When IT operations run smoothly, it’s more likely everything else in the organization will as well. Unfortunately, tech sprawl can make IT environments more prone to issues that hinder end users or, worse, customers. Recent research shows that up to 50% of organizations juggle multiple tools for observability. Too many disparate tools to monitor too many systems and applications create siloes, slowing incident response and resolution times.
But this doesn’t have to be the case. Artificial intelligence (AI), when used right, can automate and enhance IT operations, offsetting the challenges created by complex, hybrid environments. More specifically, AIOps, as such an approach is called, can improve the productivity and efficiency of IT operations.
By automating tasks, improving response time, and laying the groundwork for the self-healing, self-optimizing environment known as Autonomic IT, organizations can ensure neither they nor their IT teams fall behind. Of course, achieving AIOps, much less Autonomic IT, doesn’t happen overnight. Instead, it requires adhering to several AIOps best practices, all of which build on and relate to one another. Let’s take a look at three of them.
- Implement strong monitoring and analytics
First, to be successful with AIOps, it’s imperative to implement strong monitoring and analytics, as this is the foundation for everything else. However, research also points to significant limitations to mapping all on-premises, cloud, and edge devices into a single business view, with 47% of respondents citing it as a barrier.
To that end, complete observability across the entire IT infrastructure is non-negotiable as a best practice. Once again, the evolution of IT infrastructure—including major advances in distributed architectures, multi-clouds, and containers—has created sprawl and noise, both of which impede an IT team’s ability to understand the relationship between different technologies and IT outcomes.
The ScienceLogic AI Platform addresses this by consolidating disparate tools into a single pane of glass across cloud and on-prem environments. This level of observability has many benefits, from ensuring system reliability to enhancing security. At the same time, strong monitoring enables relationship mapping and the ability to make proactive, informed decisions. A strong foundation as such also enables IT teams to begin their journeys to Autonomic IT, positioning them between Siloed IT and Coordinated IT.
To that end, Skylar Analytics also supports this journey, reaching into Machine-Assisted IT by leveraging digital twin capabilities to model key resource metrics like Disk, Interfaces, CPU, and Memory. This allows for advance notifications of potential issues, in addition to automated notifications for business KPIs. Thus, issues can be identified and addressed before they interrupt service.
The next AIOps best practice is simple but extremely impactful: automation. By automating workflows with AI and ML, IT teams can eliminate repetitive manual operations, in turn reducing errors and increasing efficiency. The best way to embrace automation is to start small, but to ensure the software used is scalable. The ScienceLogic platform automatically creates service tickets and enriches them with diagnostic data, for example. It also has hundreds of prebuilt one-click actions and workflow automation templates.
Skylar Automated Root Cause Analysis, meanwhile, automatically ingests and runs machine learning (ML) log analysis across millions or billions of messages from log files across applications. By offering plain language summaries and recommended actions, automated RCA enables IT teams to diagnose issues ten times faster, in turn preventing expensive and inconvenient periods of downtime. Of course, it’s important to monitor and refine automated processes regularly and to always keep a human in the loop.
- Futureproof to ensure continued innovation
The third and final best practice for AIOps is to futureproof as investments are made, so new systems can be integrated and automated easily. Innovation is happening at an increasingly rapid pace, making AIOps required for seamless integration. With the ScienceLogic AI Platform, IT teams no longer must manually populate their configuration management database (CMDB) or manually configure monitoring for newly provisioned services. Instead, with continuous discovery, the platform automatically discovers new devices and syncs them in real-time.
This is the best way to futureproof IT stacks, as it allows for ongoing innovation without hurting existing workflows. Of course, futureproofing is much easier when automation has been prioritized, as offloading routine tasks ensures there is time and effort available to know what innovation is needed and to build the infrastructure it requires.
The bottom line is that AIOps is a journey, not a destination. IT teams must be equipped to meet the fast-paced demands of today while futureproofing their infrastructure for tomorrow. The first two best practices—real-time monitoring and automation—ensure the former, but must be balanced with a longer time horizon for AIOps to reach its full potential.
If you need help evaluating your current IT operations or implementing AIOps, get in touch today.
