“Have you tried shutting it off and turning it back on?”
While AIOps won’t likely remove this query from our vocabulary any time soon, technology is certainly here to take on a bulk of the heavy lifting.
For all-sized companies, service calls are still going to continue to pour in. And, there’s no sign of any of the world’s CompTIA certs going to waste in the near future. Still, thanks to AIOps, many jobs within the world of IT will become more streamlined.
Here, let’s touch on AIOps use cases and talk about how to manage your system efficiently.
AIOps Use Cases for Everyday Tasks
AI and machine learning have proved their value in many niches including eCommerce, social media, and even banking. Through artificial intelligence for IT operations (AIOps), these technologies have demonstrated that they can earn their keep in the everyday world of internet technology.
Furthermore, AIOps aren’t reserved for enterprise-level environments. These systems are used by cloud-native SMEs, all-sized DevOps teams, and businesses going through digital changes.
In the most straightforward terms, here are some of the most common everyday uses of AIOps.
- Data collection – data can be collected in real-time IT operations or from existing databases.
- Event clusters – events can be classified in various ways to enhance performance and decrease resolution time.
- Smart alerts – technicians and decision-makers can receive notifications about data anomalies, task completion, and more.
- Report generation – data can be translated to detailed events and operations.
- Forecasts – predictions can be made based on calculations made from trends.
Within these categories are nearly limitless subcategories of use cases for AIOps that oil the greater IT machine… as long as the overall system is managed efficiently.
Now, let’s find out how you can do just that.
How to Manage AIOps More Efficiently
As with all aspects of your IT strategy, the key to productivity with AIOps is organization. All the power tools in the world won’t help you build a birdhouse if you never learn how to use them, but a basic understanding will save you a ton of time and energy.
Here are some tips to help you supercharge your AIOps system.
1. Use Out-of-the-Box Integrations
When your system supports a wide variety of popular data sources, you open the doors to a new universe of time-saving opportunities. You’ll want to integrate your AIOps systems with the other tools that collect and house data within your operations.
An API-first development strategy can enable you to embrace add-ons and optimize automation within your IT system. For example, you can use a messaging API to create a customized chat app within your AIOps. Keep in mind that tags should be uniform throughout the facets of your system to ensure that they trigger the proper events.
2. Automate, Automate, Automate
The less manual data entry required for a task, the less room there is for human error. So, your AIOps system should automate everything possible, beyond the obvious tasks and this is where Robotic Data Automation(RDA) comes into the picture to automate all your data transformation activities using bots.
For example, the following tasks should be triggered without manual entry:
- Data integration/migration
- Data preparation
- Machine learning operations
- Data anomaly recognition and alerts
The magic starts to happen as soon as they start using RDA and IT professionals start to experience more ease within their workload.
3. Implement Self-Learning Capabilities
When you apply self-learning, your AIOps system has the potential to get better on its own. Of course, learning in this application doesn’t take place within a general set of rules. Rather, algorithms with a narrow focus on specific tasks should be used.
So, algorithms should be designed to not only trigger tasks but create policies that impact your operations. In this case, clustering and correlation are the cornerstones of effective policy creation.
Policies, naturally, will vary from system to system based on need. As time goes on, through policy creation, your AIOps should become better at tasks like assigning the right team of technicians, proposing likely root causes, and recommending solutions based on what’s worked in the past.
4. Apply Contextual Intelligence
Hand-in-hand with machine learning, AIOps should provide additional context automatically. For example, an alert seen by a technician should provide at least some context about a service order, which can be done with proper implementation.
Context eliminates isolated information silos and helps to automatically correlate data within a machine with multiple moving parts. In this sense, skilled technicians can ideally be given higher-value work.
Note that observability is crucial to context as it gives you a 360-view of what’s going on inside the IT environment.
4. Administer Role-Based Views and Controls
Not everyone within an AIOps system will need to see the same platform views and controls. Even though IT teams are typically small, it’s important to regulate access to certain functions, computers, or network functions based on need.
For example, senior professionals might have access to critical functions like rule-creation capabilities while interns may only need to see alerts and reports. In this case, Role-Based Access Controls (RBAC) should be implemented.
Furthermore, user dashboard design might vary from role to role. Keep in mind that a technician shouldn’t need to create a new user account when responsibilities increase, so there should be a way for higher-level admins to easily change a user’s views and controls as a team’s workload evolve.
5. Find Opportunities for Self-Scaling and Self-Healing
Until recently, it was a mystery how software might grow and heal itself. However, the puzzle is now being solved. The ultimate goal for AIOps is a system in which problems are resolved before anyone even knows they existed and at any time when a human might not be available.
Over time, you will discover relevant and not-so-mystical opportunities for self-scaling and self-healing within your system.
Don’t let the idea of auto-remediation overwhelm you. Instead, think of it as a thought tree of if/and scenarios wherein decisions are made. Over time, these AI-powered decisions can use a combination of tools to come up with solutions that can grow and fix the system from the inside.
Monitoring and managing modern IT requires some level of AIOps. The goal of automated and intelligent systems isn’t to take over the job of the technician, but to improve the efficiency of operations. Use these tips to enhance your AIOps from the ground up.