Operations | Monitoring | ITSM | DevOps | Cloud

In the recent past, every enterprise has had to deal with an outage, leading to war rooms where ITOps teams are put on the spot. While they take on the burden of ensuring 100% uptime, it is often the tools they employ which don’t live up to their promises. Especially in the wake of the pandemic, with working norms being redefined, ITOps teams have been under even greater pressure to deliver. While they strive to be efficient and rely on cutting-edge technology, uptime is often elusive.

Modern workflows are primarily aimed at one thing—reducing operational complexities so that stakeholders can focus on initiatives that boost business and innovation. For IT teams, Artificial Intelligence and Machine Learning play key roles in bringing this goal to life. And even though AIOps is considered to be not yet in mature stages, there is no denying that IT teams that do not adopt AI processes will be left behind. By 2023, the market for AIOps tools is predicted to reach $11.02 B.

AIOps myths and how to avoid them Gartner coined the term AIOps in 2016 to refer to the combining of “big data and machine learning to automate IT operations processes, including event correlation, anomaly detection and causality determination.” In the five years since, AIOps has grown leaps and bounds — last year, AIOps was at the peak of the Gartner hype cycle.

Multi-cloud hybrid cloud environments, microservices architectures, the rapid growth in the number of mission-critical applications, and the sudden surge in remote work have made enterprise networks exponentially complex. These networks are often not designed to handle the variety of physical and wireless media that’s become common today, for instance, the number of video calls, data transfer through screen sharing, etc.

If 2020 was a year of turbulence, 2021 was the year of complete digital transformation. Enterprises across the globe focused their efforts on enabling stellar digital experiences — both to customers and internal stakeholders alike. This had a significant impact on the IT landscape. The number of applications that were ‘mission-critical’ increased overnight. In a recent survey, respondents said that they have an average of 71.4 mission-critical applications.

In 2017, McAfee found that an average enterprise uses 464 custom applications. A large enterprise — a company with over 50,000 employees — uses 788 custom apps! The more applications you have, the more complex your application environment is. This means that you are more susceptible to outages. So, the tolerance for downtime is impossibly low. Mission-critical applications must be available at all times.

At 8:54 pm on November 1, 2020, a customer of HDFC bank complained on Twitter that the bank’s services like internet banking and ATMs were down. More customers started raising similar issues over the next couple of hours, saying that UPI, credit card, and debit card transactions weren’t working either. Finally, at 11:55 pm, the bank confirmed that one of their data centers faced an outage. “Restoration shouldn’t take long,” they promised.

The cloud is driving enterprise digital transformation. Gartner predicts that by 2026, public cloud spending will exceed 45% of all enterprise IT spending, a 2.5x growth from 2021. Enterprises globally are accelerating application modernization, embracing the cloud. This is giving rise to a few key trends. Software-as-a-Service (SaaS) adoption is on the rise. So, organizations are using applications whose implementation/infrastructure they have little or no control over.