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The Road to Automation in IT Operations-Part 1

Anirban Chatterjee

The buzz around automation continues to grow, in every industry, sector and vertical — and for good reason. In IT Ops, the impact can be instant and huge, with improvement measured in the tens or even hundreds of percent as automation enables even a very lean team to operate at an outsized level. Automation can facilitate faster production, the creation of new products and the delivery of more services — and all in a stable, predictable, scalable way. And today, with AI and Machine Learning in the mix (aka AIOps), the possibilities and potential for automation are almost limitless.

So, how do you ensure your journey to automated IT Ops is streamlined and effective, and not just a buzzword?


Here are 4 golden rules to help you do just that:

1. Set good standards

2. Reduce complexity

3. Define and simplify processes

4. Automate wisely

1. Set good standards — powerful and specific

In general, standards can be thought of as specifications and procedures that have been designed to make sure the materials, products, methods, or services people use every day are reliable. Standardization is particularly relevant to IT automation, which is itself a computerized implementation of a standardized process. Put bluntly, computers are dumb and lack creativity. They do only what they're told, so we have to know exactly what we want them to do before they can do it. This is why only standardized processes can be automated successfully.

The key to optimizing automation is to create powerful standards that enable you to get the most out of your systems. These standards define how your systems communicate with each other, transfer information, look at and analyze data, etc.

A good example is the standardization of naming conventions, which, in the enterprise IT world, is always something of a challenge. If there is no standard in place regarding what one system is sending across, then the receiving system will just be guessing what to do with information it is getting. And, if it can't get the information it needs out of the data stream it receives, then a separate, manually-updated lookup table may be required, compromising the efficacy of the automation you wish to set up.


Consider, for example, the host naming standards illustrated in the image above. The more the naming standard is designed with your needs in mind, the easier it is for your systems to analyze the data, and pull out the critical information needed, such as the affected frame, geographical location, application served, etc. If used consistently across the organization, this standard becomes a solid bedrock for the assumptions that tools downstream can make about the data, and for the automation processes you put in place on top — for example, to issue automated alerts, response and remediation actions when a server has an issue.

2. Reduce complexity - keep only what you need

A useful rule of thumb is that you should be able to sketch out or explain what your IT environment does and how it functions in around a minute

Complexity is inherent in the dynamic modern-day IT environments in which businesses operate, but that doesn't mean that we should not try to reduce it wherever possible. A useful rule of thumb is that you should be able to sketch out or explain what your IT environment does and how it functions in around a minute. If you can't, automation may just exacerbate complexity. So, when you contemplate automation, you should see it as an opportunity to take a step back, recognize areas of unnecessary complexity and identify what can be done to reduce it. To do this properly, you need to make sure you talk to the people that are doing the work on the ground to find out about their actual experience.

In the diagram below, we see an example of how complexity can be dealt with by moving to a SaaS environment. Operating many systems on-premise that need to be managed and maintained comes at a huge cost — both financially and in terms of efficiency. By moving to a SaaS environment, rather than having to maintain hardware, the operating system, bug fixes and patches, network bandwidth, firewalls, load balancers and the on-prem application software itself, you just need to take care of one thing only: the configuration of your SaaS apps!


Tool rationalization is another good example. By reducing the number of tools you work with, you reduce the complexity of your operations.

Now, you can focus your automation efforts on taking care of simplified tasks, saving time and reducing overhead in the long term.

Go to: The Road to Automation in IT Operations - Part 2

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For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

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The Road to Automation in IT Operations-Part 1

Anirban Chatterjee

The buzz around automation continues to grow, in every industry, sector and vertical — and for good reason. In IT Ops, the impact can be instant and huge, with improvement measured in the tens or even hundreds of percent as automation enables even a very lean team to operate at an outsized level. Automation can facilitate faster production, the creation of new products and the delivery of more services — and all in a stable, predictable, scalable way. And today, with AI and Machine Learning in the mix (aka AIOps), the possibilities and potential for automation are almost limitless.

So, how do you ensure your journey to automated IT Ops is streamlined and effective, and not just a buzzword?


Here are 4 golden rules to help you do just that:

1. Set good standards

2. Reduce complexity

3. Define and simplify processes

4. Automate wisely

1. Set good standards — powerful and specific

In general, standards can be thought of as specifications and procedures that have been designed to make sure the materials, products, methods, or services people use every day are reliable. Standardization is particularly relevant to IT automation, which is itself a computerized implementation of a standardized process. Put bluntly, computers are dumb and lack creativity. They do only what they're told, so we have to know exactly what we want them to do before they can do it. This is why only standardized processes can be automated successfully.

The key to optimizing automation is to create powerful standards that enable you to get the most out of your systems. These standards define how your systems communicate with each other, transfer information, look at and analyze data, etc.

A good example is the standardization of naming conventions, which, in the enterprise IT world, is always something of a challenge. If there is no standard in place regarding what one system is sending across, then the receiving system will just be guessing what to do with information it is getting. And, if it can't get the information it needs out of the data stream it receives, then a separate, manually-updated lookup table may be required, compromising the efficacy of the automation you wish to set up.


Consider, for example, the host naming standards illustrated in the image above. The more the naming standard is designed with your needs in mind, the easier it is for your systems to analyze the data, and pull out the critical information needed, such as the affected frame, geographical location, application served, etc. If used consistently across the organization, this standard becomes a solid bedrock for the assumptions that tools downstream can make about the data, and for the automation processes you put in place on top — for example, to issue automated alerts, response and remediation actions when a server has an issue.

2. Reduce complexity - keep only what you need

A useful rule of thumb is that you should be able to sketch out or explain what your IT environment does and how it functions in around a minute

Complexity is inherent in the dynamic modern-day IT environments in which businesses operate, but that doesn't mean that we should not try to reduce it wherever possible. A useful rule of thumb is that you should be able to sketch out or explain what your IT environment does and how it functions in around a minute. If you can't, automation may just exacerbate complexity. So, when you contemplate automation, you should see it as an opportunity to take a step back, recognize areas of unnecessary complexity and identify what can be done to reduce it. To do this properly, you need to make sure you talk to the people that are doing the work on the ground to find out about their actual experience.

In the diagram below, we see an example of how complexity can be dealt with by moving to a SaaS environment. Operating many systems on-premise that need to be managed and maintained comes at a huge cost — both financially and in terms of efficiency. By moving to a SaaS environment, rather than having to maintain hardware, the operating system, bug fixes and patches, network bandwidth, firewalls, load balancers and the on-prem application software itself, you just need to take care of one thing only: the configuration of your SaaS apps!


Tool rationalization is another good example. By reducing the number of tools you work with, you reduce the complexity of your operations.

Now, you can focus your automation efforts on taking care of simplified tasks, saving time and reducing overhead in the long term.

Go to: The Road to Automation in IT Operations - Part 2

Hot Topics

The Latest

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...