As software and systems become more complex and organizations ship software faster and more frequently, DevOps, site reliability engineering (SRE), and network operation center (NOC) teams can find themselves overwhelmed by a constant flood of data. Today’s modern technology stack means there are now many more things to monitor and respond to—a wider surface area, a complex web of dependencies, more software changes, more operational data emitted across fragmented tools, more dashboards, and more alerts.
At the same time, these teams are under increasing pressure to find and fix issues faster, or better yet, prevent them from happening in the first place. However, between noisy alerts, signals distributed among multiple tools, and thousands of “unknown unknowns,” it’s difficult to quickly determine and address the root cause of incidents, let alone detect and respond to issues proactively.
AIOps helps teams find solutions to problems faster and unearth unknown-unknowns or issues they might have missed, so that they can get out of reactive firefighting mode and back into the creative work of building more perfect software. Coined by industry analyst firm Gartner, artificial intelligence for IT operations (AIOps) combines big data and machine learning to augment IT operations processes, including anomaly detection, event correlation, alert noise reduction, and root cause analysis.
As with adopting any powerful new tool, your success with an AIOps solution will depend on your preparation. The better you prepare, the better outcomes and higher value you can expect. Use the four steps explained in this ebook to make sure you’ve prepared a solid foundation for your AIOps journey.