Artificial intelligence for IT operations (AIOps) combines big data and machine learning (ML) algorithms to augment and automate day-to-day IT ops tasks ranging from performance monitoring and reporting to data correlation and analysis.
Applying AI to IT ops is needed to keep up with the ever-increasing volume of data, which is doubling year over year. AIOps enables you to quickly analyze this data, present it in a meaningful way, and use it to proactively anticipate and resolve common IT issues.
It can seem daunting at first, but here are key elements of a pragmatic approach to implementing AIOps:
Transform Siloed Data into Contextual Insights for Faster Decision Making
There is no one tool that does it all. Silos can only be broken down by embracing an open architecture that enables you to integrate curated data from across the entire hybrid technology stack – from mobile to mainframe to multi-cloud. This does not mean taking all your raw data and throwing it into one gigantic data lake. If you do, you will end up with a data swamp: a stagnant pool clogged with untold amounts of useless data … useless because of the difficulty involved in putting random and disparate data in context to create insight and drive action. Instead, using an open architecture approach, you can easily augment and further curate the data with meaning behind the data relationships found across the tooling landscape.
Analyze Across Domains to Increase Operational Efficiencies
When your IT ops tools embrace the use of open APIs to gather analytics, you gain the ability to view your curated data from different perspectives and share hidden insights across teams, thereby achieving greater efficiencies. You can build on your current investments in products by leveraging open APIs to integrate the data you are already collecting.
The greatest benefits of contextual insights come when IT teams make collaborative analysis the new normal, maintaining constant awareness of activities that span mainframe, distributed, and cloud infrastructures. Data sharing and faster analysis across IT domains increases productivity and efficiencies for everyone, enhancing business outcomes.
Leverage Proactive Insights to Move from Recovery to Avoidance
In addition to supporting cross-functional analysis, contextual insights allow data to be mined for patterns via machine learning. These patterns enable IT to anticipate potential issues sooner and shift from a reactive recovery model to a proactive avoidance model.
Using AIOps to generate proactive insights can also help address IT skills gaps. For example, mainframe operations have the benefit of people with decades of experience. However, these people are retiring and taking their tribal knowledge with them. AI and ML can be used to collect and codify this knowledge so that it is not lost. That knowledge will then contribute to proactive insights that the next generation of operators can use to keep business critical operations running smoothly.
Use Automation to Advance Towards Self-Healing Systems
Once AIOps is providing accurate insights in context, it is just one more step to have AIOps act upon those insights, remediating issues automatically before they impact the business. This is the ideal state: AIOps sends an alert as soon as an abnormal trend or issue is identified, quickly isolates the problem, and diagnoses the source, and automates an appropriate response. No human intervention required.
By using an incremental AIOps approach that incorporates built-in feedback loops and guardrails, you can establish “trusted automation” over time. Your IT operations can be shifted so that your personnel no longer have to handle routine matters, manage policies, or resolve the majority of issues that arise: all that will run on autopilot under the auspices of AIOps.
Building an AIOps Powerhouse
AIOps is not as complex as it may first appear. You begin with a foundation of contextual insights, set in place by opening your architecture and enabling contextual insights to be derived from previously siloed data. On that foundation, you raise four pillars. First, you begin to analyze across domains, sharing data to enhance organizational efficiency. Second, you leverage ML-driven proactive insights to anticipate potential problems sooner, making the shift from reactive to proactive operations, preemptively addressing issues instead of responding to business impacts. Third, you continuously augment your operations and proactive abilities with automation you can trust as you drive towards implementing self-healing systems. Fourth, you use data-driven prioritization to generate a sustainable flow of value to the enterprise.