What is AIOps?
A vital part of the Digital Transformation journey, AIOps is artificial intelligence’s application to IT operations and DevOps. These solutions incorporate advanced analytics and self-learning AI technologies to remove the need for constant human supervision and intervention with an intelligent, autonomous system that can easily monitor an entire IT operations application stack across the organization as a whole. It is no secret that businesses of all sizes deal with more data than ever before as customers and companies adopt more platforms and technologies to keep up with the increasingly digital world. AIOps tools allow businesses to absorb and process new incoming data in record time. Before we dig into the nuts and bolts of effective AIOps, we need to understand its history so as not to repeat it.
Where We Came From
We can trace IT operations’ burgeoning field back to the late 1970s with the humble spreadsheet’s invention. The now-ubiquitous spreadsheet rapidly became the preferred method for everything from accounting to change management and stayed that way up until the late 2000s. The spreadsheet’s fundamental innovation was elegant, simple, and monumental: it was effectively a database. Spreadsheets gave rise to the possibility of logging, organizing, and retrieving data of all types (text-based data until roughly two decades ago) and offered users the ability to access and share the same data without the hassle of putting everything down on paper. The ability to read, write, and replicate exact data sets with a few keystrokes vastly changed operations processes and made the use of computers increasingly more commonplace over the decades to come.
But this doesn’t mean there weren’t any setbacks. In the early days, computers were large, heavy, and extremely expensive. With such a high barrier to entry into the digital age, adoption rates were low, speeding up only as refinements in chip design led to smaller form-factors and economies of scale meant computers made available to the average consumer at a reasonable price. Another common malady of early computer systems was the high degree of specialization required to operate one efficiently. Perhaps the fatal flaw inherent in these old processes was something that had been present since the dawn of humankind: human error.
We are still dealing with human error today, though with a significantly reduced risk of catastrophic system failure from a misplaced period or an errant syntax discrepancy. Yet, it still causes problems for users who might have to spend hours to find the one bug that is holding up the successful deployment of a new IT solution.
Since then, we have come quite a long way, and computers are more affordable, reliable, and commonplace than they ever were in the past. Suppose we prescribe to the Law of Accelerating Returns by Ray Kurzweil. In that case, we will experience explosive innovations in all technology sectors over the next few years, with the volumes of data generated, collected, and processed growing by leaps and bounds as well.
Where We Are Going
Capturing the immense quantity of raw data generated every second is no easy task, and relying on the tried-and-true spreadsheet-based methods can’t keep up with the sheer size of data handled by modern systems. With more and more business processes migrating to the cloud, work from anywhere being normalized by more than just tech companies as every industry adapts to the ‘new normal’ of a post-pandemic world. AIOps is only one piece of the puzzle, but it’s a pretty big chunk. Using AI to automate processes is merely the tip of the iceberg – and it’s a titanic iceberg.
With a slew of hot-button technologies, including IoT, big data, and cloud-native applications, AIOps fits in quite nicely wherever there is a need to connect siloed data and tie up loose ends. In essence, AIOps applies machine learning to augment IT teams’ abilities, making them more effective, not replacing them entirely. Using AIOps, IT teams don’t need to deal with everyday items like change management (the pesky spreadsheets of the 2000s). They are enabled to amplify the three Vs of big data: Volume, Variety, and Velocity. An effective AIOps platform can ingest more data from a more comprehensive array of data types, which is then evaluated and acted on in real-time.
Gartner reports that AI augmentation could account for $2.9 trillion of business value in 2021 alone by focusing on implementing human-centered AI technologies like AIOps. Seeing as we are at the start of 2021 when writing this blog, now is a marvelous time to get the ball rolling.
How to Get Started
The deployment of any AIOps solution worth its salt should come with three things: domain-agnostic platform, autonomous self-learning capabilities, and Day One value. Many other benefits come with AIOps solutions, but these three are by far the most impactful. With a domain-agnostic platform, the solution readily integrates operations data across any application stack and will adapt to new applications as the companies’ needs change and grow. Critical to the ability for the solution to be domain-agnostic is its ability to continuously and autonomously self-learn.
A self-learning solution will become fine-tuned to your company’s specific business needs solely on the newly ingested data and historical data housed in the companies’ knowledge center of choice. Last but very much not least comes the Day 1 value of an AIOps solution. The deployment should give your IT team exact results in hours, not weeks or months. If the platform your enterprise is considering doesn’t guarantee these three components, then it is not the right AIOps solution.
Fortunately, Aisera’s AIOps platform does all three and so much more. Gain full-stack observability, active alert noise suppression, and even predictive capabilities to spot future major incidents. The solution runs on Aisera’s world-class AI and comes loaded with 1200+ remediation actions and more than 400 integrations for IT and DevOps. Aisera’s AIOps is the most dynamic, flexible, and easy-to-use solution on the market.