The concept of Artificial Intelligence (AIOps) for Operations was defined by Gartner in 2016 as the utilization of big data and machine learning to automate IT operation processes such as event correlation, anomaly detection and causality determination. Observability is a measure of how well internal states of a system can be inferred from knowledge of its external outputs. Working in tandem, observability and AIOps are transforming enterprise management and bringing substantial value to businesses of every industry. It’s an evolution that is occurring before our eyes.
The Necessity for Observability and AIOps
If the evolutionary cycle of Observability and AIOps was dispersed over the course of a baseball game, hitters would currently be batting at the top of the fourth inning. Some may be surprised that this technology has progressed so quickly as this solution set just came to fruition only four years ago. There is a fundamental reason for this fast acceleration, however. It had to. The paradigm of digital transformation has outpaced the ability of the operations team to manage today’s enterprises using a traditional dashboard approach. Enterprise IT has come to realize that the game will be lost well before the ninth inning without the integration of automated intelligence and machine learning supporting their efforts. As a result, 91% of enterprises are moving towards adapting an AIOps solution to solve their major pain points.
The necessity for Observability and AIOps came about when the Kubernetes platform gained critical mass. Suddenly it wasn’t about servers anymore. It was about containers and microservices. The dynamic nature of containers and the granularity of microservices delineated the limitation of relying on a dashboard portfolio that IT personnel had to swivel amongst to gain insight into their enterprise environment. That challenge has been exponentially magnified by the growing complexity of enterprises and the recent expansion of the IT estate. It’s no longer about a single datacenter. IT teams are floundering for insights into multiple environments including edge computing locations, district offices, and multiple clouds whose geographical location is unknown.
Dashboards are no longer plausible in complex enterprise environments where things happen too fast for manual human reactionary tactics. A NOC engineer doesn’t have time to rotate through ten dashboards in reaction to an issue that is disrupting critical business operations and costing money. The futility of dashboard observability has been compounded with the injection of telemetric information in the form of alerts and logging instances. As many as 40% of large-scale organizations and providers receive more than a million event alerts each day, causing IT teams to be desensitized to them, a condition known as alert fatigue. Often times, certain alert categories are simply turned off to reduce the noise level. To combat this, Gartner predicts that 30% of large enterprises will exclusively utilize AIOps and digital experience monitoring tools to monitor applications and infrastructure. That’s compared to a mere 5% in 2018.
A Single Coherent Strategy
It’s clear that IT teams need a toolset that can filter through the noise and automatically remediate issues before they become impactful. Observability today is about combining all your existing tools, personnel, processes, and data into a single coherent strategy that will produce meaningful and actionable data. Determining the source of a latency issue or the issue behind a perpetual process failure is no longer initiated by guesswork. AIOps supported observability is about injecting and correlating multiple data types and information to find determining patterns in quick succession. This involves root-level discovery so that problems aren’t just temporarily remediated but solved for good.
The inalienable truth is that every time you introduce something new into your enterprise environment, it creates more noise. Utilizing the universal truth of the 80-20 rule in this instance, the trick is to separate the noise that makes up the 80% of alerts and attend to the 20% that is proving the most disrupting to your business. The eventual beauty of this is that once the 20% is addressed, you can make the time to focus on the 80% as well and reduce its load burden.
Observability Brings Value
In line with the evolution of Observability and AIOps, IT has transitioned from being a cost center to a profit center by driving innovation across all business units of the company. The role of IT today is to ensure that the business gets full value out of their investments. Just as SMBs have transitioned from acquiring break-fix services to managed services to maximize their IT investments, today’s large enterprises demand more than ensuring the uptime of an application. It’s about optimizing your IT resources to recoup maximum value and attain competitive advantages within your industry. Only the automated and intelligent observability platforms deliver at this level.
Making Your AIOps Integration Successful
Your enterprise wasn’t built overnight and to be successful, your automated observability solution won’t be either. However, organizations can attain realized value within a few weeks of implementation if done right. First, it’s important to pick the right solution that matches the infrastructure of your enterprise and the skillset of your IT team. Once implemented, it’s important to focus on immediate problems and attain a few early victories that will accelerate the buy-in of your personnel. Note that you can’t simply set it and forget it because your overall IT estate is constantly evolving. It will also take time to learn and finetune your solution so that the full load of consequential noise levels is properly filtered.
Why Dynatrace is Ideal for Many Organizations
Dynatrace is a great option for organizations that lack the skillsets to build and integrate their own toolsets. Their robust platform offers a wide assortment of tools that will meet the needs of most enterprises. Because Dynatrace has been implemented in so many different types of environments, its rich collection of telemetry data gives it a powerful advantage over many of its competitors. At the outset, the two primary advantages realized by customers are the ease of implementation and the time to value, which is especially important in a time of budgetary pressures.
The truth is that everyone is going to have an AIOps solution to attain greater observability into the environments. In some ways, it’s arms race. In the same way that Amazon has secured a seemingly insurmountable head start over traditional retailers by gaining a precise understanding the purchasing patterns of its customers, those organizations that can optimize their environments the fastest can secure a lead over their competitors as well. It’s time to be part of the observability/AIOps evolution.