Artificial intelligence and automation are being used to drastically reduce the time IT spends manually managing systems and environments. But what does self-healing really mean, and how can your team get started?
What Is Self-Healing In IT?
Self-healing refers to the ability of systems to detect and remediate issues without human intervention. With regards to IT, this often includes the use of machine learning (ML) algorithms and automation to predict, detect and respond to IT operations issues.
Within IT, standalone software or applications can include self-healing capabilities. Larger platforms and monitoring tools meanwhile can extend self-healing capabilities to the larger IT environment. Automation platforms can provide auto-remediation with or without the use of AI, relying on event-triggers and automated monitoring to kick-off remediation processes when issues arise.
A self-healing IT environment can also include the automation of day-to-day tasks, for example, provisioning virtual machines or distributing workloads in real-time. This can greatly reduce the occurrence of manual errors while optimizing systems and processes in order to prevent issues such as delays, workload failures and outages.
Why Is Self-Healing Important?
Self-healing in IT operations is important for two reasons:
- Problems with IT systems or processes are having larger, more immediate impacts on business operations
- IT’s list of responsibilities continues to grow, straining resources and over-extending IT staff
According to Gartner research, 25% of CIOs will be responsible for digital business operations by 2024. This underscores a larger trend in which IT teams are playing larger roles in business, whether developing apps or digital services, or maintaining tools and processes that are critical for line of business teams.
As a result, a small error or misstep in IT can have a big impact on business operations.
At the same time, IT environments are increasingly complex, consisting of more endpoints than ever before. In a report by DynaTrace, 63% of CIOs said that their cloud environments were too complex for people to manage.
Today’s IT environments are too large and complex for teams to manage manually. As a result, most IT teams rely on monitoring tools that alert IT personnel when an issue has occurred or is expected to occur. Self-healing builds on this practice by adding auto-remediation and optimization capabilities that reduce the need for manual interventions, improving reliability and reduce issues.
Self-Healing For IT Operations
Self-healing in IT operations can be either data-based or event-based.
IT collects troves of data on system and process performance. In a self-healing environment, that data is analyzed to identify trends and patterns, similar to AIOps solutions. This makes it possible to anticipate and address issues before they occur. For example, if an online retailer experiences regular spikes in demand, additional compute resources can be automatically provisioned (and then deprovisioned) to meet demand.
Auto-remediation is also a common feature in self-healing environments. Using a job scheduler or workload automation software, thresholds can be set which, when met, triggers a preventive workflow. For instance, if a process can’t locate a necessary file, the process can automatically restart at a later time, after sending a notification to the team or person responsible for uploading the file.
Additionally, automation platforms are beginning to expand self-healing to the larger IT environment. By applying machine learning to historical data, automation vendors can detect potential issues before they occur, as well as assess system health and performance. This makes it possible to automate changes and upgrades, and for vendors to notify users of potential concerns and optimizations.
Benefits Of Self-Healing In IT Operations
IT teams spend almost half of their time completing manual tasks just to keep the lights on, delaying the delivery of key projects and services. Meanwhile, the volume of projects IT teams are responsible for continues to increase, while IT teams continue to face skills gap challenges.
By leveraging automation and analytics, IT operations teams are able to drastically reduce the time spent troubleshooting and fighting fires. Self-healing capabilities make IT ecosystems more reliable, improving SLAs and uptime and enabling IT personnel to focus more time on key projects. Gartner expects that, by 2024, automation and analytics will enable IT teams to shift 30% of their time away from support and service desk and into DevOps.
Challenges Of Implementing Self-Healing Operations
The first step towards actualizing a self-healing environment is to gain end-to-end visibility. When a process fails, IT professionals need to quickly identify the root cause. What makes this difficult is that many IT environments and data centers are fragmented, with automation, job scheduling and monitoring tools deployed in silos.
The other challenge to self-healing IT operations is cultural. IT teams continue to rely on manual interactions even when analytics and automation solutions are available. This tends to be the result of traditional methods of operations, especially the reliance on mean time to resolution as a performance metric. IT teams tend to wait for something to break before fixing it. The goal of self-healing is to prevent things from breaking in the first place.
How To Get Started
Building a self-healing IT environment requires orchestration and centralization. Instead of managing processes and platforms in silos, IT needs to take a unified approach to IT infrastructure and process automation.
By centralizing control over automation environments, IT teams gain a single repository for logs, reports, and analytics. This makes it possible for IT to quickly identify root causes, and to apply new rules to prevent similar issues in the future.
More importantly, centralization makes it possible for automation platforms to extend self-healing capabilities across the enterprise. By integrating and orchestrating disparate processes, analytics, machine learning, and event-automation can be implemented across the IT environment, drastically reducing the time that IT spends responding to issues. Better reliability means fewer fires and better SLAs. That, in turn, means more time for IT to develop solutions that drive business value and digital transformation.
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