and to action events. This is when the role of AIOps becomes incredibly important and can save teams from severe consequences. Without the proper automation tools and strategy, companies will collapse as they become increasingly blind to system performance.

Will Cappelli is Field CTO at Moogsoft

Related Links

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Your Observability Stack Has a Telemetry Pipeline Problem

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

Operator to Orchestrator: 80% of IT Pros See Shift in Role as AI Permeates Workflows

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Gartner: 40% of Organizations Deploying AI Will Use AI Observability to Monitor Model Performance by 2028

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