The massive amounts of data that enterprises produce daily can help solve some of the most difficult business challenges. For AIOps to live up to the hype, organizations must eliminate data silos, adopting a holistic data and machine-learning strategy to create an environment that is continuously observing, learning and improving itself. While observability creates mountains of granular data, dependency graphs and application topologies, AIOps trains on this data — resulting in identified problems turning into long-term solutions, past behavior informing improved workflows and faults and failures fueling training algorithms. Organizations armed with both observability and AIOps are enabled to take swift action on their data and can easily automate responses, which is critical for teams aiming to exceed expectations with fewer resources and shorter timelines. I believe we will continue to see these once separate entities blend as businesses progress through this year, and it will become increasingly clear that AIOps is more than a square in buzzword bingo. There must be an inextricable link between AIOps and observability to unlock its true power.
As applications have become critical to businesses, the complexity to power them has grown. Moving to a strategy with AIOps has been a growing phenomenon over the last years, and has benefits, as automation and artificial intelligence grow more important. AIOps needs to exist as a component of a wider strategy focused on full-stack observability with the appropriate business context to accurately measure the true effects of activities across the full stack and to ensure IT teams can pinpoint root causes of potential issues and address them in a targeted and swift manner. This process leads to the ultimate goal of a self-healing environment to automate resolution before it impacts the end user. An organization must implement cultural change that removes finger pointing and encourages collaboration among teams that are closest to IT efforts. This includes IT, security teams and business leaders who should be involved so security and performance stay central to the technology and strategy decisions. By providing business context, teams can align and see how their efforts affect the business, which helps streamline resolution based on what's most valuable to the business. For AIOps to truly fulfill its potential in helping organizations scale faster and provide context to the loads of data required to keep modern applications performing optimally, it needs to be wrapped in with DevOps, DevSecOps and BizDevSecOps. Rather than adding to the growing related vocabulary, wrapping AIOps into a full-stack observability solution with business context provides adequate information for continued digital transformation.
When used properly, AIOps enables IT teams to act more efficiently and respond to issues proactively and in real time, leading to better experiences for the IT teams, customers and employees. For companies that are just starting out on their AIOps journey, it's often smart to start with a focused approach on a single use case before applying AIOps across the entire organization. This allows IT leaders to demonstrate the value of the implementation, showcase the power of AIOps and establish the data-driven mindset in the team that is needed to successfully implement AIOps deployments. Then, once they've demonstrated the ROI and prepared their teams for this new approach, they can scale up across the enterprise with the proper goals and objectives in place.
Globally, it's estimated we'll generate a staggering 79 zettabytes of data in 2021 and more than double that at 181 zettabytes in 2025. Complex enterprise IT environments aren't immune to this data growth, which is making it difficult to scale and address the challenge. When something slows or breaks, it can take time to figure out the problem. AIOps technologies can help today — it's not hype.
